Author: Kanda Data
Heteroscedasticity Test and How to Interpret the Output in Linear Regression
The objective of the heteroscedasticity test is to determine whether the variance of residuals is constant. One of the assumption tests in linear regression using the ordinary least square (OLS) method is that the variance of residuals is constant.
How to Test the Normality Assumption in Linear Regression and Interpreting the Output
The normality test is one of the assumption tests in linear regression using the ordinary least square (OLS) method. The normality test is intended to determine whether the residuals are normally distributed or not.
Correlation Test for Ratio Scale Variables
Correlation analysis is an associative test to determine the relationship between variables. Correlation tests for parametric variables and non-parametric variables are different. Therefore you need to understand how to choose a correlation test according to statistical rules.
How to Write and Test Statistical Hypotheses in Simple Linear Regression
We need to develop hypotheses when conducting research. A hypothesis is a provisional assumption or statement of the research. The hypothesis needs to be proven, whether true or false, through the research process.
How to Reverse Natural Logarithm to Initial Data in Excel
Researchers often use the natural logarithm form (Ln) transformation. In the previous article, I have written about How to Transform Data into Natural Logarithm (Ln) in Excel.
How to Transform Data into Natural Logarithm (Ln) in Excel
You may have seen research data transformed into other forms in analyzing research data. The data transformation is determined by changing the measurement scale of the original data into another form.
Create Residual and Y Predicted in Excel
Residual and predicted Y have an important role in linear regression analysis using the Ordinary Least Square (OLS) method. Several OLS linear regression method assumptions were conducted to test the residuals, including normally distributed residuals and constant residual variance.
Multiple Linear Regression Analysis and Interpreting the Output in Excel
We can use multiple linear regression analysis to estimate the effect of the independent variable on the dependent variable. Multiple linear regression using at least two independent variables.