| Title | First Submission Due Date | Resubmission Due Date |
|---|---|---|
| 00 Student Profile | 2021-08-30 | Not available |
| 01 Roulette | 2021-09-06 | 2021-09-24 |
| 02 Monte Carlo Error | 2021-09-13 | 2021-10-04 |
| 02b Interview Question | 2021-09-20 | 2021-10-11 |
| 03 World Series | 2021-09-27 | 2021-10-19 |
| Extra credit: Birthday Problem | 2021-09-27 | Not available |
| 04 Home Field Advantage | 2021-10-04 | 2021-10-25 |
| 05 Log Transformation | 2021-10-11 | 2021-11-03 |
| 06 Order Statistics | 2021-10-25 | 2021-11-17 |
| Take a break / Get caught up | 2021-11-01 | Not available |
| 07 MLE ane MM | 2021-11-08 | 2021-12-09 |
| 08 Coverage Probability | 2021-11-29 | 2021-12-16 at 9am |
| 09 Methods not equally good | ||
| 10 Central Limit Theorem Shortcut | ||
| 11 Correlation and Inference |
| Due Date | Problem |
|---|---|
| 2021-10-20 | (a) Generate, via simulation, a plot of the distribution of the 25th percentile of a sample of size 100 when the underlying distribution is gamma with shape = 3 and scale = 12 (b) Overlay on the plot from (a) the analytic solution of the pdf |
| 2021-10-27 | (a) Read chapter 3. (b) Complete problem 4 of Section 3.12 (c) Complete problem 8 of Section 3.12 |
| 2021-11-01 | (a) Read chapter 7. (b) Read chapter 8. (c) Replicate figure 8.5 by following the example in section 8.4.2 You can get the pima dataset by installing the faraway package and the command data(pima) (d) Generate the same plot as Figure 8.5 for adult males using the NHANES dataset. Recall you can use the command Hmisc::getHdata(nhgh) to retrieve the data. (e) Replicate figure 8.6 by following the example in section 8.4.4 You can use the following commands to retrieve the bike dataset from the UCI repository temp <- tempfile() |
| 2021-11-03 | (a) Do exercise 7 of section 8.10, overlay the estimated pdf on the histogram (b) Add to the plot in (a) a kernel density estimate of the pdf Recall, the dataset is in the faraway package. Missing values are coded as zero. |
| 2021-11-10 | (a) Review the slides at https://biostatdata.app.vumc.org/tgs/13-bootstrap.html (b) Replicate the figure on slide 32. (The code is provided in slides 30 and 31. Copy and paste.) |
| 2021-11-15 | (a) Read chapter 9 (b) Complete questions 1, 2, 3, 6, 7 in section 9.13 |
| 2021-11-17 | (a) Read https://biostatdata.app.vumc.org/tgs/18-parallel-processing.html (b) Read https://biostatdata.app.vumc.org/tgs/20-batch-processing-accre.html |
| Date |
|---|
| 2021-11-10 |
| 2021-11-15 |
| 2021-11-17 |
| 2021-12-08 (practice problems) |
The final exam will occur between 13 December 2021 and 18 December 2021. Students will sign up for oral exam slots in early December.
PLEASE NOTE: The slides are often changed before lecture (both major edits and minor tweaks).
| Topic | Slides | Textbook sections | Videos |
|---|---|---|---|
| Class logistics | |||
| Definitions of Probability | slides | ||
| Simulation & Operating Characteristics | slides review |
2 | |
| Basic Probability Ideas | slides slides part 2 |
1 | |
| → Belief vs Frequency | |||
| → Notebook / data.frame definition | |||
| → And, Or | 1.3 | ||
| → Conditional Probability | 1.3 | ||
| → Law of Total Probability | slides slides part 2 |
||
| → Bayes Rule | slides slides part 2 |
1.9 | |
| Discrete Probability Models | 3, 4, 5 | ||
| → Bernouli Random Variables | slides | ||
| → Binomial Random Variables | slides | ||
| → Negative Binomial Random Variables | slides | ||
| → Poisson Random Variables | slides | ||
| → Probability Mass Function | slides | ||
| Continuous Probability Models | 6 | ||
| → Cumulative Distribution Function | slides | ||
| → Probability Density Function | slides | ||
| → Uniform Random Variables | slides | ||
| → Normal Random Variables | slides | ||
| → Exponential Random Variables | slides | ||
| → Gamma Random Variables | slides | ||
| → Beta Random Variables | slides | ||
| → Mixture Distributions | slides | ||
| Expectation and Variance | 3.6, 4.1, 4.3, 6.5 | ||
| → Data Types | slides | ||
| → Categorical, Ordinal, Interval, and Ratio Variables | slides | ||
| → Covariance | slides | ||
| Transformations of individual observations | |||
| Transformations of samples | 7 | ||
| → Min and Max | slides | ||
| → Quantiles | slides | ||
| → Order Statistics | slides application |
||
| → Sampling distributions | slides | ||
| Methods of Fitting Models | Lots of pdfs | 8 | |
| → QQ-plot | |||
| → Method of moments | slides | ||
| → Maximum likelihood | slides | ||
| → Bayesian | |||
| → Kernel Density Estimation | slides | ||
| Sampling Distributions from Fitted Models | |||
| → Bootstrap | slides | ||
| → Simulation | slides | ||
| → Central Limit Theorem | slides | ||
| Simulation | |||
| → Parallel Computing | slides | ||
| → Batch processing on ACCRE or AWS | slides | ||
| Inference | |||
| → Sampling and Inference | |||
| → Inference with CI | slides | ||
| → Inference with Hypothsis testing | slides | ||
| Multivariate Normal Distribution | 12 | ||
| → Properties | slides | ||
| → Correlation | slides | ||
| → Conditional Distribution | slides | ||
| → Marginal Distribution | slides |