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ANOVA table

ANOVA Table Exam Help

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ANOVA Table

ANOVA, short form for “Analysis of Variance”, is a statistical way of analyzing the degree of differences between a minimum of two groups in an experiment. And the tabular representations of the ANOVA test results are known as the ANOVA table. The prime usage of the ANOVA table is to test the hypotheses 0f population means. Also, there can either be one way or two way ANOVA table.

To learn How to fill out an ANOVA table you need to understand the significance of both rows and columns which are used to construct an ANOVA table. Below are the various column headings which are used in the ANOVA table:

  1. Source: It represents the sources that are responsible for any variation in the given data.
  2. P: Stands for P-value.
  3. F: Stands for F-statistic.
  4. SS: Represents the sum of the squares of the data.
  5. MS: Represents the mean sum of the squares of the data.
  6. DF: Stands for the degree of freedom of the data.

Similarly, to learn How to fill ANOVA table, various row headings are used in the ANOVA table and we can see them below:

  1. Total: It represents the total deviation of the data occurred from the grand mean.
  2. Error: It’s kept to explain to random inexplicable errors or variabilities present within the groups.

Factor: It signifies the variabilities that come from the factor of interest.

How to interpret ANOVA table?

ANOVA table can be interpreted by the significance of “P-value”.

Case 1: From any ANOVA table, if the obtained “P-value” is either less than or equivalent to the level of significance, it discards the null hypotheses. This means that all the population’s means are not equal.

Case 2: From any ANOVA table, if the obtained “P-value” is greater than the level of significance, the null hypotheses get accepted. This means that all the population’s means are equal.

How to fill ANOVA table can be done both by hands or usage of a software application. To learn more about statistics and probability check our other related topics in Statistics Explained.

 

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