Statistics 135: Concepts of Statistics (Spring 2020)

May 22, 2020

Course Information

  • Professor: Adam Lucas
  • Office Hours: W 12-1pm, Th 1-3pm, F 9:30-10:30am
  • Lab Sections: F 12-2pm, F 4-6pm
  • Content: STAT 135 is Berkeley’s core upper-division course on statistical theory and methodology for undergraduates in statistics and related fields. Topics include parameter estimation, hypothesis testing, statistical tests (parametric and non parametric) and linear regression (single and multiple).

Please refer to Lab 7 slides for a thorough review of part 1 & 2 (i.e., parametric estimation and hypothesis testing). The following teaching materials were made by me, and the problem sets in the slides were from previous iterations of the class.

Materials

Part 1: Parametric Estimation

  • Lab 2: MoM, nonparametric bootstrap, SE calculation [Slides]

  • Lab 3: MLE, Delta method [Slides]

  • Lab 4: Quiz 1 recap, Fisher information, CRLB, MSE, ggplot [Slides]

  • Lab 5: Sufficiency, minimal sufficiency, Rao-Blackwell theorem [Slides]

Part 2: Hypothesis Testing

  • Lab 6: Hypothesis testing, Neyman-Pearson lemma, LRT, UMP test, GLRT, p value [Slides]

  • Lab 7: Midterm Review [Slides]

Part 3: Statistical Tests (parametric & nonparametric)

  • Lab 8: 2-sample t-test, chi-squared test [Slides]

  • Lab 9: TOH, TOI, Mann-Whitney test [Slides]

  • Lab 10: Wilcoxon signed rank test, ANOVA, Bonferroni correction [Slides]

Part 4: Linear Regression (single & multiple)

  • Lab 11: Bonferroni t-test, Kruskal Willis test, review of statistical tests, linear regression with its R implimentation [Slides] [R demo]

  • Lab 12: Statistical properties for least squares estimation [Slides] [R soln]

  • Lab 13: Multiple linear regression, prediction interval, Bayesian statistics [Slides] [R demo]