Analysis of variance (ANOVA) is a technique that is used to examine the differences among the means for two or more populations. In analysis of variance (ANOVA), the null hypothesis is always assumed as the fact that there is no significant difference in the means of the populations being examined.
Statistics Solutions is the country's leader in analysis of variance and statistical consulting. Contact Statistics Solutions today for a free 30-minute consultation.
Analysis of variance (ANOVA) should always have an interval or a ratio scaled dependent variable and one or more categorical independent variables. In analysis of variance (ANOVA), the categorical independent variables are generally called factors. A particular combination of factor levels or categories is often designated as treatments in analysis of variance (ANOVA).
An analysis of variance (ANOVA) technique consists of only one categorical independent variable. This single factor is used in a technique that is called one way analysis of variance (ANOVA). On the other hand, if an analysis of variance (ANOVA) technique consists of two or more than two categorical independent variables or factors, then that technique is called n way analysis of variance (ANOVA). Here, the term ‘n’ denotes the number of factors in analysis of variance (ANOVA).
Thus, there are two techniques of analysis of variance (ANOVA). The technique called one way analysis of variance (ANOVA) can be used to understand the variation in the brand evaluation exposed to different types of commercials. One way analysis of variance (ANOVA) can also be used to understand the difference of attitudes among the retailers, wholesalers and agents towards the distribution policy of a particular firm.
So, in general, the technique of one way analysis of variance (ANOVA) is a useful technique to test the similarity of means at one time by usage of their respective variances. It is for this reason that analysis of variance (ANOVA) has its name.
The F test statistic used in analysis of variance (ANOVA) is nothing but the ratio of the sample variances. This test in analysis of variance (ANOVA) is basically done to test the statistical significance of the variability of the components. In other words, we can say that this test is a measure of variance that is used in analysis of variance (ANOVA).
The n-way of analysis of variance (ANOVA) can be used in understanding the variation in the consumer’s intentions to buy a particular brand of product with respect to different levels of price and different levels of distribution. In the field of psychology, the n-way of analysis of variance (ANOVA) can be used in understanding the affect of the consumption of a particular brand in terms of the educational level of a person and the age of that person. This technique of analysis of variance (ANOVA) also helps in understanding the interaction in the levels of advertisement and the price level of the brand.
There are major assumptions that the researcher must follow while conducting analysis of variance (ANOVA). In analysis of variance (ANOVA), the sample drawn from the population must be independent of each other. The sample drawn from the population is assumed to be a normal population in analysis of variance (ANOVA). The variances in analysis of variance (ANOVA) should always be homogeneous in nature.
The following are the steps involved in conducting analysis of variance (ANOVA):
The first and foremost step in analysis of variance (ANOVA) is to identify the dependent and the independent variables. The next step is to disintegrate the total variation in analysis of variance (ANOVA). The third step involves the measurement of the effects while conducting analysis of variance (ANOVA). The fourth step is to test the significance in the analysis of variance (ANOVA). And the last step is to interpret the results obtained after the analysis of variance (ANOVA).
