Category: Assumptions of Linear Regression
Assumptions of Multiple Linear Regression on Time Series Data
Multiple linear regression is a statistical analysis technique used to model the relationship between one dependent variable and two or more independent variables. The multiple linear regression model is used to predict the value of the dependent variable based on the estimated values of the independent variables.
When is autocorrelation testing performed in linear regression analysis?
In regression analysis, researchers must ensure that the constructed model meets several required assumptions. One assumption in ordinary least square linear regression is the absence of autocorrelation in the model’s residuals. Autocorrelation occurs when there is a correlation pattern among the residual values in the regression model.
Understanding the Essence of Assumption Testing in Linear Regression Analysis: Prominent Differences between Cross-Sectional Data and Time Series Data
Linear regression analysis has become one of the primary tools for researchers to explore the influence of independent variables on dependent variables. The Ordinary Least Squares (OLS) method has been a mainstay in conducting this linear regression analysis.
The data that cannot be transformed using natural logarithm (Ln)
In quantitative data analysis, to ensure unbiased and consistent estimations, it’s important to meet several assumptions required in the conducted tests. However, sometimes, the test results may not meet the desired expectations.
Understanding Normality Test in Ordinary Least Squares Linear Regression
Linear regression analysis examines the influence of independent variables on dependent variables. This analysis can take the form of simple linear regression or multiple linear regression. Most linear regression analyses utilize the Ordinary Least Squares (OLS) method.
Comparing Logistic Regression and Ordinary Least Squares Linear Regression: Key Differences Explained
The analysis of Ordinary Least Squares (OLS) linear regression is most commonly used to estimate the influence of independent variables on a dependent variable. In OLS linear regression analysis, several assumptions must be fulfilled to obtain the best linear unbiased estimator.
How to Calculate Durbin Watson Tests in Excel and Interpret the Results
Researchers who use time series data in linear regression analysis with the OLS method need to conduct some of the required assumption tests. One of the assumption tests required in the regression is the autocorrelation test.
How to Analyze and Interpret the Durbin-Watson Test for Autocorrelation
Researchers can use regression analysis to determine the effect of independent variables on the dependent variable. The data used in the regression analysis can use cross-section, time series, and panel data. On this occasion, Kanda Data will discuss regression analysis using time series data.