Course Content

Course Content


  • Normality refers to the fact that each sample is drawn from a population that is regularly distributed.
  • Sample independence refers to the fact that each sample was drawn independently of the others.
  • Variation equality refers to the fact that the variance of data across groups should be the same.

(1) interval data of the dependent variable, (2) normality, (3) homoscedasticity, and (4) no multicollinearity are all assumptions that must be met in the factorial ANOVA.

The analysis of variance (ANOVA) is a statistical method that divides a data set's observed aggregate variability into two parts: systematic components and random factors. Random factors have no statistical impact on the supplied data set, whereas systematic influences do.

The samples must come from populations that are regularly distributed. The sampling is carried out accurately. Independent observations must be made within and between groups. Variations between populations must be equal (homoscedastic).

The one-way ANOVA is regarded as a reliable test for the assumption of normality. This means it tolerates deviations from its normalcy assumption fairly well.

Recommended Courses

Share With Friend

Have a friend to whom you would want to share this course?

Download LearnVern App

App Preview Image
App QR Code Image
Code Scan or Download the app
Google Play Store
Apple App Store
598K+ Downloads
App Download Section Circle 1
4.57 Avg. Ratings
App Download Section Circle 2
15K+ Reviews
App Download Section Circle 3
  • Learn anywhere on the go
  • Get regular updates about your enrolled or new courses
  • Share content with your friends
  • Evaluate your progress through practice tests
  • No internet connection needed
  • Enroll for the webinar and join at the time of the webinar from anywhere