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<p align="justify"><strong>Note:</strong> Reject the null hypothesis if the sig value corresponding to F-statistic is less than 0.05.<br />
Rejection of the null hypothesis in ANOVA only tells us that all population means are not equal. But if we are interested to know the difference between (group1 and group2) or (group 2 and group 3) then we will also apply Post-Hoc multiple comparison.<br />
<strong>Procedure:</strong> One-Way ANOVA can be applied by using Univariate GLM which is used to assess the effects of several independent variables on a single dependent variable.</p>
<h3>Two-Way ANOVA</h3>
<p align="justify">This is used to know the effects of two independent variables on the same dependent variable. For example Educational background (factor A having two categories Arts and Science) and experience (Factor B having two categories of low and high) affect the salary level.<br />
Hypothesis</p>
<p align="justify">1. There is no difference in the means of factor A</p>
<p align="justify">2. There is no difference in means of factor B</p>
<p align="justify">3. There is no interaction between factors A and B</p>
<p align="justify"><strong>Note:</strong> Reject the null hypothesis if P value corresponding to F statistic is less than 0.05 at 5% confidence interval.</p>
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<h3>Difference between One-Way and Two-Way ANNOVA</h3>
<p align="justify">The difference is that where one-way ANOVA only generates one F-value, two-way ANOVA generates three F-values: one to test the main effects of each factor, and a third to test the interaction effect (i.e., the combined effect of the two factors). Factorial ANOVA yields the same information that two one-way ANOVA&#8217;s would, but it does so in one analysis</p>
<p align="justify">
<p align="justify"><strong>Written By</strong></p>
<p align="justify"><strong>Syed Asif Shah</strong>
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