Author: Kanda Data
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 Perform Multiple Linear Regression Analysis in Excel: Data Analysis Tools
Multiple linear regression analysis is a method used when a researcher aims to estimate the effect of independent variables on a dependent variable. In multiple linear regression, the number of independent variables must be at least two.
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.
Differences in Assumptions of Normality, Heteroscedasticity, and Multicollinearity in Linear Regression Analysis
If you analyze research data using linear regression, it is crucial to understand the required assumptions. Understanding these assumption tests is essential to ensure consistent and unbiased analysis results.
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.
How to Analyze Multicollinearity in Linear Regression Using R Studio
In linear regression analysis using the Ordinary Least Square method, it is necessary to ensure that there is no strong correlation between independent variables. To obtain the best linear unbiased estimator, there must not be a strong correlation between the independent variables.