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  1. Nicole Seaman

    P1.T2.20.17. Hypothesis tests of univariate linear regression model

    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...
  2. Nicole Seaman

    P1.T2.20.16. Linear regression models

    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...
  3. Nicole Seaman

    YouTube T2-15 Linear regression: OLS coefficients minimize the SSR

    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
  4. Nicole Seaman

    YouTube T2-14 Linear regression: Sample Regression Function

    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...
  5. FlorenceCC

    Sampling distribution of OLS estimators

    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...