# simple linear regression

## Understanding the Differences in Using R Squared and Adjusted R Squared in Research

When you choose to use linear regression analysis, itâ€™s essential to master and understand the interpretation of the coefficient of determination. The coefficient of determination is one of the key indicators in linear regression analysis that can be used as a metric to determine the goodness of fit of a regression model.

## Calculating Predicted Y and Residual Values in Simple Linear Regression

Residual values in linear regression analysis need to be calculated for several purposes. In linear regression using the ordinary least squares method, one of the assumptions that must be met is that residuals must be normally distributed, hence the necessity to first calculate residual values. However, before calculating the residual values, we need to first …

## Calculation Formula for the Coefficient of Determination (R Square) in Simple Linear Regression

The coefficient of determination plays a crucial role in regression analysis. It is not surprising that various studies using regression analysis often present the value of the coefficient of determination. Recognizing the importance of this value, Kanda Data will discuss this topic in detail.

## Tutorial on How to Calculate Residual Values in Excel

Regression analysis is already widely used by researchers to explore the influence of independent variables on dependent variables. If we use regression analysis, we must have a good understanding of residual values. These residual values are needed in regression analysis. In addition, in the assumption tests required in linear regression analysis using the ordinary least …

## How to Conduct a Normality Test in Simple Linear Regression Analysis Using R Studio and How to Interpret the Results

The Ordinary Least Squares (OLS) method in simple linear regression analysis is a statistical technique aimed at understanding the influence of an independent variable on a dependent variable. In simple linear regression, there is only one dependent variable and one independent variable.