May 28, 2026 1:30 am
MS Excel allows us to run linear regressions pretty easily.Here are the steps to add the 'Data Analysis' toolpack for the version of excel you are using. First go to 'Files' and then select 'Options'.This will open a window similar to the below screenshot. Select...
R sq statistic provides a measure of goodness of fit of the estimated regression model to sample data. It helps to understand the relevance of chosen explanatory variables in the estimated model.The value of R sq necessarily lies between 0 and 1. When the value of R...
The difference between 0.051 and 0.049 is 0.002 and that is the exact number which determines if the result of a scientific study is going to be accepted or rejected.To put it in context, p values of less than 5% are widely used as an ultimate decision making tool for...
For the estimated coefficients of an econometric model to be most accurate and reliable, there are few properties we need to look for.The following describes the same. Unbiasedness : An estimator is said to be an unbiased estimator if its mean or expected value is...
In regression models, dummy variables are used to represent categorical data by assigning binary values (0 or 1). They help quantify qualitative information about dependent variables. When dealing with categories, n-1 dummy variables prevent multicollinearity. These variables are essential for accurately modeling and interpreting economic relationships.
Any simple linear regression model can be presented in the form of a simplified equation as shown below Yt =α + β*Xt + εt Here the εt is the called the residual which is not explained by the model.More conventionally this is known as the error term.This basically...
Multicollinearity occurs when explanatory variables in regression are interrelated, making it difficult to assess their individual effects on the dependent variable. It leads to inaccurate coefficient estimates. Detect multicollinearity using Variance Inflation Factor (VIF). Solutions include transforming variables, increasing sample size, or using Principal Component Analysis (PCA) to combine correlated variables.