In this Spotle masterclass you will learn:

• The concepts of statistical estimation, test of hypothesis, normality test, ANOVA, contingency table Chi square test explained through detailed videos

• To conduct statistical tests with real-life examples

• All concepts through exercises, solved problems

Your take-aways:

A solid foundation in statistical decision making to build a data science career.

ANOVA or Analysis of Variance is a group of statistical models to test if there exists a significant difference between means. It tests whether the means of various groups are equal or not. In ANOVA, the variance observed in a variable is partitioned into different components based on the sources of variation. An important fact to note is that while we use ANOVA to find out whether the means differ significantly, we compare the variances, hence the name – Analysis of Variance.

While studying test of hypothesis, we have seen how we can compare two means from two population. When we compare two or more than two means, we use ANOVA.

ANOVA is easy to compute and can be manually computed using simple algebra rather than complex matrix calculations. This was one of the reasons for its early popularity.

Following are the examples where ANOVA can be used.

A group of psychiatric patients are trying three different therapies: Counselling, medication and biofeedback. You want to see if one therapy is better than the others.

A manufacturer has two different processes to make light bulbs. They want to know if one process is better than the other.

Students from different colleges take the same exam. You want to see if one college outperforms the other.