Data Science Course > Analysis of Variance

Two way ANOVA in Data Science

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The dependent variable fluctuates depending on the magnitude of the independent variable, according to ANOVA. Consider the following scenario: Social media use is your independent variable, and you allocate groups to low, medium, or high levels of social media use to see if there is a difference in the number of hours of sleep per night.

The assumptions of a two-way ANOVA are:

  • Normality assumption: The distribution of the population must be normal, which means that it is symmetrical and bell-shaped. This assumption is often violated in many real-life situations like when the sample size is small. In such cases, transformations can be done to make the distribution normal again.
  • Homogeneity assumption: The variances for both groups must be equal. If this assumption does not hold true, then we can perform an F test to see whether there is a significant difference between the two groups.

The number of independent variables is the only variation between one-way and two-way ANOVA. One independent variable is used in a one-way ANOVA, whereas two are used in a two-way ANOVA.

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  3. Errors in Statistical Hypothesis in Data Science
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  5. Testing Example in Data Science
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  6. Statistical Inference with Two Population in Data Science
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  1. Analysis of Variance and Annova in Data Science
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  2. Assumption and Variation in One-Way ANOVA in Data Science
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  3. Two way ANOVA in Data Science
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  4. Summary of ANOVA in Data Science
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