Tag: Kanda data
How does high variance affect hypothesis testing in linear regression?
Will a high variance value affect the statistical test on linear regression? Many questions related to this topic were Kanda Data obtained. On this occasion, Kanda Data will discuss the impact of the variance value on hypothesis testing on linear regression.
How to Test Linearity Assumption in Linear Regression using Scatter Plot
The linearity test is one of the assumption tests in linear regression using the ordinary least square (OLS) method. The objective of the linearity test is to determine whether the distribution of the data of the dependent variable and the independent variable forms a linear line pattern or not?
Multicollinearity Test and Interpreting the Output in Linear Regression
One of the assumptions in linear regression using the ordinary least square (OLS) method is that there is no strong correlation between independent variables. To get the Best Linear Unbiased Estimator in linear regression with ≥ 2 independent variables, you must be fulfilled the non-multicollinearity assumption.
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.