Hypothesis Testing

Hypothesis testing is a statistical method used to determine if there is enough evidence to reject a null hypothesis (H0) in favor of an alternative hypothesis (𝐻𝑎 ). By analyzing sample data, the process involves setting a significance level, calculating a test statistic, and comparing it to a critical value or P-value to make data-driven decisions. Common tests include t-tests, z-tests, and chi-square tests, applied in diverse fields to validate assumptions and drive conclusions.

Multiple Linear Regression

Multiple Linear Regression (MLR) is a statistical technique used to model the relationship between a dependent variable and two or more independent variables. It extends simple linear regression by allowing multiple predictors, making it useful for more complex data where a single variable does not explain the outcome adequately. Formula for Multiple Linear Regression The general formula for a multiple linear regression model is: 𝑌 = 𝛽0 + 𝛽1𝑋1 + 𝛽2𝑋2 + ⋯ +𝛽𝑛𝑋𝑛 + 𝜖 Where:

Simple Linear Regression

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. Its main goal is to find the best-fitting linear equation that can predict the dependent variable based on the independent variables.

ggplot2

ggplot2 is an R package widely used for data visualization. Created by Hadley Wickham, it implements the Grammar of Graphics, which allows users to create complex multi-layered graphics by building them step-by-step. With ggplot2, you can create a wide range of visualizations, from simple scatter plots and bar charts to complex faceted plots.