| Date: | Topics: | Reading: | Code and data from class, and visualizations: | Homework and project deadlines: |
| #1
Tues 28 January |
Syllabus, academic integrity code; types of data, sample spaces, discrete random variables; intro to RStudio: importing CSV files, math, line plots | Syllabus Academic integrity policy Intro to Probability Theory: Lesson 1, sections 1-4 Intro to Probability Theory: Lesson 7, section 1 Math in R Importing data into RStudio Line plot: add type = "l" as a parameter to code for a scatter plot |
nycHistPop.csv code from class |
|
| #2
Thurs 30 January |
Histograms, barplots, probability mass functions, empirical distribution | Intro to Probability Theory: Lesson 1, section 5 Intro to Probability Theory: Lesson 7, sections 1-2 R: histograms R: barplots |
Yellow taxi data: jan30_2019_yellow_taxi.csv (from NYC OpenData) code from class |
|
| #3
Tues 4 February |
Measures of center, expectation of a random variable | Intro to Probability Theory: Lesson 8, sections 1,3,5 Online Stat Book: Chapter III, section 4 (median and mode) Online Stat Book: Chapter III, section 10, part 5 (trimmed mean) R: mean, median |
Washington CD bikeshare data: hour.csv (originally download from UCI Machine Learning Repository and modified) code from class |
Homework 1 |
| #4
Thurs 6 February |
Measures of spread, order statistics, variance of a random variable | Intro to Probability Theory: Lesson 8, sections 4, 5 Intro to Probability Theory: Lesson 13, section 3 Online Stat Book: Chapter III, section 13 (range, inter-quartile range) R: Variance, standard deviation, IQR, min, max, range |
NYC subway variability code from class |
Homework 2 |
| #5
Tues 11 February |
Box plots, shape of distributions, continuous random variables, probability density function | Intro to Probability Theory: Lesson 13, sections 4-5 Intro to Probability Theory: Lesson 14, section 1 R: boxplot |
GitHub
code from class |
Homework 3 Milestone 1 (dataset) in class |
| #6
Thurs 13 February |
Bernoulli and binomial random variables Using GitHub Cleaning data: renaming columns, missing values |
Intro to Probability Theory: Lesson 10, sections 1-2, 4-5
GitHub basics tutorial R: renaming columns R: computing binomial distribution density |
Visualizing the binomial distribution
Data: starbucks drinks Data: babies.data code from class |
Homework 4 Milestone 2 (GitHub account) on Blackboard |
| #7
Tues 18 February |
Normal distribution, Central Limit Theorem, probability in R |
Intro to Probability Theory: Lesson 16, sections 1-2 Intro to Probability Theory: Lesson 27 R: distributions R: Testing the Central Limit Theorem |
Visualization (interactive): Normal distribution Visualization (interactive): Central Limit Thorem Visualization: Central Limit Theorem code from class |
Homework 5 |
| #8
Thurs 20 February |
Scatterplots, joint probability distribution, correlation |
Intro to Probability: Lesson 17, sections 1-2 Intro to Probability: Lesson 27 Online Stats book: IV Describing Bivariate Data, sections A-E R: Scatterplots |
Correlation guessing game
code from class |
Homework 6 Milestone 3 (webpage and data description) in class |
| #9
Tues 25 February |
Likelihood and maximum likelihood estimation | Intro Mathematical Statistics: Lesson 29, sections 1-2 | Homework 7 | |
| #10
Thurs 27 February |
MLE continued and unbiased estimation | Intro to Probability: Lesson 24, section 3 Intro Mathematical Statistics: Lesson 29, sections 2-3 |
Homework 8 Milestone 4 (distribuion plots) in class |
|
| #11
Tues 3 March |
Subsets in R and Midterm 1 review | subset() function subset() function Examples of the subset() function |
code from class | Homework 9 |
| #12
Thurs 5 March |
Midterm 1 | |||
| #13
Tues 10 March |
Confidence intervals for one mean | Intro Mathematical Statistics: Lesson 30, sections 1-3 and 5-6 | t-distribution code from class |
|
| #14
Thurs 12 March |
Confidence interval for two means, variances, and proportions | Intro Mathematical Statistics: Lesson 31 Intro Mathematical Statistics: Lesson 32 Intro Mathematical Statistics: Lesson 33 |
Homework 10 If applicable: Milestone 5 (missing data and outliers) in class |
|
| #15
Tues 17 March |
Simple linear regression | Intro Mathematical Statistics: Lesson 35
R: Simple linear regression |
||
| #16
Thurs 19 March |
Regression continued | Intro Mathematical Statistics: Lesson 36
R: confidence intervals for regression R: Multiple linear regression |
Homework 12 (Day 14) Milestone 6 (measures of center and spread) in class |
|
| #17
Tues 24 March |
Introduction to hypothesis testing; type 1 and 2 errors; p-values; tests about one proportion | Intro Mathematical Statistics: Lesson 37, sections 1-3
R: One proportion test |
Homework 13 (Day 15) | |
| #18
Thurs 26 March |
Hypothesis tests about two proportions, tests about one mean | Intro Mathematical Statistics: Lesson 37, section 4 Intro Mathematical Statistics: Lesson 38 Two Proportion Z-Test in R R: One sample t-test | Homework 14 (Day 16) Milestone 7 (scatterplots and correlation) in class |
|
| #19
Tues 31 March |
Hypothesis testing for two means |
Intro Mathematical Statistics: Lesson 39, sections 1-2 R: Two sample t-test |
Homework 15 (Day 17) | |
| Wed 1 April | Last day to withdraw from class with a grade of W | |||
| #20
Thurs 2 April |
Midterm 2 review | Homework 16 (Day 18) Milestone 8 (confidence intervals) |
||
| Tues 7 April | Wednesday schedule: No MAT 327/782 class | |||
| 8-16 April | Spring recess: no classes | |||
| #21
Tues 21 April |
Midterm 2 | |||
| #22
Thurs 23 April |
Analysis of variance (ANOVA) | Intro Mathematical Statistics, Lesson 41
R: ANOVA |
Visualizing ANOVA | Milestone 9 (linear regression) in class |
| #23
Tues 28 April |
Analysis of variance (ANOVA) continued | Intro Mathematical Statistics, Lesson 41
R: ANOVA |
Homework 17 (Day 19) | |
| #24
Thurs 30 April |
Chi-squared goodness-of-fit test | Intro Mathematical Statistics, Lesson 44, sections 1-4 | Homework 18 (Day 22) Milestone 10 (hypothesis test) in class |
|
| #25
Tues 5 May |
Contingency tables | Intro Mathematical Statistics, Lesson 45 | Homework 19 (Day 23) | |
| #26
Thurs 7 May |
Bayesian statistics | Intro Mathematical Statistics, Lesson 52 | Homework 20 (Day 24) Milestone 11 (your choice) in class |
|
| #27
Tues 12 May |
Project presentations, Bayesian satistics cont'd | Homework 21 (Day 25) Project slides due at 10am |
||
| #28
Thurs 14 May |
Review for final exam | |||
| Tues 19 May | Final exam 3:45pm - 5:45pm | |||