linear-regression

Learning objectives: Explain the role of linear regression and logistic regression in prediction. Understand how to encode categorical variables. Questions: 23.6.1. Darlene is a risk analyst who evaluates the creditworthiness of loan applicants at her financial institution. Her department is...

Learning objectives: Explain the role of linear regression and logistic regression in prediction. Understand how to encode categorical variables. Questions: 23.5.1. Clara fits a logistic regression model to her training data that predicts whether a financial transaction is fraudulent. Binary...

Learning objectives: Construct, apply, and interpret hypothesis tests and confidence intervals for a single regression coefficient in a regression. Explain the steps needed to perform a hypothesis test in a linear regression. Describe the relationship between a t-statistic, its p-value, and a...

Learning objectives: Describe the models which can be estimated using linear regression and differentiate them from those which cannot. Interpret the results of an ordinary least squares (OLS) regression with a single explanatory variable. Describe the key assumptions of OLS parameter...

The ordinary least squares (OLS) regression coefficients are determined by the "best fit" line that minimizes the sum of squared residuals (SSR). David's XLS: https://trtl.bz/2uiivIm

In theory, there is one population (and one population regression function). Each sample varies and generates its own sample regression function (SRF). Therefore, the regression coefficients generated by the SRF are random variables; e.g., their standard deviations are the standard errors...

Hi, I understand that the assumption that the sampling distribution of OLS estimators b0 and b1 is asymptotically normal is a key property. However I'm a bit stuck as to why that is. I assume the magic CLT comes into play here, but I guess there are stil grey areas for me. When we apply the...