STAT4113  Methods of Data Analysis I  Fall 2017
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 (18241907)
 Syllabus, Al's email, Office Phone: 9945145, Office: Barnard (EPS) 304, Office Hours and Schedule.
 R resources:
 home page, download R, download RStudio (a nice R interface)
 help: web pages An Intro to R, SimpleR, printable pdfs: An Intro to R, SimpleR
 Writing a statistical report for Data Analysis problems; Summary of Statistical Findings examples; Scope of Inference Writing examples; report on iris analysis example and the markdown file that created it.
 Other useful links:
 Stat Course Catalog
 Final exam schedule for Fall 2017
 Search classes
 If you need to take 412 or 512 in either Spring 2018 or Fall 2018, please fill out
the survey: https://goo.gl/forms/
dlb5zs4zsv2Zg2MW2 . If you have other questions about 412 or 512, you can contact Mark Greenwood at greenwood@montana.edu.
 Exams:
 12/14 Final Exam II, 4pm  6pm in Reid 401
 10/13 Midterm Exam 1, UPDATED formula sheet. SOLUTIONS.
 Homeworks:
 DUE 11/20 HW9 on Chapters 9 and 10.
 DUE 11/13 HW8 on Chapter 9. Other problems on p. 261 (answers on p. 269): #1, 2, 411.
 DUE 11/6 HW 7 on Chapters 7 and 8. Other problems on p. 227 (answers on p. 235): #114.
 DUE 10/30 HW6 on Chapters 6 and 7. Other problems on p. 170 (answers on p. 175): #1, 4, 611; on p. 198 (answers on p. 206): #311.
 DUE 10/9 HW5 on Chapter 5: nonparametric ANOVA and random effects ANOVA. Biofilm data. SOLUTIONS.
 DUE 10/2 HW4 on Chapter 5: ANOVA and PERMANOVA. SOLUTIONS: pdf, Rmd. Other problems on p. 141 (answers on p. 147): #14, 610, 12.
 DUE 9/22 HW3 on Chapters 2 and 3. SOLUTIONS. Other problems on p. 77 (answers on p. 84): #110, 14, 16, 17, 19.
 DUE: 9/13 HW2: By hand (i.e., turn in a handwritten writeup), perform a 1sample ztest and construct a 1sample CI for some data set of interest (e.g., for the intrinsic writing group Sleuth3 dataset case0101 in R (see Chapter 1 notes)). You choose a value for Ho: mu = mu0 that is different than 0. You choose the confidence level. Perform all 6 steps of the hypothesis testing framework, including a conclusion in terms of the problem. SOLUTIONS. Other problems on p. 51 (answers on p.56): #110, 11b.
 DUE: 9/8 HW1 on Chapter 1, article: Your Brain on Meth. SOLUTIONS. Other problems on p.22 (answers on p.26): #111, 15.
 Labs:
 11/15 Working on R code for HW9.
 11/8 Working on R code for HW8.
 11/1 Working on R code for HW7.
 10/25 Working on R code for HW6.
 10/18 Lab 4 on regression, claims data. Partial SOLUTIONS: pdf, rmd
 10/11 Review Chapters 13, 5, 6
 10/4 Working on R code for HW5.
 9/27 Lab 3, using R Markdown for reporting; working on R code for HW4.
 9/20 Working on R code for HW3.
 9/13 Lab 2, working with objects in R.
 9/6 Working on R code for HW1.
 8/30 Lab 1, getting started with R. Dairy data as: text, csv
 Course Schedule
 11/22  11/24 THANKSGIVING BREAK!
 11/20 In class group work for extra credit
 11/17
 11/15
 11/13 §9.49.6 Fitting any MLR, graphical tools for assessment
 11/10 VETERANS DAY observed, go thank a veteran
 11/8 §9.19.3 Fitting the basic MLR model with one predictor and one factor with an interaction, including factors with more than 2 levels, notes
 11/6 §9.19.3 Fitting the basic MLR model with one predictor and one factor with no interaction, notes
 11/3 §8.5 Extra sum of squares as model selection
 11/1 §8.5 Lackoffit test comparing SLR to ANOVA, notes
 10/30 §9.29.3 Basic MLR models: one predictor and a factor, or two predictors
 10/27 §8.4 transforms, example from Lab 4: pdf, rmd
 10/25 §7.5, 8.2, 8.5 Correlation, robustness
 10/23 §7.4 Tests and CIs of regression parameters and mean response, PIs for a future response
 10/20 §6.4, 8.3, 8.6 Examples of planned vs unplanned comparisons, checking regression assumptions, notes
 10/18 §6.3 Planned vs unplanned comparisons, Bonferronis
 10/16 §7.17.3 Simple linear regression, notes: pdf, rmd
 10/13 Midterm Exam 1
 10/11 §6.4 Tukeys multiple comparisons after ANOVA
 10/9 §6.2 Testing linear combinations after ANOVA
 10/6 §6.1 Case study, Chapter 6 notes
 10/4 §5 When to use which ANOVA
 10/2 §5.6 Nonparametric ANOVA, random effects ANOVA
 9/29 §5 Permutation and randomization ANOVA
 9/27 §5.25.3 ANOVA as a comparison of reduced and full models, followup ttest for a planned comparison
 9/25 §5.3, 5.5 ANOVA model, checking ANOVA model assumptions with residuals
 9/22 §5.25.3 How ANOVA fits in our toolbox along with ttests, randomization and permutation tests, nonparametric tests, Chapter 5 notes, skull data, diagANOVA.r
 9/20 §3.5 Transforming to normality: BoxCox
 9/18 §3.43.5 Identifying and vetting outliers; identifying nonnormality
 9/15 §3.23.3 Robustness to nonrandom sampling, resistance to outliers, Chapter 3 notes, timber data
 9/13 §2.3, §3.2, §4.3.2 Twosample ztest and ttest, robustness to nonnormality
 9/11 §2 z and t confidence intervals
 9/8 §2.2 Central Limit Theorem, onesample ztest and ttest, Chapter 2 notes: original, updated with 1sample tCI and example Rcode
 9/6 §1.4 permutation test for analysis of observational data
 9/4 NO CLASS: LABOR DAY!

 9/1 §1.3: randomization test for analysis of experimental data
 8/30 §1.2: sampling, parameters, estimation, uncertainty quantification, Chapter 1 notes
 8/28 Welcome! Remember the Scientific Method?