Summer I, 2017







Lecture 
Date 
Lecture & Lab Topics 
Readings 
Homeworks and Tests 
Labs 
1  T 5/23  Administrative matters; Goals of the course; Motivating Examples: Why should you know probability and statistics? Basic definitions of probability theory: outcomes, sample spaces, probability functions, axioms of probability; examples of finite, countably infinite, and uncountable sample spaces and typical problems in each. Definition of probability of event in equiprobable, finite case. 
Here is a link to an article on traffic deaths after 9/11: HTML Here is the wiki page explaining the "Base Rate Falacy" (look at Example 1, on breathalyzer tests): HTML Just for Fun: Here is a short YT video with various clips from movies which involve probability (we will return to the Monty Hall Problem, the first clip, in a later lecture): HTML I covered approximately sections 3.1  3.5 in Schaum's, BUT without talking about using set operations (union, intersection, complement) on events (i.e., pp.6365). If you need a review of sets, read through chapter 1. 

2  W 5/24  Probability spaces; Theorems on probability and set operations on events; nonequiprobable probability spaces; tree diagrams and the "Four Step Method". The Monty Hall Problem. The Birthday Problem.  Read Schaum's Chapter 3; read MCS sections 17.1 & 17.2 on the "Monty Hall Problem" and the "Four Step Method." Look at the link above to a video of the "Monty Hall Problem." Optional: Section 17.5 from MCS covers the same material in Schaum's Chapter 3, but more rigorously, it is worth looking at for the application of the tree diagram technique. Optional: Section 4.3 of Schaum's has a slightly different presentation of the tree diagram technique. 
Practice problems from Schaum's (not to hand in): 3.2, 3.3, 3.5, 3.6, 3.7, 3.16, 3.18, 3.26. HW 01: HTML Solution: HTML 

3  R 5/25  Conditional Probability  Read Schaum's Chapter 4 (whole thing) MCS 18.5  18.7 has the same material, including Simpson's Paradox 
Practice problems from Schaum's (not to hand in): 4.1, 4.4, 4.5, 4.6, 4.21, 4.22, 4.25, 4.26, 4.29, 4.36 HW 1: HTML Lab and HW due Tuesday night 5/30 at midnight in the CS Homework Station. Solution: HTML 
Lab 1: HTML 
M 5/29  Holiday, no class 


4  T 5/30  Counting principles and combinatorics; permutations and combinations; accounting for duplicates; permutations; applications to probability problems. 
Read Schaum's Chapter 2 and MCS Sections 15.1  15.7 (pp.609)  This should be review from CS 131! Also, look at this summary of problemsolving strategies, most of which involve combinatorics: HTML

Practice problems from Schaum's (not to hand in): 2.4, 2.8, 2.12, 2.15, 2.16, 2.19, 2.23, 2.25, 2.29, 2.30, 2.33 HW 2: HTML HW 2 Solution: HTML 
Lab 2: HTML 
5  W 5/31  Counting continued; multinomial coefficients, partitions  Read this link on Permutations with Repetitions; 


6  R 6/1  Random Variables; Expected value, Variance, and Standard Deviation; Distributions.  Read Schaum's Chapter 5.1  5.5, 5.11  Practice problems from Schaum's (not to hand in): 5.1  5.5, 5.8, 5.9, 5.15, 5.18 HW 3: HTML HW 3 Solution: HTML 
Lab 3: HTML 
7  F 6/2  Important Distributions: Uniform, Bernoulli, Binomial  Read Schaum's Chapter 6.2 For an illustration of the Binomial, take a look at this Quincunx animation: HTML 
Practice problems from Schaum's (not to hand in): 6.2, 6.3, 6.5, 6.8, 6.12. Here is a summary of some of the most useful discrete distributions: HTML (you do not need to know any that we did not study in lecture) 

8  M 6/5  Important Distributions: Binomial, Geometric, & Poisson;  Read Schaum's Chapter 6.7 and 6.8 (c) (p.195).  Practice problems from Schaum's (not to hand in): 6.38, 6.39.  
9  T 6/6  Poisson continued; Relationship between Binomial and Poisson. Discussion of homeworks 2 & 3; review for midterm. 

No lab!  
10  W 6/7  First Midterm Exam  Midterm 1 Solution: PDF 
Here is last summer's exam: PDF  
12  R 6/8  Joint Random Variables in discrete case; Conditional JRV's; Independence of JRVs.  Read Schaum's 5.6  5.7. Here is my spreadsheet from lecture: XLS 
Practice problems from Schaum's (not to hand in): 6.25, 6.27, 6.29 HW 4: HTML HW 4 Solution: HTML 
Lab 4: HTML 
13  M 6/12  No class (I was sick) 


14  T 6/13  General (continuous) Random Variables; Uniform Distribution; Importance of CDF; Normal Distribution, Exponential distribution;  Read Schaum's 5.6, 5.7, 5.10, 6.3, 6.4 Lecture on Normal Distribution: PDF 6.5, 6.6, 6.9 
There are practice problems at the end of chapter 5 of Schaum's, keyed to the relevant section: Look at practice problems for the sections I asked you to read.  Lab 5 (Generating Normal and Exponential Variates; Normal approximation to Binomial): HTML Here is documentation on the scipy.norm statistics library: HTML Starter code: Lab05.py 
W 6/14  Relationships between discrete and continuous distributions: Normal and Binomial; Exponential and Geometric; Exponential and Poisson 
Review 5.6, 5.7  
R 6/15  Limit Theorems; Central Limit Theorem  Read 5.12, 6.5,  HW 5: HTML (due M 6/12) HW 5 Solution: HTML Look at relevant Schaum's problems. 
Lab 6 (Pandas): HTML  
M 6/21  Missed Class  Read the Appendix of Schaum's on Statistics, your friendly neightborhood Statistics textbook, or here: PDF 

T 6/22  Basic Statistics: Sampling theory, point estimation, confidence intervals; Hypothesis Testing; 
Schaum's Appendix A.5  A.6  For practice look at Examples A.1  A.6 in the text, then problem A.5 Here are additional problems from another Schaum's: PDF 
No lab  
W 6/23  Joint Random Variables concluded: Scatterplots and linear regression;  Schaum's Chapter 7;  HW 6: HTML HW 6 Solution: HTML For practice, look at Examples A.10  A.13, then problem A.12. Here are practice problems from another Schaum's: PDF 

R 6/24  CS Applications: Autocorrelation and Frequency Detection in Music Files; Queuing Theory  No lab  
M 6/26  Second Midterm  Solution: PDF 
Here is the table of values for the CDF of the standardized normal distribution; be sure you know how to convert a given normal RV to the standard RV so you can look up the values.  
T 6/27  Discussion of final homework  Final Homework: HTML  
W 6/28  CS Applications: Probability and Complexity Theory; Discussion of Final Exam, and Evaluation  
R 6/29  Final Exam  
F 6/30  Final Homework Due at 5pm 