Doctoral Dissertation Consultants

If you are a student working to receive your doctoral degree, then you know just how difficult it is to finish your dissertation. Chances are that you’ve struggled with the statistics portion of the dissertation, you’ve had difficulty getting help when you need it, and you’ve pushed back deadlines over and over. If this is true, you are certainly not alone, and for this reason, doctoral dissertation consultants are available to help.

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Doctoral dissertation consultants make the task of writing, working on, and finishing the dissertation much more manageable. This is because doctoral dissertation consultants do exactly what their name implies— they help doctoral candidates attain their doctoral degrees by consulting them on their dissertation. This consultation comes in many forms and can help in every aspect of the dissertation.

Doctoral dissertation consultants are there to assist you throughout the dissertation process. In fact, doctoral dissertation consultants can even help you choose your topic. You no-doubt have an idea of what you want to study, but doctoral dissertation consultants can help you narrow down that topic and doctoral dissertation consultants can help you do the initial research that you must do before you choose your topic. And though it might seem obvious to choose a topic, this is in fact not always the case as many topics sound like a good idea, but do not make sense statistically. Thus, doctoral dissertation consultants can point you in the right direction as you choose a topic that is interesting to you.

Once you have chosen a topic, doctoral dissertation consultants can help you word or phrase that topic in a statistically-appropriate manner. If the topic is not phrased correctly, it will not get accepted and doctoral dissertation consultants are available to help steer you in the right direction in terms of phrasing the topic accurately and appropriately.

Once the topic has been chosen and is written in a statistically-appropriate manner, doctoral dissertation consultants will help you carry out the research portion of your dissertation. This is by far the most time-consuming area of the dissertation. This can be made even more time consuming if you gather data incorrectly or if you gather biased data. Doctoral dissertation consultants will not let that happen, however, as doctoral dissertation consultants are trained in statistics and can help you gather data. Doctoral dissertation consultants know all of the rules, guidelines, procedures and protocols for gathering data and thus, doctoral dissertation consultants will help you every step of the way in terms of gathering data. And while other doctoral degree-seeking students might struggle with gathering data and might have to start over because their data is invalid, with the help of doctoral dissertation consultants, you will be able to move on to the next step quickly and efficiently.

Because doctoral dissertation consultants are trained statisticians, they can also help you interpret the data that you have obtained. This too can be very time consuming if you are not trained in statistics. Granted, some students have the statistical know-how to get the job done efficiently, but most students are not in that same boat! In other words, most students are not trained in statistics (an anthropology major looking to get his or her dissertation, for example, might not have all of the necessary training in statistics). Thus, doctoral dissertation consultants will help you with your statistical needs and a doctoral dissertation consultant will guide you step by step through the process of statistics. In other words, not only will you come up with valid inferences and results, but you will also understand these results. This last part, the understanding of the statistics, is crucial, as it will be you who has to defend your dissertation—not the doctoral dissertation consultants. Doctoral dissertation consultants know this and they therefore prepare you for the defense of your dissertation.

There is no question, then, that doctoral dissertation consultants can be extremely beneficial throughout the entire process of the dissertation.

Dissertation Statistics Services

One of the most helpful things that a student can do while working on his or her dissertation is to acquire the help of dissertation statistics services. Dissertation statistics services can ensure that a student finishes his or her dissertation on-time and successfully.

While the ideal time to seek the help of dissertation statistics services is at the very beginning of a project, a student can get the assistance of dissertation statistics services at any point while working on his or her dissertation. Of course, if a student seeks dissertation statistics services at the beginning of the project, dissertation statistics services can ensure that the student starts off on the right foot. This is true because many students do not seek the help of dissertation statistics services until they are months into their project and they begin to struggle as they realize that they have done parts of their research incorrectly. Dissertation statistics services can ensure that students avoid the frustrating experience of having to start parts of the dissertation all over.

Dissertation statistics services provide help on all things related to statistics. This is true because dissertation statistics services are staffed with experts in both statistics and dissertations. In fact, most people who offer dissertation statistics services have themselves acquired their dissertation degree. This is helpful because these people offering dissertation statistics services know exactly what students should expect as they have gone through it themselves.

One of the most important parts of the dissertation is the statistics involved in the dissertation. Without statistics, a student cannot prove his or her thesis, and therefore cannot prove his or her dissertation properly. Statistics, then, play a huge role in the dissertation. Oftentimes, however, students are not trained adequately in statistics. Granted, they have taken some classes in statistics and they know the basics. This basic training, however, does not prepare a student for what they need to complete a successful dissertation. Dissertation statistics services can fill in the gaps of the student’s knowledge of statistics, however, as dissertation statistics assistance can provide all of the feedback, guidance and assistance that a doctoral-seeking student needs.

Dissertation statistics services help students with every statistical aspect of their dissertation. Thus, dissertation statistics services will help students perform the following important steps;

• Dissertation statistics services will ensure that the proper data is collected and acquired. This is oftentimes very difficult and time consuming as there is precise methodology for the collection of data.
  
• Dissertation statistics services will ensure that the student is using the proper sample size. This too can be very time consuming as the wrong sample size will nullify the data.
  
• Dissertation statistics services will interpret the results of the data and dissertation statistics services will ensure that those results fit into the dissertation.

The services that dissertation statistics services provide are not limited to the dissertation, however. Dissertation statistics services will ensure that the student actually understands every single aspect of his or her dissertation. In other words, dissertation statistics services will go through every single point of the dissertation and every single methodology used in the dissertation. Dissertation statistics services do this so that the student can understand everything contained in the dissertation. This ensures that the student is able to properly defend his or her dissertation. In fact, dissertation statistics services are even willing and able to have video conferences about the data and information so that the student can understand it. In other words, dissertation statistics services will go to every possible end to ensure that the student both turns in a successful dissertation and also understands every single point made in that dissertation.

Dissertation Statistics Consultant

There is nothing more helpful to a doctoral student than a dissertation statistics consultant. Because the dissertation is the most important part of a doctoral candidate’s academic career, seeking a dissertation statistics consultant can be the most important and wise decision a student can make.

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A dissertation statistics consultant will ensure that the doctoral student successfully finishes his or her dissertation. The dissertation statistics consultant does this by helping the student from the very beginning of the dissertation. A dissertation statistics consultant, then, should be hired at the very beginning of the project as this will save the student much time and energy.

Once the dissertation statistics consultant is hired, the student and the dissertation statistics consultant can get to work on the very important and very lengthy process of working on, researching and writing the dissertation. The first thing that happens once a student hires a dissertation statistics consultant is that the student and the dissertation statistics consultant have a conversation about what the student actually wants to study. This conversation is extremely important because in this conversation, the student can verbalize everything that they want to study and everything that they need help with from the dissertation statistics consultant. This is also a good time to see whether or not the dissertation statistics consultant is qualified to help the student. In this conversation, the student can tell if the dissertation statistics consultant is 1) articulate and a good communicator, 2) willing to help in all things related to the dissertation, and 3) proficient in writing dissertations. The student should not be afraid to ask the dissertation statistics consultant about their qualifications, etc.

Once this conversation takes place and the student is satisfied with the dissertation statistics consultant, they can put together a plan that will work. In other words, they can collectively come up with a timeline to get things done. This timeline is essential as the dissertation can take months to finish. Without a timeline, students usually end up finishing well after they had anticipated.

Once the timeline is complete and the topic is chosen and approved, the dissertation statistics consultant and the student can get to work on the most important part of the dissertation—the statistics part of the dissertation. The statistics part of the dissertation is the most important part because the statistics will provide the evidence for the student’s theory or thesis. Without the statistics, the dissertation does not actually prove anything. Thus, the statistics part of the dissertation is the most important part of the dissertation.

This, of course, is where the dissertation statistics consultant can be most useful, as oftentimes, students do not know everything they need to know in order to get valid statistics on which to base their dissertations. Every aspect of getting accurate statistics can take an extremely long time—but this is not true with the steady help of dissertation statistics consultants! Dissertation statistics consultants will help the student step by step as he or she does everything that he/she needs to do in order to get accurate statistics and results. This includes the gathering of the data (which is lengthy and difficult in and of itself—as the proper gathering of data involves following very precise rules, guidelines, methodologies and regulations), the interpretation of the data (once again this is lengthy and difficult as this too requires that a student follow proper protocol and methodology), and the application of this data to the dissertation.

Once all of this is complete, the dissertation statistics consultant will go over every single aspect of the completed dissertation. The dissertation statistics consultant will even proofread the entire document—something that can save the student from turning in a dissertation full of minor errors (something that no student wants to do).

Finally and perhaps most importantly, the dissertation statistics consultant will ensure that the student actually understands everything that he or she has written about. Clearly, dissertation statistics consultants go a long way in ensuring success for the student.

Latent Class Analysis (LCA)

Latent class analysis (LCA) is a multivariate technique that can be applied for cluster, factor, or regression purposes.

Latent class analysis (LCA) is commonly used by the researcher in cases where it is required to perform classification of cases into a set of latent classes. The Latent class analysis (LCA) carried out on latent classes are based on categorical types of indicator variables. In Latent class analysis (LCA), indicator variables are those variables that are assigned as ‘1’ if their condition is true, and are otherwise assigned as ‘0.’

Latent class analysis (LCA) uses a variant called Latent profile analysis for continuous variables. Mixture modeling with the structural equation models is a major type of Latent class analysis (LCA).

Latent class analysis (LCA) divides the cases into latent classes that are conditionally independent. In other words, Latent class analysis (LCA) divides those cases in which the variables of interest are not correlated within any other variables in the class.

The model parameters in Latent class analysis (LCA) are the maximum likelihood estimates (MLE) of conditional response probabilities.

There are two ways by which the number of the latent classes in the Latent class analysis (LCA) is determined. The first and more popular method is to perform an iterative test of goodness of fit models with the latent classes in Latent class analysis (LCA) using the likelihood ratio chi square test.

The other method is the method of bootstrapping of the latent classes in Latent class analysis (LCA). The rho estimates refer to the item response probabilities in Latent class analysis (LCA).

The odds ratio in Latent class analysis (LCA) measures the effective sizes of the covariates in the model. The odds ratio in Latent class analysis (LCA) is calculated by carrying out multinomial regression. The dependent variable in this regression in Latent class analysis (LCA) is the latent class variable, and the independent variable is the covariate.

If the value of the odds ratio in Latent class analysis (LCA) is 1.5 for class 1, then it means that a unit increase in the covariate corresponds to a 50 % greater likelihood.

The posterior probabilities in Latent class analysis (LCA) refer to the probability of that observation that is classified in a given class.

Latent class analysis (LCA) is done using software called Latent Gold. This software in Latent class analysis (LCA) implements Latent class models for cluster analysis, factor analysis, etc. The latent models in Latent class analysis (LCA) support nominal, ordinal as well as continuous data.
There are certain measures of model fit in Latent class analysis (LCA).

The latent model in Latent class analysis (LCA) can be fitted to the data with the help of likelihood ratio chi square. The larger the value of the statistic in Latent class analysis (LCA), the more inefficient the model is to fit the data.

The difference chi square in Latent class analysis (LCA) is used to calculate the difference of the two model chi squares for the two nested models.

In order to assess the validity or the reliability of Latent class analysis (LCA) a statistic called Cressie-Read statistic is used. The validity of Latent class analysis (LCA) can be assessed with the help of the probability value being compared with the probability value of the model chi square.

It is assumed that Latent class analysis (LCA) does not follow linearity within the data.
Latent class analysis (LCA) does not follow the normal distribution of the data.

Latent class analysis (LCA) does not follow the homogeneity of variances.

Hypothesis Testing

Hypothesis testing is a scientific process of testing whether or not the hypothesis is plausible.

The following steps are involved in hypothesis testing:

The first step in hypothesis testing is to state the null and alternative hypothesis clearly. The null and alternative hypothesis in hypothesis testing can be a one tailed or two tailed test.

The second step in hypothesis testing is to determine the test size. This means that the researcher decides whether a test should be one tailed or two tailed to get the right critical value and the rejection region.

The third step in hypothesis testing is to compute the test statistic and the probability value. This step of the hypothesis testing also involves the construction of the confidence interval depending upon the testing approach.

The fourth step in hypothesis testing involves the decision making step. This step of hypothesis testing helps the researcher reject or accept the null hypothesis by making comparisons between the subjective criterion from the second step and the objective test statistic or the probability value from the third step.

The fifth step in hypothesis testing is to draw a conclusion about the data and interpret the results obtained from the data.

There are basically three approaches to hypothesis testing. The researcher should note that all three approaches require different subject criteria and objective statistics, but all three approaches of hypothesis testing give the same conclusion.

The first approach of hypothesis testing is to test the statistic approach.

The common steps in all three approaches of hypothesis testing is the first step, which is to state the null and alternative hypothesis.

The second step of the test statistic approach of hypothesis testing is to determine the test size and to obtain the critical value. The third step of the test statistic approach of hypothesis testing is to compute the test statistic. The fourth step of the test statistic approach of hypothesis testing is to reject or accept the null hypothesis depending upon the comparison between the tabulated value and the calculated value. If the tabulated value in hypothesis testing is more than the calculated value, than the null hypothesis is accepted. Otherwise it is rejected. The last step of this approach of hypothesis testing is to make a substantive interpretation.

The second approach of hypothesis testing is the probability value approach. The second step of this approach in hypothesis testing is to determine the test size. The third step of this approach of hypothesis testing is to compute the test statistic and the probability value. The fourth step of this approach of hypothesis testing is to reject the null hypothesis if the probability value is less than the tabulated value. The last step of this approach of hypothesis testing is to make a substantive interpretation.

The third approach of hypothesis testing is the confidence interval approach. The second step of hypothesis testing is to determine the test size or the (1-test size) and the hypothesized value. The third step of hypothesis testing is to construct the confidence interval. The fourth step of hypothesis testing is to reject the null hypothesis if the hypothesized value does not exist in the range of the confidence interval. The last step of this approach of hypothesis testing is to make the substantive interpretation.

The first approach of hypothesis testing is a classical test statistic approach, which computes a test statistic from the empirical data and then makes a comparison with the critical value. If the test statistic in this classical approach of the hypothesis testing is larger than the critical value, then the null hypothesis is rejected. Otherwise, it is accepted.

Content Analysis

Content analysis provides information related to newspaper promotions, stories, photographs, displays and classified advertisements, etc. All of these fall in the scope of the study of Content analysis. Content analysis consists of counting and classifying content.

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In Content analysis, the researcher gathers information about the entire paper. This information includes the number of pages, the number of sections, etc. Then, an in-depth Content analysis is performed by the researcher on each content item.

The researcher in Content analysis analyzes each story in terms of its attributes, including topics, sources treatments, writing styles, etc. The researcher in Content analysis analyzes newspaper promotions for things like type, color, topic and size.

The researchers conduct content analysis for certain reasons. These reasons are as follows:

• Content analysis is usually carried out to study the discrepancy in the trends of the content with respect to time.
• Content analysis is carried out to describe the reasons why the readers focus on certain topics of the content.
• Content analysis can be used to make comparisons on international differences.
• Content analysis helps in comparing group differences in the content.
• Content analysis can expose the usage of biased terms in the research. Such biased terms can influence the opinions or behaviors of people.
• Content analysis is also useful in the testing of hypotheses about the cultural and symbolic usages of terms in the content.
• Content analysis helps the researcher for purposes of coding as well. Coding on open ended questions is done with the help of Content analysis.

There are certain terms that are used in content analysis that are helpful in understanding Content analysis. For example, unitizing in content analysis is a process in which the investigator establishes uniformity in the analysis. Thus, the researcher in content analysis unitizes the words, sentences, paragraphs, etc.

Sampling is one of the crucial weapons in content analysis. The sampling plan in Content analysis is designed to minimize the distortion caused in some particular content due to certain major events, etc. In Content analysis, the content is generally enormous. Thus, the researcher utilizes the technique of sampling in order to make his content in content analysis less complicated. The theory behind sampling in content analysis consists of counting. This involves development of different kinds of similar-meaning terms.

Inference is a major part of content analysis. A contextual phenomenon in content analysis must be analyzed in order to obtain a valid inference of the context for findings.

Content analysis involves conclusions that are usually communicated by the researcher in a narrative manner.

There are basically two assumptions in content analysis. First, content analysis is generally assumed to be subjected to the problems of sampling. Second, content analysis is assumed to be based upon the context for words and meanings.

There are certain software resources for conducting content analysis. These include the following:

• ATLAS.ti is used in content analysis as software for text analysis and model building.
• The General Inquirer is the classic package for content analysis.
• Intext and TextQuest is software developed by Harald Klein for content analysis.

Heteroscedasticity

The crucial assumption of a classical linear regression model is that the volatility that has occurred in the model should be uniform in nature. If this assumption is not satisfied by the model, then one would have to consider that the model has been exposed to heteroscedasticity.
There are examples that can be discussed to gain a better understanding of heteroscedasticity. In the case of an income expenditure model, if the income is decreased, then the expenditure will also simultaneously decrease, and vice versa. If, however, heteroscedasticity is present in the model, then as the income is increased, then the graph for the expenditure variable would remain constant.

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Heteroscedasticity generally occurs due to the presence of an outlier. An outlier in relation to heteroscedasticity is nothing but an observation that is numerically apart from the rest of the observations given in the data.

Heteroscedasticity can occur if a major variable is eliminated from the model. In the case of the income expenditure model, for example, if the variable called ‘income’ is eliminated, then there would be no inference from that model, and one would have to consider that the model has undergone heteroscedasticity.

Heteroscedasticity can also occur due to the presence of symmetrical or assymeterical curves of the regressor included in the model.

Heteroscedasticity can also occur due to false data transformation and incorrect functional form (like comparisons between a linear model and a log linear model, etc.).

Heteroscedasticity is a common or popular type of disturbance, especially in cases involving cross sectional data or time series data. If investigators who conduct ordinary least squares (OLS) do not consider the disturbance caused by heteroscedasticity, then they would not be able to examine the confidence intervals and the tests of hypotheses. This is because in the presence of heteroscedasticity, the variance calculated would be significantly less than the variance of the best linear unbiased estimator. As a result, the outcomes of the significant tests will not be accurate due to heteroscedasticity.

For a researcher to detect the presence of heteroscedasticity in the data, certain informal tests have been proposed by several econometricians.

There is a high probability of heteroscedasticity in a cross sectional data if small, medium and large organizations are sampled together.

An informal method, called the graphical method, helps the researcher to detect the presence of heteroscedasticity. If the investigator assumes that there is no heteroscedasticity and then performs regression analysis, the estimated residuals (with the help of the graphical method) would then exhibit certain patterns that would indicate the presence of heteroscedasticity.
A formal test, called Spearman’s rank correlation test, is used by the researcher to detect the presence of heteroscedasticity.

Suppose the researcher assumes a simple linear model, for example- Yi = β0 + β1Xi + ui - to detect the presence of heteroscedasticity. The researcher then fits the model to the data by calculating the absolute values of the residual and further sorting them in ascending or descending manner to detect heteroscedasticity. Then, the researcher computes the value of Spearman’s rank correlation for heteroscedasticity.

The researcher then assumes the population rank correlation coefficient as zero and the size of the sample is assumed to be greater than 8 for heteroscedasticity. A significance test is carried out to detect heteroscedasticity. If the computed value of t is more than the tabulated value, then the researcher assumes that heteroscedasticity is present in the data. Otherwise heteroscedasticity is not present in the data.

Dissertation Statistics Consultation

A dissertation statistics consultation is a service that a dissertation statistics firm can provide to anyone who needs to write a dissertation. A dissertation statistics consultation is a way to help students who are struggling with the statistics part of their dissertation. Thus, dissertation statistics consultations can make the difficult task of writing a dissertation, and moreover, dealing with statistics, much more manageable.

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Dissertation statistics consultations involve closely working with a dissertation consultant. With the help of dissertation statistics consultations, the student can navigate the difficult aspects of his or her dissertation. And while a student’s advisor can provide similar help as a dissertation statistics consultation, oftentimes a student’s advisor is not easily accessible or available. Dissertation statistics consultations provided by dissertation consultants are always available however, as the main goal of the dissertation statistics consultation is to help students whenever they need help.

First, dissertation statistics consultations involve a lengthy discussion about the topic of study. In the dissertation statistics consultation, the student and the dissertation consultant discuss all aspects of the topic. The dissertation statistics consultation can provide valuable feedback to the student at this stage in the process, as the dissertation statistics consultation provided by the dissertation consultant will address several issues that are likely to come-up when the student attempts to get the topic approved. One such issue is whether or not the topic is appropriate and able to be studied. In other words, with the help of a dissertation statistics consultation, the student will have a better grasp of whether or not the topic can actually be studied, and whether or not that topic should be chosen by the student. Another issue that comes up during the topic- choosing phase is addressing the concern of whether or not that topic has been studied before. A proper dissertation statistics consultation will advise students on how to do research into whether or not their topic of study has already been studied.

The next service that a dissertation statistics consultation will provide is to explain to the student how to write that topic in a way that makes sense statistically. This part of the dissertation statistics consultation is essential because without the proper wording for the topic, the dissertation will not get approved. A dissertation statistics consultation will explain this wording so that it gets approved and makes sense to the student.

Once the topic is chosen and is phrased correctly, the dissertation statistics consultation provided by dissertation consultants can address the actual gathering of statistics. Because statistics is a science, the gathering of data and the interpretation of that data need to be done meticulously. A dissertation statistics consultation will ensure that the student knows how to gather proper statistics because a dissertation statistics consultation will discuss the methods, means and theories behind the gathering of data. Dissertation consultants are well versed in the gathering of data, and in the dissertation statistics consultation, the dissertation consultants will explain these precise methods of gathering data. Thus, with the help of dissertation consultants, students can gather data much quicker and much more efficiently.

Once the data is gathered, a dissertation statistics consultation provides all of the necessary information as to how to interpret the results and apply it to the dissertation. Here too the expertise of the dissertation consultant comes into play as the dissertation statistics consultation will go over every facet of the interpretation of results.

Finally, a dissertation statistics consultation service will ensure that the dissertation is finished accurately and on-time. With the on-going help of a dissertation consultant, the student will be able to finish his or her dissertation with much success.

Correlation

Correlation, as the name suggests, depicts a relationship between two or more variables under study. Correlation is generally categorized into two types, namely Bivariate Correlation and Partial Correlation.

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Bivariate Correlation is the one that shows an association between two variables. Correlation is the one that shows the association between two variables while keeping control or adjusting the effect of one or more additional variables.

A Correlation is a degree of measure, which means that a Correlation can be negative, positive, or perfect. A positive Correlation is a type of Correlation in which an increase changes the other variable. In other words, if there is an increase (or decrease) in one variable, then there is a simultaneous increase (decrease) in the other variable. A negative Correlation is a type of Correlation where if there is a decrease (or increase) in one variable, then there is a simultaneous increase (or decrease) in the other variables.

A perfect Correlation is that type of Correlation where a change in one variable affects an equivalent change in the other variable.

A British biometrician named Karl Pearson developed a formula to measure the degree of the Correlation, called the Correlation Coefficient. This Correlation Coefficient is generally depicted as ‘r.’ In mathematical language, the Correlation Coefficient, which was developed by the biometrician Karl Pearson, is defined as the ratio between the covariance of the two variables and the product of the square root of their individual variances. The range of the Correlation Coefficient generally lies between -1 to +1. If the value of the Correlation Coefficient is ‘+1,’ then the variable is said to be positively correlated. If, on the other hand, the value of the Correlation Coefficient is ‘-1,’ then the variable is said to be negatively correlated.

The value of the Correlation Coefficient does not depend upon the change in origin and the change in the scale.

If the value of the Correlation Coefficient is zero, then the variables are said to be uncorrelated. Thus, the variables would be regarded as independent. If there is no Correlation in the variables, then the change in one variable will not affect the change in the other variable at all, and therefore the variables will be independent.

However, the researcher should note that the two independent variables are not in any Correlation if the covariance of the variables is zero. This, however, is not true in the opposite case. This means that if the covariance of the two variables is zero, then it does not necessarily mean that the two variables are independent.

There are certain assumptions that come along with the Correlation Coefficient. The following are the assumptions for the Correlation Coefficient:

The Correlation Coefficient assumes that the variables under study should be linearly correlated.
Correlation coefficient assumes that a cause and effect relationship exists between different forces operating on the items of the two variable series. Such forces assumed by the correlation coefficient must be common to both series.

For the cases where operating forces are entirely independent, then the value of the correlation coefficient must be zero. If the value of the correlation coefficient is not zero, then in such cases, correlation is often termed as chance correlation or spurious correlation. For example, the correlation between the income of a person and the height of a person is a case of spurious correlation. Another example of spurious correlation is the correlation between the size of the shoe and the intelligence of a certain group of people.

A Pearsonian coefficient of correlation between the ranks of two variables, say, x and y, is called rank correlation coefficient between that group of variables.

Conjoint Analysis

Conjoint Analysis is considered a type of analyses that is used in the field of market research. Conjoint Analysis is used by the researcher to obtain the statistically significant importance that a consumer in the market gives to the crucial characteristics of a particular product. Conjoint Analysis is also used to obtain the utilities that a consumer in the market attaches to the various levels of the attributes of a particular product.

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All of this is determined in Conjoint Analysis with the help of an assessment done on the consumer’s preference towards a particular set of characteristics of a brand or a brand profile.

The researcher working on Conjoint Analysis constructs stimuli that consist of a questionnaire. This questionnaire consists of certain attribute levels of a particular brand under study. This stimulus in Conjoint Analysis is filled-out by the respondents participating in the study.

In order to obtain a valid inference about the study with the help of Conjoint Analysis, it is crucial that the respondents participating in the study respond to the stimulus in an appropriate manner. The respondents in Conjoint Analysis should address the questions of the stimuli according to their desirability.

These evaluations carried out in Conjoint Analysis are reliable only if the subjective evaluations of the respondent are true.

Conjoint Analysis, therefore, addresses various issues. The utilization of Conjoint Analysis is done in order to determine the comparative importance of the crucial characteristics that affect the choice of the consumer. Conjoint Analysis is used in estimating the share of market brands that fluctuates by the level of attributes.

Thus, in a similar manner, Conjoint Analysis can be used by the researcher to assess the consumer’s preference over the attributes of consumer goods, industrial goods, etc. The process of Conjoint Analysis is useful in cases where one needs to address certain issues instead of carrying out the concept of testing. Conjoint Analysis is useful for a person who is not so well versed with statistical skills.

The model that is used by the researcher in Conjoint Analysis to fit the data obtained is the utility function model. This model in Conjoint Analysis is a mathematical model that is used by the researcher to establish a fundamental relationship between the attributes and the utility attached to the product under study.

In Conjoint Analysis, the dependent or the predicted variable is generally the variable that is labeled as the preferences that make the customers attached to a particular brand.

In order to assess the reliability or the validity of Conjoint Analysis, there are several procedures that have been developed.

In Conjoint Analysis, a reliability test called the test retest reliability test, is used by the researcher to obtain identical judgments that are sometimes present in the process of data collection. If the Conjoint Analysis is carried out in a collective manner, then the estimated sample is split into several samples. Then, on each of the split sub samples, Conjoint Analysis is carried out in order to assure whether or not the Conjoint Analysis is valid.

The steps involved while conducting conjoint analysis are the following:

• The first step in conjoint analysis is to form a problem.
• The next step in conjoint analysis is to construct stimuli.
• The third step in conjoint analysis is to choose the form of input data.
• The fourth step of conjoint analysis consists of the selection of the conjoint analysis procedure.
• The fifth step is to infer the results from conjoint analysis.
• The last step is to assess the reliability and validity of conjoint analysis.

Statistical Formula

Statistical formula can be defined as the group of statistical symbols used to make a statistical statement.

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The term called the expected value of some random variable X will be represented by the statistical formula as E(X)= μx=∑[xi * P(xi)]. In this statistical formula, the symbol ‘μx’ represents the expected value of some random variable X. In this statistical formula, the symbol ‘P (xi)’ represents the probability that the random variable will have an outcome ‘i.’ In this statistical formula, the expected value of the random variable X will be computed in the above described manner if the random variable is discreet in nature.

The term called the variance of some random variable X is represented by the statistical formula as Var(X) =σ2 = Σ [Xi – μx]2 * P(xi). In this statistical formula, the symbol ‘σ2’ represents the variance of that random variable.

The term called the chi square statistic will be represented by the statistical formula as X2=[(n-1)*s2]/ σ2. In this statistical formula, the X2 is being represented as the chi square statistic. In this statistical formula, ‘n’ represents the size of the sample. In this statistical formula, ‘s2’ represents the sample variance.

The term called the f statistic will be represented by the statistical formula f=[s12/ σ12]/ [s22/ σ22]. In this statistical formula, s12 represents the variance of the sample drawn from population 1 and s22 represents the variance of the sample drawn from population 2.

The expected value of the sum of two random variables, for example, random variable X and random variable Y, will be represented by the statistical formula as E(X+Y)=E(X)+E(Y). The term E(X) and E(Y) in the statistical formula is nothing but the same as described above.
The expected value of the difference between the random variables will be represented by the statistical formula as E(X-Y) =E(X)-E(Y). The term ‘E(X-Y)’ in the statistical formula is nothing but the expected value of the difference between the random variables.

The variance of the sum of the independent variable is represented by the statistical formula as Var(X+Y) = Var(X)+Var(Y). Ideally, in this statistical formula, the covariance between the two variables should also exist, but since the two variables are independent in nature, the covariance in this statistical formula will not exist.

The standard error of the difference for proportion is represented by the statistical formula as SEp= sp = sqrt [ p*(1-p)*{1/n1 + 1/n2} ] . The term ‘SEp’ in the statistical formula represents the standard error for difference proportion. The term ‘p’ in the statistical formula is the pooled sample variance. The term ‘n1’ in the statistical formula represents the size of the first sample and the term ‘n2’ in the statistical formula represents the size of the second sample, which is pooled with the first sample.

The binomial formula is represented by the statistical formula as P(X=x)=b(x;n,P)= nCx * px(1-p)n-x. The term ‘n’ in this statistical formula represents the number of trials. The term ‘x’ in this statistical formula represents the number of successes in ‘n’ trials. The term ‘p’ in this statistical formula represents the probability of getting success from the ‘n’ binomial trials.
The poisson formula is represented by the statistical formula as P(x;µ)=(e-µ)(µx)/x!. The term ‘µ’ in the statistical formula represents the mean number of the successes that has occurred in a specific region. The term ‘x’ in the statistical formula represents the actual number of successes that has occurred in a specific region. The term ‘e’ in the statistical formula represents the base of the natural logarithmic system. Its value is approximately 2.71828.

Canonical Correlation

Canonical correlation was developed by H.Hotelling. Canonical correlation analysis is a method of measuring the linear relationship between two variables that are multidimensional in nature. The canonical correlation analysis locates the bases for each variable that are most favorable with respect to the association. The canonical correlation analysis also determines the corresponding associations. In other words, the canonical correlation analysis determines the two bases in which the association matrix between the variables is diagonal and the associations on the diagonals are maximized. The dimensionality of these new bases in canonical correlation is equal to or less than the smallest dimensionality of the two variables.

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One of the crucial properties of canonical correlation is that they are independent in relation to the transformation of the variables. This property of canonical correlation indicates the difference between the canonical correlation and the ordinary types of correlation.

Canonical correlation is a standard tool in statistical analysis which is used in the fields of economics, medical studies, etc.

In statistical language, the canonical correlation can be defined as the problem of finding two sets of basis vectors in such a manner that associations between the projections of the variables into the basis vectors are mutually maximized.

The canonical correlation between the two random vectors can be obtained by calculating the Eigen value equations. The Eigen values are nothing, but are equivalent to the square of the canonical correlation.

The canonical correlation is a member of the multiple general linear hypothesis family and contributes most of the assumptions of multiple regression, such as the linearity of relationships, homoscedasticity, interval level of data, proper specification of the model, lack of high multicollinearity, etc. The canonical correlation is also called a characteristic root.

The maximum number of canonical correlation between the two sets of variables is the number of variables in the smaller set.

The pooled canonical correlation is the sum of squares of all the canonical coefficients and it represents all the orthogonal dimensions in the solution by which the two sets of variables are associated. The pooled canonical correlation is used to extract the extent to which one set of variables can be forecasted by the other set of variables.

The canonical weights are nothing but the canonical coefficient in canonical correlation, which is used to assess the comparative importance that is contributed by the individual variable to a given canonical correlation.

The canonical scores in the canonical correlation are the values that are assigned to the canonical variable for a particular case. This score of canonical correlation is based on the value of the canonical coefficients for that variable. The canonical coefficients in canonical correlation are multiplied by the scores that are standardized and are then summed to yield canonical scores.

The structure correlation coefficients in canonical correlation are also called canonical factor loadings. It is defined as the canonical correlation of a canonical variable with an original variable in its set. The squared structure correlations in canonical correlation depict the contribution of a variable to the explanatory power of the canonical variate based on the set of variables.

Multicollinearity

Multicollinearity depicts the stage in which the independent variables in the data show high interactions and associations. Therefore, multicollinearity is considered a disturbance that causes volatility in data.

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There are reasons behind the outcome of multicollinearity in data.

Multicollinearity can occur due to the improper utilization of dummy variables. Researchers who are not experts can end up causing multicollinearity in the data.

If the researcher includes a variable which is being computed from the other variables in the equation, then this action can cause multicollinearity in the data. For example, if the family’s health is equal to the husband’s health+ wife’s health + child’s health, and the regression includes all four health cases, then this calls for multicollinearity.

If a researcher includes the same type of variables twice in an experiment, then this activity performed by the researcher causes multicollinearity. For example, if two models of Nokia phones are included as different variables in the study, then this causes multicollinearity in the data.

There are certain outcomes of multicollinearity.

The researcher should note that as the level of multicollinearity increases, the value of the standard error gets higher and higher. When there is high multicollinearity in the data, then the confidence intervals for the coefficients tend to be extremely wide and the value of the t-statistics tends to be very small. The researcher should keep in mind that the value of the coefficients should be larger in order to have it statistically significant. In other words, in the presence of multicollinearity, the null hypothesis assumed by the researcher is harder to get rejected.

If the value of the tolerance is closer to the value of one, then this means that there is very little multicollinearity. On the other hand, if the value of the tolerance is closer to zero then this means that there is very high multicollinearity. So, in the latter case, the multicollinearity is considered a threat.

The reciprocal of the tolerance is known as the variance inflation factor (VIF). The variance inflation factor shows the amount of variance of the coefficient estimate that is inflated by multicollinearity.

Multicollinearity is not something that is discreet in nature, but is a matter of degree. The matter of multicollinearity can be detected with the help of certain warning signals.

If the t ratios for each coefficient are statistically significant, and the F statistic is not statistically significant, then this indicates that there is multicollinearity in the data.

It is important to check the stability of the coefficients when two different types of samples are used. If the coefficients differ quiet significantly, then this shows that there is multicollinearity in the data.

If the sign of the variables gets changed, or if some variables are being added, then this signifies the presence of multicollinearity.

In order to address the problem of multicollinearity, one has to make sure that he/she does not conduct improper usage of the dummy variables.

If the sample size is increased from the desired sample size, then this will decrease the value of the standard errors and simultaneously it would decrease the level of multicollinearity.

It is sometimes suggested that the researcher drop the variable that is causing multicollinearity. The researcher should keep in mind that if the most important variable is dropped by the researcher, then this would cause a specification error, which is even worse than multicollinearity.

The most important thing for obtaining a valid inference about the data is to realize the presence of multicollinearity. Additionally, a researcher should be aware of the consequences of multicollinearity.

Dissertation Statistics Help

The most trying, difficult, and lengthy aspect of a student’s academic career is writing his or her dissertation. Because the dissertation is the last hurdle standing in the way of a student attaining a doctoral degree, it can also be one of the most frustrating aspects of a student’s academic life. The dissertation, then, is the most important and most difficult part of attaining a doctoral degree.

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Unfortunately, many students struggle with their dissertation because they do not know that dissertation help is available from dissertation consulting firms. Dissertation consulting firms provide expert assistance and guidance to any student seeking to obtain their doctoral degree. Dissertation consulting firms are capable of helping students from the very beginning of the project and they are certainly willing to make sure that the dissertation is completed to its fruition.

Among the services that dissertation consulting firms provide is offering dissertation statistics help. Dissertation statistics help assists students with all things relating to statistics. All dissertations have statistics in them because all dissertations must offer proof for the argument, question or thesis. Statistics provide this proof. Many students are not well versed in statistics, however, and this is where dissertation statistics help proves to be invaluable to students as dissertation statistics help provides one-on-one help for students struggling to complete the statistics portion of their dissertation.

Among other things, dissertation statistics help assists students as dissertation statistics help does the following:
· Dissertation statistics help lets students know if their topic can be studied. In other words, dissertation statistics help provides information as to whether or not statistics can be gathered on the topic or subject.
· Dissertation statistics help assists students in gathering the research necessary to begin the project.
· Dissertation statistics help plays a key role in gathering the necessary data off of which to base the statistics.
· Dissertation statistics help accurately interprets the data and information.
· Dissertation statistics help provides valid statistics from the interpretation of the data.
· Dissertation statistics help ensures that the student is using the statistics properly to make the correct conclusions.
· Dissertation statistics help makes sure that those conclusions are then applied to the entire dissertation.

While the above bulleted points help to illustrate just how valuable dissertation statistics help can be for students who need assistance with statistics, dissertation statistics help offers much more than can be listed in a document. This is because dissertation statistics help explains each of the above bulleted points so that the student actually understands these points. The student, then, gains confidence in his or her research and abilities as dissertation statistics help ensures that the student understands every single aspect of what is going on in his/her dissertation. In other words, dissertation statistics help enables students to grasp the concept of statistics. There is no reason for a student not to understand everything that they are writing about, and dissertation statistics help will explain everything in a step-by-step fashion. The student, then, will feel confident with his/her dissertation and will be prepared to defend his or her dissertation because of the help provided by dissertation statistics help.

When one considers the fact that dissertation statistics help is available, it becomes clear that it makes no sense for a student to struggle with his/her dissertation alone. Dissertation statistics help can provide clear, meaningful, and accurate results. More importantly than dissertation statistics help providing the necessary “nuts and bolt” of the statistics, dissertation statistics help will allow students to finish their dissertations, with their diploma in hand and their head held high!

Dissertation Methodology

The final step a student needs to take to acquire a doctoral degree is to finish his or her dissertation. The dissertation, however, is lengthier and more difficult than anything the student has done up to this point. This is because a dissertation must be accurate and precise, and it must offer new information on the subject or field. And this last part—offering new information to the subject or field— is especially difficult.

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The dissertation itself is broken down into numerous parts, and each of these parts must be accepted and approved if the student is to obtain his or her doctoral degree. One of these parts is the dissertation methodology. The dissertation methodology is essentially the part of the dissertation that lays out the entire dissertation. In other words the dissertation methodology is a chapter of the dissertation that describes the methods that the researcher will use to approach his or her topic of study. The dissertation methodology, then, must be detailed and accurate because the dissertation methodology will allow others to see what is to be done in order to come up with the results.

Oftentimes, the dissertation methodology takes a student more time than it should. Granted, the dissertation methodology should take a long time to complete—for it is the detailed explanation of what is to be done and how it is to be done—but it should not delay students as much as it sometimes does. In other words, oftentimes the dissertation methodology chapter of the dissertation is where students spend an unproductive amount of time wondering what to do with their dissertation methodology and what to put in their dissertation methodology chapter.

Because methodology can be defined as “a body of practices, procedures, and rules used by those who work in a discipline or engage in an inquiry” (Dictionary.com) the dissertation methodology chapter should be specific, precise and accurate. Thus, in order for a dissertation methodology to be accepted, the dissertation methodology must do several things. First, the dissertation methodology should outline the problem that is to be studied. This is an obvious part of the dissertation methodology because without the problem that is to be studied, the dissertation methodology would be nonexistent. In other words, you need a problem to study in order to have a way to study it! Second, the dissertation methodology must outline the plans that the researcher intends on using to solve this problem. This is where the specifics come into play, and this is where the dissertation methodology must be precise. In this part of the dissertation methodology, the researcher should say what tests will be used and why such tests should be used. Because there are an abundant number of tests, procedures, and guidelines while conducting research and performing statistics, this part of the dissertation methodology is one of the trickiest parts to complete. The dissertation methodology, then, must make the method of study clear so that the people reading the dissertation methodology can see exactly what is to be done in order to gather the proper data and information. Finally, the dissertation methodology must then identify some of the various hurdles or difficulties that the researcher may experience while solving the problem or question. This is an important part of the dissertation methodology as it will provide more context into what, exactly, needs to be done and why it needs to be done. The combination of all three of these will lead to a detailed dissertation methodology section and a dissertation methodology section that is acceptable (providing that the dissertation methodology is also accurate and precise).

Because dissertations must follow dissertation formats, there is a specific format for the dissertation methodology section as well. There is some variance here, however, as different advisors can tweak this section to their liking. This tweaking, however, is by no means justification to do a dissertation methodology section anyway a student wants to. On the contrary, the dissertation methodology must follow certain guidelines and must be laid out according to a format that includes an introductory paragraph, an outline of the general dissertation methodology, and a type of conclusion.

It is, of course, essential that the dissertation methodology chapter is completed with much attention to detail. And while it is time consuming to complete the dissertation methodology, a student should get help on the dissertation methodology if he/she is spending too long on that one section of the dissertation. A proper, accurate and detailed dissertation methodology will help ensure success on the overall dissertation. Thus, one should not hesitate to seek help in completing the dissertation methodology.

Estimation

The method of statistically drawing an inference on data is called the statistical inference. Thus, the testing of hypothesis and the inference are the most important factors involved in statistical inference. The theory of estimation is a part of statistics that extracts parameters from observations that are corrupted with noise.

Estimation is a division of statistics and signal processing that determines the values of parameters through measured and observed empirical data. The process of estimation is carried out in order to measure and diagnose the true value of a function or a particular set of populations. It is done on the basis of observations on the samples, which are a combined piece of the target population or function. Several statistics are used to perform the task of estimation.
There are two very important terms that are used in estimation: the estimator and the estimate. To understand the concept of the estimator and estimate in detail, we will use an example. Let’s say that a1, a2, a3 and so on is a collection of samples from some group of a certain population with ‘x’ as its parameter. Here, if T= T (a) is a statistic, then E (T(a)) = x. From these equations we can realize that an estimation of the statistic has been carried out, where the statistic T is an estimator and the parameter ‘x’ is the estimator.

Estimation has many important properties for the ideal estimator. These properties include unbiased nature, efficiency, consistency and sufficiency.

The estimators that are unbiased while performing estimation are those that have 0 bias results for the entire values of the parameter. Going by statistical language and terminology, unbiased estimators are those where the mathematical expectation or the mean proves to be the parameter of the target population. In the above mentioned example for estimation, T is going to be the unbiased estimator only if its estimate comes out to be equal to ‘x.’

In estimation, the estimators that give consistent estimates are said to be the consistent estimators. As the number of random variables increase, the degree of concentration should be higher and higher around the estimate in order to make the estimator of estimation the consistent estimator. If the estimator gives an unbiased estimate and the variance of the estimator comes out to be zero, then the estimator of estimation is called the consistent estimator. These two conditions need to be fulfilled only if the numbers of random variables reach infinity.

In estimation there are many estimators that have ample incidences of consistent estimators, and according to the property of efficiency in estimation, the consistent estimators should be normally distributed. This kind of property was taken into account in the theory of estimation, because there were incidents of the estimators having ample consistent estimations but were not the efficient estimators.

An estimator is known as the sufficient estimator only when the joint conditional distribution function of the sample/observation has the condition of T1 T2 T3 T4 (and so on and so forth), and are the values under the given estimator function ‘T.’ Thus, the resultant joint conditional estimation has to be absolutely sovereign of the parameter ‘x.’While carrying out the task of estimation, a researcher should always know that the best estimator is the one that is the minimum variance unbiased estimator (MVUE). This means that the estimator has the minimum variability when it is compared to other estimators.

Methodology

The theoretical analysis of the fitting processes for a particular field of study is called methodology. Methodology is used for different fields of studies. One such field where methodology is important is psychology.

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Statistical methodology (like statistical significance testing) is used in the field of psychology. Statistical significance testing consists of tests such as the t-test, z-test, f-test, etc. These tests perform methodology. For instance, if one wants to diagnose and compare the death rate and birth rate of two different subject regions, then the t-test methodology is very appropriate in such a case. If the null hypothesis is arrived at in such an instance, it would mean that there is no statistically significant difference between the birth rates and death rates of the samples of the two subject regions. If the calculated t statistic goes over the tabulated t statistic in this methodology, then the assumed null hypothesis of this methodology is prone to be rejected at some particular level of significance.

Analysis of variance (ANOVA) is a statistical methodology that is used to diagnose the disparities in the mean values of dependant variables that are linked with the upshot of the controlled independent variables. Before this occurs, however, the influence of the uncontrolled independent variables is taken into account. In one way ANOVA methodology, there is only one categorical variable or one single factor that is involved in the process. In case more than one factor or variables are used in methodology analysis, then the methodology is known as ANOVA n way methodology.

Like any other methodology or statistical processes, this kind of methodology has its own set of assumptions. First, the nature of the extracted samples of this methodology should be random. Second, the nature of the variance involved in this methodology should be homogenous and not at all heterogeneous by any degree.

Another type of statistical methodology used in the field of psychology is called partial correlation methodology. When we want to manage the variables by controlling or adjusting the influence of more than one additional variable, this methodology measures the relationship between the two target variables. Behavioral study is a part of psychology where this partial correlation methodology proves to be very useful. Psychology is a form of social science and the quantitative methodology for it can be carried out by statistical software of social sciences called SPSS. To understand this methodology in the best manner, we should try and understand the basic terms involved in this methodology. The variables that extract the variances from the initial correlated variables are called the control variables, and the correlation with the control variables in this methodology is called the order of correlation. For example, first order partial correlation methodology has a single control variable. In addition to quantitative processes for this methodology, qualitative methods are also used for this methodology. The two qualitative techniques used in this methodology are the Delphi Process and the Nominal Group Technique. In the Delphi process, a number of experts provide independent and expert forecasts with the help of some questionnaires. The forecasts of the experts are thoroughly amended to reach one united decision consisting of many assumptions in that one whole. Nominal Group Technique is used to set a priority structure for issues that involve many participants. It is utilized to judge strengths and weaknesses of institutions, departments, etc. The best thing about this methodology is that it carries a balanced approach to the entire participant through discussions, where all the participants are allowed to have their say in the presence of a moderator. Thus the Nominal Group methodology is considered to be more reliable than the Delphi process methodology.

Dissertation Data Analysis

In order to receive their doctoral degrees, students must first successfully complete a dissertation. Part of this dissertation involves dissertation data analysis. Dissertation data analysis consists of all of the data that accompanies the dissertation and the interpretation of that data. Dissertation data analysis, then, is a crucial part of the dissertation.

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The dissertation is a difficult thing to complete because it is extremely time consuming and laborious. Part of what makes it so time consuming is the accumulation of all of the data that is necessary for the dissertation. Further, once this data is actually accumulated, it must be interpreted properly. This dissertation data analysis must follow proper methodology and this is where many students need help as they are not trained in dissertation data analysis.

Dissertation data analysis involves the breaking down of complex statistics and numbers in order to prove a student’s thesis. Dissertation data analysis is done by following very precise methodologies. Dissertation data analysis can only be done after the data for the dissertation data analysis has been collected by also following very precise methodologies. If the data that is collected is not accurate or valid, the dissertation data analysis will not be valid or precise either. Therefore, before beginning the dissertation data analysis, it is important for the researcher (or student) to make sure that he or she follows the proper methodologies while collecting the data.

Dissertation data analysis is very technical and follows strict rules, tests, and methodology. These tools for dissertation data analysis include t-tests, ANOVA, descriptive statistics, etc. Unfortunately, most doctoral students are not trained to do proper dissertation data analysis because it is so technical and because dissertation data analysis requires the understanding of the tests and methodology of statistics.

Fortunately however, there is help for dissertation data analysis as students can seek dissertation consultants for dissertation data analysis help. Dissertation consultants are trained in all things regarding statistics and dissertation consultants are therefore proficient in dissertation data analysis. Additionally, getting the help of dissertation consultants will save students much time and energy. This is true because dissertation consultants will help students complete the dissertation and dissertation consultants will help students understand all aspects of the dissertation—from the proposal phase to the dissertation data analysis. Not only will dissertation consultants help students in terms of finishing on-time, dissertation consultants will also explain everything carefully to the student. This is important because students need to understand their research and methodology in order to complete the oral defense of their dissertation. Dissertation consultants know this and dissertation consultants make sure that they explain every single aspect of the dissertation to the student. Dissertation consultants do this by going over every step of the dissertation with the student—including reviewing the dissertation data analysis. As mentioned earlier, the dissertation data analysis can be very technical and the dissertation data analysis can be one of the most complicated aspects of the dissertations. Dissertation consultants will guide students through this difficult dissertation data analysis and dissertation consultants will make sure that everything that the student does in terms of the dissertation data analysis is accurate, precise and related to the dissertation. What’s more, dissertation consultants will go out of their way to make sure that the student understands every single component and aspect of the dissertation data analysis.

Dissertation consultants, then, can be extremely valuable to anyone writing a dissertation. Dissertation consultants have themselves obtained their doctoral degrees and therefore dissertation consultants know exactly what the student needs to do. Additionally, dissertation consultants know how to explain the complex procedures and methodologies of statistics to each and every student—and dissertation consultants will do so regardless of the student’s statistical abilities.

Attribute

An attribute refers to the quality of a characteristic. The theory of attribute refers to the qualitative characteristics that cannot be computed with the help of quantitative measurements. Thus, attributes require slightly different statistical treatment from the treatment of variables. Some of the examples of attributes are smoking, drinking, blindness, intelligence, etc.

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An attribute can be noticed by its presence or absence. It should be known that the methods of statistical techniques that are used in the study of the variables can also be used at a much wider extent in the theory of the attribute and vice versa.

There are certain notations used in the theory of attribute.

• A population is divided into two classes, namely the negative and the positive class according to the presence or absence of an attribute.
• The positive class, which indicates the presence of the attribute, is generally denoted by capital roman letters like A, B, C, etc. The negative class, which indicates the absence of the attribute, is generally denoted by Greek letters like α, β, etc.
• The combination of the two attributes are denoted by grouping the letters together. In other words, AB is the combination of the two attributes A and B.
• If the population is divided into two subclasses with respect to each of the attributes, then that classification is termed a dichotomous classification.

The number of observations assigned to the attribute is termed as the class frequencies which are denoted by bracketing the attribute symbols. For example, (A) stands for the frequency of the attribute A. A class represented by ‘n’ attribute is called a class of nth order and the corresponding frequency of that attribute is the frequency of the nth order. For example, (A) is a class frequency of first order.

These class symbols of the attribute also work as an operator. For example, A.N=(A) means that the operation of dichotomizing N according to the attribute A gives the class frequency equal to (A).

The two attributes A and B are said to be independent only if there exists no relationship of any kind between those two attributes. If the two attributes are independent, then one can expect that the same proportion of A attribute amongst B attribute is the same as that amongst the β attribute, and the proportion of B attribute amongst A attribute is the same as that amongst the α attribute.

The two attribute A and B are said to be associated if the two attributes are not independent but are related in some way or another. The two attributes are said to be positively associated if (AB) > (A) (B)/ N, and are said to be negatively associated if (AB) < (A) (B) /N.

The two attributes A and B are said to be completely associated if the attribute A cannot occur without the attribute B, though the attribute B may occur without the attribute A, and vice versa.

Ordinarily, the two attributes are said to be associated if the two occur together in a number of cases.

The consistency between the two attributes (A)=20 and (AB)=25 is not present as the attribute (AB) cannot be greater than the attribute (A) if they have been observed from the same population.

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Thesis and Dissertation Consultants

All students seeking to obtain their doctoral degree must successfully finish a dissertation. This is by no means easy as the dissertation is one of the hardest things a student must do in his or her academic career. Thesis and dissertation consultants can help students in the process of writing their dissertation as thesis and dissertation consultants can guide students through every part of the dissertation.

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Thesis and dissertation consultants are experts that a student can hire to help with the process of the dissertation. Many students do not know that thesis and dissertation consultants can be so helpful because many students begin their theses and dissertations thinking that they do not need any help. It is only after months and months of failed research and design that students realize the benefit they can get from thesis and dissertation consultants. Thesis and dissertation consultants, then, should be sought in the initial stages of the dissertation as thesis and dissertation consultants can help students so that they do not have to waste that valuable time with failed research and design.

Another reason why students do not seek thesis and dissertation consultants is because they believe that hiring thesis and dissertations consultants is unethical. This is untrue, however, because thesis and dissertation consultants do not do the work for the students. As the name thesis and dissertation consultants (key word consultants) suggests, thesis and dissertation consultants help students with the dissertation. In other words thesis and dissertation consultants consult students, they do not write the dissertation for the student. A similar analogy to what thesis and dissertation consultants do would be to compare thesis and dissertation consultants to a tutor. When a student struggles with a subject, oftentimes that student hires a tutor. The tutor can then help the student by teaching the student the valuable skills they need that they do not have. Thesis and dissertation consultants do the exact same thing because thesis and dissertation consultants are interested in teaching the student what he or she needs to know so that the student is able to complete the dissertation on-time and successfully. Again, the thesis and dissertation consultant does not do the work for the student. On the contrary, the thesis and dissertation consultant guides the students and essentially “tutors” the student on what needs to be done. And while the student’s advisors can do the very same thing, often a student’s advisor is not available when the student needs him/her. Much like a teacher not being available for a student all of the time, an advisor cannot be there for the doctoral candidate at all times. Thesis and dissertation consultants can be, however, and therefore thesis and dissertation consultants can act as a guide and educator for the student. Hiring thesis and dissertation consultants, then, is ethical as thesis and dissertation consultants are there to provide help, feedback, assistance and guidance for the student—not to do the work for the student.

Once thesis and dissertation consultants are hired, they can go about the lengthy task of working on the dissertation. Thesis and dissertation consultants are invaluable in this process as thesis and dissertation consultants provide the following help:

• Thesis and dissertation consultants help the student choose the topic
• Thesis and dissertation consultants help the student phrase the topic appropriately, in statistical language
• Thesis and dissertation consultants help with the dissertation research necessary before beginning the dissertation
• Thesis and dissertation consultants help the student through the entire proposal phase of the dissertation
• Thesis and dissertation consultants help with every single statistical procedure- including the collection of data (which includes following precise methodology, guidelines and rules in the collection of data), the interpretation of data (which again requires following precise methodology, rules and guidelines), and the application of that data and analysis to the dissertation
• Thesis and dissertation consultants proofread the entire dissertation
• Thesis and dissertation consultants prepare the student for the oral defense of the dissertation

Thesis and dissertation consultants can be invaluable to students seeking to attain their degrees. Thesis and dissertation consultants are professionals who have the expertise and know-how to get students through the difficult task of writing their dissertations successfully.

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Dissertation Statistics Services

Because statistics are the central part to most dissertations, dissertation statistics services are available to help students who needs help in statistics. Dissertation statistics services are designed to help students with the accurate, precise and timely completion of their dissertation. And because students are not always well versed in statistics, dissertation statistics services can be invaluable as dissertation statistics services can provide help every single step of the way.

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Dissertation statistics services are staffed by both expert statisticians and by individuals who have already earned their doctorate. Thus, the people who provide dissertations statistics services know what needs to be done in order for a student to successfully complete his or her dissertation. Dissertation statistics services should be sought at the onset of the project or dissertation, as dissertation statistics services can help students from the very beginning to the very end of the project. Of course, it is never too late to hire the help of dissertation statistics services—but much time can be saved if dissertation statistics services are obtained in the very beginning of the dissertation.

The dissertation itself must provide new and useful research on the subject that the student chooses. In other words, the dissertation must be unique and original. Thus, much time needs to be devoted to researching the topic and subject of the dissertation. And though dissertation statistics services provide invaluable help in the statistics part of the dissertation, dissertation statistics services can also help students do this research. This can save students much time and energy as the dissertation statistics services will make the process of doing the research necessary much more efficient.

Once the research is done and the topic is chosen, dissertation statistics services get to work on the actual dissertation. The first step, of course, is to gather the proper data. This collection of data can be very time consuming without the help of dissertation statistics services. This is true because without the help of dissertation statistics services, many students do not know where to begin in terms of collecting data. Of course, the collection of data is essential in the process of statistics, because if data is not collected properly, the results will be inaccurate. In other words, if certain rules and procedures are not followed in the collection of data, the data will be skewed. An example of this is the sample size in data collection. There are many rules governing how many people need to be surveyed, questioned, etc. and there are many rules about the populations that are surveyed and questioned. Proper sample size justification is essential to the process of gathering data and without the help of dissertation statistics services, students can falter and stumble over the sample size needed. Because all research is different, different research requires different sample sizes. Again, dissertation statistics services are experts in statistics, and therefore are experts in the collection of data and they can guide students so they do not make the mistake of sampling the wrong size population. Obviously this can save valuable time as students who do not have the help of dissertation statistics services oftentimes sample an incorrect number of respondents from incorrect populations and must then start all over in terms of their gathering of data and information. And nothing could be more frustrating than gathering all of the data and information only to need to do it again!

Once the data is collected, dissertation statistics services can help interpret the results. This too can be very challenging for the student not well versed in statistics. Because statistics is a science, there are rules guiding every single part of statistics. The people providing dissertation statistics services know all of these rules, however, and they can help students interpret and analyze all of the results. They know, for example, what to do with outliers in the data, how to accumulate the data, how to chart the data in proper APA format, how to apply the data to the dissertation, etc. Dissertation statistics services, then, can perform all necessary tasks in regards to the interpretation of the statistics of the research.

With the statistics of the research analyzed and interpreted, the student can then go on to complete the dissertation. Some dissertation statistics services will proofread the entire document. What’s more, some dissertation statistics services will even prepare the student for the oral defense of the dissertation. This is crucial as the student must be able to verbally explain every aspect of the dissertation. The right dissertation statistics services firm will therefore guarantee success for a student, because with their help, a student will be able to properly complete the dissertation and defend it!