# Hypothesis testing

## Assumptions of Multiple Linear Regression on Cross-Section Data

Multiple linear regression is a statistical technique used to predict the value of a dependent variable based on several independent variables. This regression provides a way to understand and measure the influence of independent variables on the dependent variable.

## Assumption of Residual Normality in Regression Analysis

The assumption of residual normality in regression analysis is a crucial foundation that must be met to ensure the attainment of the Best Linear Unbiased Estimator (BLUE). However, often, many researchers face difficulties in understanding this concept thoroughly.

## Differences Between Paired Sample T-Test, Independent Sample T-Test, and One-Way ANOVA

Differential testing is aimed at determining the mean differences in the tested sample groups. In practice, paired sample t-test, independent sample t-test, and one-way ANOVA are often used to test means in more than one sample group.

## Hypothesis Testing: Unveiling Insights in Multiple Linear Regression Analysis

In inferential statistics, we need to formulate research hypotheses. These research hypotheses are formulated according to the research objectives. Furthermore, statistical hypotheses need to be established in the analysis method, consisting of null and alternative hypotheses.

## Things to consider if none of the variables has a significant effect (null hypothesis accepted)

For researchers, obtaining statistically significant results is the desired outcome. In a research proposal, researchers write the background and research problem. Futhermore, based on the research problem, the research objectives are formulated.