Tag: Kanda data
Understanding the Difference between Parametric and Non-Parametric Statistics
The selection of data analysis methods is an important step that researchers must prepare. The correct analytical method will obtain the proper conclusion.
How to Create and Analyze Variables using a Likert Scale
In statistics, research variables can be measured in parametric and non-parametric variables. Variables measured by interval scale and ratio scale are grouped in parametric variables.
How to Find Variance and Standard Deviation in Excel
Variance and standard deviation are components of statistical values that are most often found in descriptive analysis. When we perform statistical software analysis, we will find the value of variance and standard deviation if we enable descriptive statistics.
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.