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 14 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 12 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, interquartile 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 45 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 12, 45
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 12 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 12 Intro to Probability: Lesson 27 Online Stats book: IV Describing Bivariate Data, sections AE 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 12  Homework 7  
#10
Thurs 27 February 
MLE continued and unbiased estimation  Intro to Probability: Lesson 24, section 3 Intro Mathematical Statistics: Lesson 29, sections 23 
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 13 and 56  tdistribution 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; pvalues; tests about one proportion  Intro Mathematical Statistics: Lesson 37, sections 13
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 ZTest in R R: One sample ttest  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 12 R: Two sample ttest 
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  
816 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 
Chisquared goodnessoffit test  Intro Mathematical Statistics, Lesson 44, sections 14  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 