Estimating parameters using Maximum Likelihood
optim, and glmIntro to Bayesian
Estimating parameters using Maximum Likelihood
ggeffects packageReview of statistical concepts in context of linear regression:
If all assumptions hold:
\[\frac{\hat{\beta}_1-\beta_1}{\hat{SE}(\beta_1)} \sim t_{n-2}\]
\(\Rightarrow\) when \(H_0: \beta_1 = 0\) is true, \(\frac{\hat{\beta}_1-0}{\hat{SE}(\beta_1)} \sim t_{n-2}\)
So, we can use the \(t_{n-2}\) distribution for testing the null hypothesis.
Review of statistical concepts in context of linear regression
Coding:
More review of linear regression
Bootstrapping
Usually, I would post a few bullet points to remind ourselves of what we covered during the previous class
Review of statistical concepts in context of linear regression
Coding: