The essay will discuss the HSC3103 correlation and regression analysis, the t-test and ANOVA analysis, and their application; mainly, the use of correlation analysis occurs when quantifying the association between two continuous variables. An example is with the dependent and independent variables or between two independent variables. Primarily, regression analysis involves assessing the relationship between the outcome variable and one or more variables. The dependent variable is the outcome, and the independent variable becomes the independent variable. Moreover, “x” represents the independent variable, and “y” is the dependent variable. The essay will help understand the application of the correlation and regression analysis and t-test and ANOVA analysis.
correlation and regression analysis
T-TEST AND ANOVA ANALYSIS AND STATISTICAL TESTS APPLICATION
The essay provides the t-test and ANOVA analysis and highlights the typical statistical test applications. Notably, the t-test and ANOVA help in examining whether a group means differ from one another. Moreover, the T-test compares two groups, while ANOVA can handle more than two groups. There are three assumptions to the t-test and ANOVA: independence assumption, normality assumption, and equal variance assumption. Primarily, the independence assumption involves elements of a sample having no relationship to the other example. A similar variance sample includes equal population variances for two groups. Thus, the assignmnet will provide standard statistical application tools through a correlation and regression analysis and t-test and ANOVA analysis.
t-test and ANOVA analysis and statistical tests application
HSC3103 CORRELATION AND REGRESSION ANALYSIS; NONPARAMETRIC TESTS AND APPLICATION OF NONPARAMETRIC TESTS
Nonparametric tests are statistical analysis methods that do not require distribution in meeting assumptions needed for analysis. Therefore, they get commonly referred to as distribution-free tests. The nonparametric tests function as an alternative for the T-test or ANOVA, which gets employed if available data satisfies specific criteria. Notably, the nonparametric tests are not substitutes for parametric tests. Thus, data meeting required assumptions for parametric tests performance will require applying the relevant parametric tests. Moreover, data failing to meet essential assumptions will still get to have parametric tests performed. Lastly, the essay highlights correlation and regression analysis and t-test and ANOVA analysis and their statistical application.
Nonparametric tests and application of nonparametric tests
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