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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  2. How to use LearnVern
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  1. Introduction to Data Measurement in Data Science
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  2. Types of Variables in Data Science
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  3. Levels of Data Measurement in Data Science
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  1. Charts and Graphs in Data Science
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  2. Frequency Distribution in Data Science
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  3. Bar Graphs and Pie Charts in Data Science
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  4. Summarizing Quantitative Data in Data Science
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  5. Cummulative Distribution in Data science
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  6. Histogram and Scatter Plot in Data Science
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  7. Cross Tabulation in Data Science
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  1. Measures of Central Tendency and Variability in Data Science
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  2. Mean and Percentiles in Data Science
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  3. Quartiles in Data Science
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  4. Measures of Variability in Data Science
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  5. Variance and Standard Deviation in Data Science
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  6. Application of Standard Deviation in Data Science
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  7. Z-score and Co-efficient of Variation in Data Science
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  8. Measures of Central Tendency for Grouped Data in Data Science
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  9. Standard Deviation and Variance for Grouped Data in Data Science
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  1. Skewness in Data Science
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  2. Kurtosis in Data Science
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  3. Box and Whisker Plots in Data Science
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  1. Hypothesis Testing in Data Science
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  2. Statistical Hypothesis in Data Science
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  3. Errors in Statistical Hypothesis in Data Science
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  4. Statistical Inference for Single Population 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. R Programming in Data Science
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  2. Data Types in R in Data Science
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  3. Understanding R Console and R Studio in Data Science
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  1. Analysis of Variance and Annova in Data Science
    3m 55s
  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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  5. ANOVA example in Data Science
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  6. One way ANOVA Example in Data Science
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  7. Two way ANOVA Example in Data Science
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  1. Understanding Datamining in Data Science
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  1. Introduction to Simple Linear Regression and Correlation in Data Science
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  4. Hypothesis and Co-Efficient of Determination in Data Science
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  5. Regression Example - Creating Model in Data Science
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  7. Residual Analysis of Model Part 2 in Data Science
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  8. Residual Analysis of Model Part 3 in Data Science
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  4. K Means Clustering in Data Science
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  5. Example of K Means Clustering in Data Science
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  2. Creating Pivots and Preparing Charts
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  3. Slicers in Dashboard
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