Author: Kanda Data
How to Analyze Multicollinearity in Linear Regression and its Interpretation in R (Part 2)
Non-multicollinearity is one of the assumptions required in the ordinary least square (OLS) method of linear regression analysis. Non-multicollinearity assumption implies that there is no strong correlation among the independent variables in the equation.
How to Analyze Multiple Linear Regression and Interpretation in R (Part 1)
Multiple linear regression analysis has been widely used by researchers to analyze the influence of independent variables on dependent variables. There are many tools that researchers can use to analyze multiple linear regression.
How to Transform Natural Logarithm (ln) and Reverse (anti-Ln) in Excel
Researchers often transform data to convert original data into another form to meet certain assumptions. Researchers can do several forms of data transformation. Natural logarithm transformation is a form of transformation frequently used by researchers in data analysis.
Step-by-Step Tutorial: Finding Predicted and Residual Values in Linear Regression with Excel
In linear regression analysis, residual values play an important role in supporting the main analysis. Residual values are the difference between actual values and predicted values. In the assumption testing of linear regression using the OLS method, residual values are needed for the testing of assumptions.
Sampling Methods and Statistical Analysis in Survey Research
In conducting research, the primary objective is to analyze a phenomenon and find effective solutions to problems. The process of research involves collecting facts, evidence or results to develop, test or enhance knowledge about natural and social phenomena. Research plays a significant role in advancing science, and it is essential that research findings are made accessible to all.
How to Interpret Negative Coefficient Estimations in Linear Regression?
The ordinary least squares (OLS) method is commonly employed in linear regression analysis to establish the relationship between the independent and dependent variables. Despite its numerous advantages, researchers must meet certain requirements to use this method.
How to Perform Linear Regression using Data Analysis in Excel
Researchers have widely used linear regression analysis to analyze the effect of a variable on other variables. Linear regression analysis consists of the dependent variable and the independent variable. The difference between the two is that the dependent variable is the affected variable, while the independent variable is the influencing variable.
How to use data analysis for sampling in Excel
In research activities, researchers can take samples from the observed population. The purpose of sampling is motivated by time and cost limitations if observations are made on the entire population.