Shaprio-Wilk Test | Levene Test | Graphical inspection | Statistical inference | Hypothesis Testing
ฝัง
- เผยแพร่เมื่อ 12 ก.ย. 2024
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In this video, we introduce two essential statistical tests - the Shapiro-Wilk test and the Levene test - that are widely used to check the assumptions of normality and homogeneity of variances in all popular Hypothesis Tests.
We explore the concepts of normality and homogeneity of variances and explain why it is crucial to assess these assumptions before drawing meaningful conclusions from data.
Next, we dive into the practical aspects of assessing normality using graphical inspection methods. We discuss the importance of QQ plots and density plots, showcasing how they offer visual insights into the distribution of data. We demonstrate how to interpret these plots and discuss the implications for normality.
Moving forward, we introduce the Shapiro-Wilk test, a statistical test specifically designed to assess normality. We explain the test's working principles and its p-value, which indicates the strength of evidence against the null hypothesis of normality. We discuss the interpretation of p-values and their role in decision-making.
Additionally, we explore the Levene test, which focuses on evaluating the homogeneity of variances across groups or samples. We explain the underlying concepts and the test's use in determining whether the variances of different groups are statistically different. We emphasize the importance of homogeneity of variances in ensuring accurate statistical analyses.
To solidify our understanding, we generate two random samples using the numpy.random.normal function and apply both the Shapiro-Wilk and Levene tests to these datasets. We demonstrate how to interpret the test results and explain the implications for the assumptions of normality and homogeneity of variances.
Throughout the video, we employ clear visualizations, including QQ plots, density plots, and test result summaries, to enhance comprehension and provide a visual learning experience.
Happy Learning!
