Tag: statistics
How to Calculate the Variance Inflation Factor (VIF) in a Multicollinearity Test for Regression
In linear regression analysis, to obtain the best linear unbiased estimator, you need to perform a series of assumption tests. One of the assumption tests required in linear regression is the multicollinearity test.
How to Determine Alpha Values of 5% and 1% in Hypothesis Testing
If you are conducting research, you certainly have a hypothesis for your study. Hypothesis testing is crucial in research, especially if you’re performing inferential statistical analysis. In statistical hypothesis testing, you are often faced with the choice of using an alpha value of 5% or 1% for your study.
The Impact of Residual Variance on P-Value in Regression Analysis
When conducting linear regression analysis on your research data, you naturally hope that some independent variables significantly affect the dependent variable. Achieving this indicates that you’ve successfully selected independent variables that are presumed to influence the dependent variable.
Differences in the Formulas for Calculating Standard Deviation for Sample Data and Population Data
Standard deviation is a crucial measure in explaining how data is distributed relative to its mean. Generally, when conducting research and performing descriptive statistical analysis, the value of the standard deviation will often appear.
How to Find the Standard Deviation of Sample Data in Excel
One of the metrics in statistics is the standard deviation. When conducting research, the standard deviation is crucial for describing the data you have obtained. It illustrates the dispersion of the data relative to the mean.
Multicollinearity Test in R Studio for Multiple Linear Regression Using Time Series Data
In time series data analyzed using multiple linear regression with the ordinary least squares (OLS) method, it is also necessary to test for multicollinearity. The multicollinearity test is one of the assumption tests to ensure the best linear unbiased estimator.
How to Analyze Heteroskedasticity for Time Series Data in Multiple Linear Regression and Its Interpretation
The heteroskedasticity test is one of the assumption tests in the Ordinary Least Squares (OLS) linear regression method, aimed at ensuring that the residual variance remains constant. If the multiple linear regression equation being tested shows non-constant residual variance, this is referred to as heteroskedasticity.
Tutorial on R Studio: Testing Residual Normality in Multiple Linear Regression for Time Series Data
The normality test in multiple linear regression analysis is aimed at detecting whether the residuals are normally distributed. In research using time series data, it is also necessary to perform a normality test to ensure that the required assumptions are met.