When you can measure what you are speaking about and express it in numbers you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of an unsatisfactory kind - Kelvin (1824-1907)

  • Syllabus, Al's email, Office Phone: 994-5145, Office: Barnard (EPS) 304, Office Hours and Schedule.
  • Allison's office hours on 4/17 (Tuesday) and 4/19 (Thursday) are from 9a-11a in Wilson 2-232.
  • R resources:
  • Writing a statistical report for Data Analysis problems; Summary of Statistical Findings examples; Scope of Inference Writing examples
  • Other useful links:
  • Exams:
  • STAT511 Project
  • Homeworks:
    • DUE 4/23 HW12: activity from Chapter 11 MLR notes, more details to be provided in class on 4/20, SOLUTIONS: pdf
    • DUE 4/18 HW11, outline of SOLUTIONS: pdf
    • DUE 4/11 HW10, SOLUTIONS: pdf or Rmd
    • DUE 4/4 HW9, SOLUTIONS: pdf or Rmd
    • DUE 3/26 HW8, SOLUTIONS: pdf or Rmd
    • DUE 2/26 HW7, SOLUTIONS: pdf or Rmd
    • DUE 2/21 HW6, chronic wound data, SOLUTIONS: pdf or Rmd
    • DUE 2/12 HW5, SOLUTIONS: pdf or Rmd
    • DUE 2/5 HW4, SOLUTIONS
    • DUE 1/29 HW3, grading scheme
    • DUE 1/22 HW 2, pregnancy and pot use article, SOLUTIONS
    • DUE 1/17 HW 1, turn in answers to questions #1-8 from Lab 1: copy and paste the questions into a new document, then enter your R code and output under each question.
  • Labs:
    • 4/25 Lab 15, open format, come with any questions you have
    • 4/18 Lab 14, §11 Case influence statistics
    • 4/11 Lab 13, working on HW11
    • 4/4 Lab 12, working on HW10
    • 3/28 Lab 11, working on HW9
    • 3/21 Lab 10, working on HW8
    • 3/7 Lab 9, SLR, claims data.  Partial SOLUTIONs: pdf and Rmd.
    • 2/27 Lab 8, working with objects in R: pdf or Rmd
    • 2/20 Lab 7, working on HW7
    • 2/14 VALENTINES DAY LAB 6, working on HW6, chronic wound data
    • 2/7 Lab 5: A closer look at ANOVA table, F distribution, permutation and randomization ANOVA (PERMANOVA), notes
    • 1/31 Lab 4: checking for normality and the Box-Cox transform of 1-sample data, SOLUTIONS
    • 1/24 Lab 3, using RMarkdown: example: Rmd, doc, html
    • 1/17 Lab 2, working on HW2.
    • 1/10 Lab 1.pdf: the Rmarkdown file that generated Lab1.pdf; getting started with R; Download Rdownload RStudio; dairy data as: text, csv
  • Extra problems:
    • Chapter 11: p. 338 (answers on p. 343): #1-3, 5-8.
    • Chapter 10: p. 297 (answers on p. 309): #3-8.
    • Chapter 9: p. 261 (answers on p. 269): #1, 2, 4-11.
    • Chapter 8: p. 227 (answers on p. 235): #1-14.
    • Chapter 7: p. 198 (answers on p. 206): #3-11. 
    • Chapter 6: p. 170 (answers on p. 175): #1, 4, 6-11.
    • Chapter 5: p. 141 (answers on p. 147): #1-4, 6-10, 12.
    • Chapter 3: p. 77 (answers on p. 84): #1-10, 14, 16, 17, 19.
    • Chapter 2: p.51 (answers on p.56): #1-10, 11b.
    • Chapter 1: p.22 (answers on p.26): #1-11, 15.
  • Course Schedule
    • 5/3 12pm - 1pm Office Hours in Barnard304; 6pm - 8pm Final Exam in JABS407
    • 5/2 Office Hours CANCELLED (going to sample in Yellowstone)
    • 5/1 12pm - 1pm Office Hours in Barnard304
    • 4/27 Review
    • 4/25 §12 An example
    • 4/23 §12 Variable selection using AIC and BIC and all-subsets regression, Chapter 12 notes

    • 4/20 §11 Refining the model.  Chapter 11 MLR notes: pdf and Rmd,  evaluation data
    • 4/18 NO CLASS - but in lab: §11 Case influence statistics.   Chapter 11 SLR notes
    • 4/16 §10.2-10.4 CIs for mean response, PI for future response, extra sum of squares F test

    • 4/13 §10.2-10.4 Linear combinations of coefficients
    • 4/11 §10.1-10.2 How R finds the least squares solution; tests and CIs for individual coefficients, Chapter 10 notes
    • 4/9 §9.4-9.6 Fitting an MLR model with factors with more than 2 levels; beware of overfitting

    • 4/6 §9.4-9.6 Fitting the basic MLR Model IV with two predictors and an interaction; fitting an MLR model with any number of predictors and factors
    • 4/4 §9.4-9.6 Fitting the basic MLR Model III with two predictors without an interaction
    • 4/2 §9.4-9.6 Graphical tools for assessing Models III and IV

    • 3/30 SPRING FRIDAY - NO CLASS
    • 3/28 §9.1-9.3 Fitting the basic MLR Model II with one predictor and one factor with an interaction, Chapter 9 notes
    • 3/26 §9.1-9.3 Fitting the basic MLR Model I with one predictor and one factor with no interaction

    • 3/23 §8.5, Extra sum of squares test for model selection, Chapter 8.5 notes
    • 3/21 §7.5, 8.2-8.3 Correlation, Robustness of SLR to deviations from assumptions, Chapter 8.2-8.3 notes.
    • 3/19 §7.4Tests and CIs for mean response, PIs for a future response and mean response, PIs for a future response

    • 3/12 - 3/16 SPRING BREAK!

    • 3/9 In-class group work for extra credit
    • 3/7 §7.4, 8.3, 8.6 Checking regression assumptions, tests of regression parameters, Chapter 7 notes
    • 3/5 §6.2 Intro to SLR, ROutput: pdf, Rmd

    • 3/2 Mid-term exam
    • 2/28 Review
    • 2/26 §6.3 Bonferronis

    • 2/23 §6.3-64 Planned vs unplanned tests and CIs, simultaneous family of tests and CIs, Tukeys multiple comparisons after ANOVA
    • 2/21 §6.2 Testing linear combinations after ANOVA, Chapter 6 notes
    • 2/19 PRESIDENT'S DAY - NO CLASS

    • 2/16 §5.6 Random effects ANOVA applied to the skull data with a cluster effect for Epoch
    • 2/14 §5.6 Kruskal-Wallis (non-parametric ANOVA), random effects ANOVA
    • 2/12 §5.2 Follow-up t-test for a planned comparison

    • 2/9 §5.3, 5.5 ANOVA model, checking ANOVA model assumptions with residuals, notes
    • 2/7 NO CLASS - but in Lab: A closer look at ANOVA table, F distribution, permutation and randomization ANOVA (PERMANOVA), notes
    • 2/5 §5.2-5.3 The basics of ANOVA; ANOVA as a comparison of reduced and full models, Chapter 5 notes, skull data, diagANOVA.r

    • 2/2 §3.4 -3.5 Box Cox transform of 2 sample data, biofilm data, ROutput
    • 1/31 §3.2-3.3  Robustness to non-normality and non-independence, Resistance to outliers, Chapter 3 notes, timber data
    • 1/29 §2.3 Two-sample z- and t-tests: unpooled, pooled, paired, ROutput

    • 1/26 §2.3 t confidence intervals, two sample tests, ROutput
    • 1/24 §2.2 one-sample z-test and t-test, z confidence intervals, ROutput: pdf or Rmd, Chapter 2 notes
    • 1/22 §1.4, 2.2 permutation test for observational studies, a simulation model for random sampling, Central Limit Theorem
    • 1/19 §1.3 Randomization test for experiments, ROutput.
    • 1/17 §1.2 Study design, sampling plans, Scope of Inference
    • 1/15 NO CLASS: MARTIN LUTHER KING DAY!

    • 1/12 §1.2: t-test, inference, sampling distribution, R output, Chapter 1 notes
    • 1/10 Welcome!   Remember the Scientific Method?