Class Meets Monday/Wednesday/Friday 11:30–12:20

Room Keller Hall 402

See here for the syllabus.

• Chapter 1

• Chapter 2

• Chapter 3

• Chapter 4

• Chapter 5

• Chapter 6

• Chapter 7

• Chapter 8

• Chapter 12

## Group Projects

The group projects page is here. Group projects are canceled on account of global pandemic.

## Announcements

• (4/30) Here is a study guide for the final exam. For the exam, you should know everything you were expected to know for the two midterms, as well as the material we covered after the second midterm. In particular, the new material you should know is:

• Using chi squared distributions to get confidence intervals for the variance of normal distributions from a random sample.

• The concepts of hypothesis testing.

• How to do z tests for hypothesis tests involving the mean, and to calculate facts about the test.

• The concepts of linear regressions.

• How to calculate the least squares regression line for a scatterplot and determine information about it.

• (4/6) Here is a study guide for the second midterm. The two csv files are bigsample.csv and normalsample.csv. You can do calculations on the random samples either by importing the csv files to a spreadsheet or by using R. (Or another programming language, if you prefer.) For R, the command to import a csv file is read.csv("filename.csv"). This imports the csv file as a dataframe. For the csv files I gave, the column of values is labeled as x, so if you do xs <- read.csv("bigsample.csv") to store the dataframe to a variable xs, then xs$x will give you the list of values, which you can then use for your calculations. • (4/6) Here’s what you should know for the second midterm: • How to compute probabilities involving normally distributed random variables. • How to check whether random variables are independent. • How to use the central limit theorem to approximately computable probabilities involving sample means. • How to compute probabilities involving linear combinations of normal random variables. • Basic concepts about point estimators—what does it mean to be unbiased, what the MVUE is, and so on. • How to use computer tools to compute confidence and prediction intervals. • (3/4) Here is the R code I used to generate the handout from class today. • (2/24) Here is the formula sheet you will have for the first midterm. • (2/19) Here is the home page for the R project. You can find download links and documentation on that site. And here is the sample program from class today. • (2/14) Here are some resources about using a calculator or computer to do calculations about normal random variables. This video tutorial explains how to use a TI-83 calculator for the calculations. This WolframAlpha widget can be used to calculate probabilities like$P(a < X < b)$where$X\$ is normally distributed. You may find these useful for homework questions involving normal random variables.

• (2/14) The study guide for the first midterm is here.

• (2/12) Here are the topics you should know for the first midterm:

• Basic definitions and properties of sample spaces, events, probability functions, conditional probability, and independent events.

• Counting techniques: permutations, combinations.

• Bayes’s theorem.

• Discrete random variables: calculating probabilities, expected values, variances. Probability distribution functions and cumulative distribution functions.

• Binomial and Poisson discrete random variables. Poisson processes.

• Continuous random variables: calculating probabilities, expected values, variances. Probability distribution functions and cumulative distribution functions.

• Uniform and Normal continuous random variables.

• (2/11) Here is the schedule for important upcoming dates: The first midterm will be Wednesday, February 26. It will cover material up to the homework due on Friday, February 21. I have pushed back the due date for the first preliminary report for your projects to Friday, March 6. In the near future I will put up information about what to expect for the exam and this first preliminary report.

• (2/10) Next Monday, February 17, is a federal holiday. The university is closed and class will not be meeting.

• (1/29) If you are a resident of Hawaiʻi and want to vote in the presidential primary, important dates are coming up soon. The Hawaiʻi Republican party is not running a primary this year [link], but the Democratic party is. Starting this year the primary is conducted by mail-in ballots. To vote in the primary you must be a registered voter in the state and a member of the Democratic Party of Hawaiʻi. You can register to vote here and enroll in the party here. To receive an early ballot in the mail you must register and enroll by February 18. The final deadline to receive a ballot in the mail is March 8. In-person voting and same-day registration options will also be available on April 4.

• (1/16) UH is closed next Monday (1/20) in observence of Martin Luther King Day, so class will not be meeting.

## Written Homework

A subselection of the bolded problems will be graded. You must still do all problems for completion points.

• Week 2 (Due Friday, Jan 24) Section 2.1: 3, 5, 7; Section 2.2: 14, 17, 24.

• Week 3 (Due Friday, Jan 31) Section 2.3: 30, 33, 36; Section 2.4: 49, 52, 56, 57.

• Week 4 (Due Friday, Feb 7) Section 2.5: 73, 76, 86; Section 3.1: 2, 4, 5, 7; Section 3.2: 11, 18, 21.

• Week 5 (Due Friday, Feb 14 💘) Section 3.3: 32, 37, 44a, 45; Section 3.4: 46, 50, 62; Section 3.6: 81, 85.

• Week 6 (Due Friday, Feb 21) Section 4.1: 1, 5, 8; Section 4.2: 11, 14, 19, 21.

• Week 8 No homework.

• Week 9 (Due Friday, Mar 13) Section 5.4: 46, 47, 52, 56; Section 5.5: 58, 64, 72.

• Spring Break!

• Week 10 No homework. Canceled this week as we acclimate to the new set-up.

• Week 11 (Due Friday, Apr 3) Section 7.1: 1, 3, 5, 7. (Note: there will be ne WeBWorK homework this week. Expect it to return starting next week.)

• Week 12 (Due Friday, Apr 10) Section 7.2: 12, 13, 15; Section 7.3: 28, 34, 37.