Tag: Data Analysis
How to Automatically Display Residual Values in Regression Analysis Using Excel
Residual values play an important role in linear regression analysis. These residuals are used for OLS assumption tests, such as normality tests and heteroskedasticity tests. For instance, one of the key assumptions in linear regression analysis is that the residuals are normally distributed.
Spearman Rank Correlation Analysis | Practical Test on the Relationship Between Education Level and Performance Level
In statistics, correlation tests are frequently used by researchers to examine associations. The purpose of conducting a correlation test is to determine the relationship between two observed variables. However, many people still ask me about one specific correlation test for variables measured on an ordinal scale.
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.
How to Perform Residual Normality Analysis in Linear Regression Using R Studio and Interpret the Results
Residual normality testing is a key assumption check in linear regression analysis using the Ordinary Least Squares (OLS) method. One essential requirement of linear regression is that the residuals should follow a normal distribution. In this article, Kanda Data shares a tutorial on how to perform residual normality analysis in linear regression using R Studio, along with steps to interpret the results.
What to Do If the Regression Coefficient Is Negative?
Linear regression is one of the most commonly used statistical analysis techniques to understand the impact of independent variables on a dependent variable. In regression analysis, the estimated coefficients indicate the extent to which each independent variable affects the dependent variable.
Calculation Formula for the Coefficient of Determination (R Square) in Simple Linear Regression
The coefficient of determination plays a crucial role in regression analysis. It is not surprising that various studies using regression analysis often present the value of the coefficient of determination. Recognizing the importance of this value, Kanda Data will discuss this topic in detail.
Simple Linear Regression Analysis Easily Using Excel
Simple linear regression analysis is a useful statistical technique for measuring and understanding the relationship between two variables. In this analysis, one variable (independent variable) is used to predict or explain the other variable (dependent variable).