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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Course Content

  1. Understanding of Data Science
    7m 33s
  2. How to use LearnVern
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  1. Introduction to Data Measurement in Data Science
    10m 6s
  2. Types of Variables in Data Science
    2m 26s
  3. Levels of Data Measurement in Data Science
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  1. Charts and Graphs in Data Science
    4m 2s
  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
    7m 42s
  7. Cross Tabulation in Data Science
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  1. Measures of Central Tendency and Variability in Data Science
    6m 51s
  2. Mean and Percentiles in Data Science
    5m 32s
  3. Quartiles in Data Science
    2m 38s
  4. Measures of Variability in Data Science
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  5. Variance and Standard Deviation in Data Science
    7m 30s
  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
    5m 25s
  2. Kurtosis in Data Science
    3m 48s
  3. Box and Whisker Plots in Data Science
    3m 45s

  1. Hypothesis Testing in Data Science
    6m 33s
  2. Statistical Hypothesis in Data Science
    6m 30s
  3. Errors in Statistical Hypothesis in Data Science
    5m 45s
  4. Statistical Inference for Single Population in Data Science
    7m 11s
  5. Testing Example in Data Science
    5m 27s
  6. Statistical Inference with Two Population in Data Science
    8m 33s

  1. R Programming in Data Science
    5m 5s
  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
    7m 50s
  3. Two way ANOVA in Data Science
    7m 48s
  4. Summary of ANOVA in Data Science
    3m 11s
  5. ANOVA example in Data Science
    10m 50s
  6. One way ANOVA Example in Data Science
    4m 7s
  7. Two way ANOVA Example in Data Science
    9m 24s

  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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  2. Least Square and Residual Analysis in Data Science
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  3. Test assumption of Regression Model 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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  6. Residual Analaysis of Model example 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
    6m 38s

  1. Understanding Clustering in Data Science
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  2. Hierarchical Clustering in Data Science
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  3. Example of Hierarchial Clustering in Data Science
    13m 39s
  4. K Means Clustering in Data Science
    8m 6s
  5. Example of K Means Clustering in Data Science
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  1. Theoretical Concepts of Dashboard
    5m 41s
  2. Creating Pivots and Preparing Charts
    5m 36s
  3. Slicers in Dashboard
    3m 27s
  4. Preparing Final Dashboard
    8m 20s
  5. Presenting Dashboard
    3m 2s
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