Data Science Course > Analysis of Variance

Two way ANOVA in Data Science

71k

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.

Learner's Ratings

4.4

Overall Rating

  • 73%
  • 16%
  • 6%
  • 4%
  • 1%

Reviews

G
Golu kumar Kumar
5

love you to

S
Suman Bhattacharya
4

It's good to understand

V
vaibhav
5

best

A
Anjali
5

Excellent explanation

A
ashif mahiboob yadwad
5

Very good

S
SHAIKH TOHEED
5

good but practical knowledge not perfect

P
PUSHPAK DILLIP MALI
4

The content and teaching style is excellent. But there is slight issue is that video quality is kind of poor the dashboard is blur on 720mp on PC.

S
Sayali Jadhav
4

Very useful all data

S
Syed Mohammad Qaiser Rizvi
5

Way of teaching is very good. I want to get PPT which he is using to teach us ?

A
Anuja Bagadi
5

It is a very interactive and useful course..

Show More
Recommended Courses
Course Content

  1. Understanding of Data Science
    7m 33s
  2. How to use LearnVern
    3m 20s

  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
    10m 25s

  1. Charts and Graphs in Data Science
    4m 2s
  2. Frequency Distribution in Data Science
    5m 46s
  3. Bar Graphs and Pie Charts in Data Science
    9m 1s
  4. Summarizing Quantitative Data in Data Science
    7m 25s
  5. Cummulative Distribution in Data science
    8m 4s
  6. Histogram and Scatter Plot in Data Science
    7m 42s
  7. Cross Tabulation in Data Science
    6m 13s

  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
    4m 56s
  5. Variance and Standard Deviation in Data Science
    7m 30s
  6. Application of Standard Deviation in Data Science
    5m 40s
  7. Z-score and Co-efficient of Variation in Data Science
    5m 23s
  8. Measures of Central Tendency for Grouped Data in Data Science
    3m 59s
  9. Standard Deviation and Variance for Grouped Data in Data Science
    11m 21s

  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
    3m 51s
  3. Understanding R Console and R Studio in Data Science
    7m 14s

  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
    7m 33s

  1. Introduction to Simple Linear Regression and Correlation in Data Science
    7m 28s
  2. Least Square and Residual Analysis in Data Science
    7m 15s
  3. Test assumption of Regression Model in Data Science
    7m 3s
  4. Hypothesis and Co-Efficient of Determination in Data Science
    4m 9s
  5. Regression Example - Creating Model in Data Science
    12m 1s
  6. Residual Analaysis of Model example in Data Science
    9m 7s
  7. Residual Analysis of Model Part 2 in Data Science
    8m 10s
  8. Residual Analysis of Model Part 3 in Data Science
    6m 38s

  1. Understanding Clustering in Data Science
    5m 58s
  2. Hierarchical Clustering in Data Science
    8m 33s
  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
    9m 41s

  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
Enroll For Free