Dummy Variables and Truncated variables, Diagnostic Checking
by Dummy Variables: Dummy variables, also known as indicator variables or binary variables, are used in regression analysis to represent categorical data in a quantitative way. …
Tag: PRESIDENT
Heteroskedasticity Nature and Cause, Detection of Heteroskedasticity, Consequences of Heteroskedasticity, Solution to heteroskedasticity problem
Heteroskedasticity is a common issue in regression analysis that occurs when the variability of the errors (residuals) in a regression model is not constant across …
Multicollinearity Nature and Causes, Estimation in presence of perfect and imperfect multicollinearity problems with measuring multicollinearity, Solution to multicollinearity problem
Multicollinearity is a phenomenon that occurs when two or more independent variables in a regression model are highly correlated with each other. It can lead …
Serial Correlation Consequences, Detection and Remedies
Serial correlation, also known as autocorrelation, occurs when there is a correlation between the error terms (Residuals) of a time series or panel data regression …
Functional forms of Regression models: Qualitative (dummy) independent variables Misspecification; Model selection (criteria)
Functional forms of Regression models Functional forms of regression models refer to the mathematical representation or structure of the relationship between the dependent variable and …
Goodness of fit – R2 and adjusted R2: Partial regression coefficients
Goodness of Fit – R-squared (R²) and Adjusted R-squared: In regression analysis, R-squared (R²) and adjusted R-squared are two important measures of goodness of fit …
Multivariate regression, Multiple Linear Regression Model, Estimation of parameters, Properties of OLS estimators
Multivariate regression, also known as multiple regression, is a statistical technique used to model the relationship between a dependent variable and two or more independent …
Testing hypotheses Individual and Joint
In regression analysis, hypothesis testing can be conducted to assess the significance of individual regression coefficients (parameters) and the joint significance of multiple coefficients. Hypothesis …
Gauss-Markov theorem
The Gauss-Markov theorem, also known as the Gauss-Markov theorem of linear regression, is a fundamental result in econometrics and statistics. It establishes the conditions under …
Interval estimation and Hypothesis testing
Interval Estimation: Interval estimation is a statistical technique used to estimate population parameters by providing an interval (or range) of values within which the true …