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.
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.
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.
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.
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.
