Click on the text like “Week 1: Jan 22 – 25” to expand or collapse the items we covered in that week.

I will fill in more detail and provide links to lecture notes and labs as we go along. Items for future dates are tentative and subject to change.

  • Introduction; review of topics from probability; properties of the sample mean for a simple random sample from a finite populaiton

Wed, Jan 23

  • In class, we will work on: Overview and review
    • Discuss syllabus, overview of course
    • Review of prerequisites
      • Topics from probability: pdf (also posted on resources page)
      • Practice examples for probability: pdf
      • Common probability distributions: pdf (also posted on resources page; you don’t need to memorize this material)
      • Topics from calculus (not discussed today, please review after class): pdf (also posted on resources page)
  • After class, please do the following:
    • Register for GitHub here if you haven’t already; I will ask you to provide your GitHub user name in the questionairre below.
    • Fill out a brief questionnairre
    • Fill out this brief poll about when my office hours should be held
    • Sign up for our class at Piazza (anonymous question and answer forum): https://piazza.com/mtholyoke/spring2019/stat343
    • Reading
      • Review the prerequisite material from probability and calculus. If there are any topics from probability or calculus that you don’t have memorized, please start memorizing them. This will make the rest of this class go much more smoothly for you. If you have any questions on this material, please don’t hesitate to ask me in office hours or on Piazza. I am happy to help with this material.
      • Over the next few classes we will cover material from Sections 7.1 - 7.3 of the text. Give those sections in the book a brief skim.
    • Homework 0 due Wed, Jan 30

Fri, Jan 25

  • In class, we will work on: Sections 7.1, 7.2, 7.3.1, and 7.3.2 (mostly lecture)
    • Last two probability examples from last class
    • Briefly talk through the “common probability distributions” handout
    • Motivating example for Chapter 7: pdf
    • Definitions from Chapter 7 (some on motivating example handout): population parameter, statistic, estimator, estimate, simple random sample, sampling distribution, bias, variance, mean squared error
    • Start on some derivations of the expected value, variance, and MSE of the sample mean based on a simple random sample from a finite population.
    • If time (unlikely), start on estimating the population variance based on a simple random sample from a finite population.
    • Lecture notes for Sections 7.1, 7.2, 7.3.1, and 7.3.2: pdf
    • Set up git on RStudio (if you took Stat 340 with me, you can leave early)
  • After class, please do the following:
    • Start memorizing any definitions from today’s class that you don’t know yet.
    • Problem Set 0 due Wed, Jan 30

Section 7.3.3 (see Stat 344SM, survey sampling, for the rest of Chapter 7 and much more); start on Chapter 8

Mon, Jan 28

  • In Class, we will work on: Sections 7.1, 7.2, 7.3.1, and 7.3.2 (mostly lecture, some worked examples)
    • Finish stuff from Sections 7.2, 7.3.1, and 7.3.2 that we didn’t get to on Friday.
  • After class, please do the following:
    • Problem Set 0 due Wed, Jan 30

Wed, Jan 30

  • In Class, we will work on: Section 5.3 and part of 7.3.3 (mostly lab)
    • Central Limit Theorem, more on the sampling distribution of the sample mean
    • Lab 1 on GitHub
    • Topics from R:
      • for loops: pdf
      • dplyr::sample_n
  • After class, please do the following:
    • Problem Set 0 due Fri, Feb 1

Fri, Feb 1

  • Problem Set 0 due today!!
  • In Class, we will work on: Section 7.3.3
    • Mean Squared Error = Bias\(^2\) + Variance. Lecture notes: pdf
    • Finish lab from last time
  • After class, please do the following:
    • Problem Set 1 due Fri, Feb 15

Mon, Feb 4

  • In Class, we will work on: Section 7.3.3
    • A first look at confidence intervals for a population mean based on a simple random sample. pdf (not on the lecture notes but discussed in class are the coverage probability and nominal coverage probability of a confidence interval)
    • R topics: Logical operations on vectors, mutate and summarize pdf
    • Lab 2: confidence intervals for simple random sampling
  • After class, please do the following:
    • Problem Set 1 due Fri, Feb 15

Wed, Feb 6

  • In Class, we will work on: Finish Section 7.3.3
    • Finish confidence intervals lab from last class
    • If you finish early, free time or work on homework
  • After class, please do the following:
    • Problem Set 1 due Fri, Feb 15

Fri, Feb 8

  • In Class, we will work on: Start on Sections 8.1 through 8.5
    • Introductory worksheet, friend or foe: pdf
    • Some plots to go with the introductory worksheet: pdf
      • Rmd file in case you want to see code for making the plots: Rmd
    • Defined the likelihood function, log-likelihood function, maximum likelihood estimator, maximum likelihood estimate
    • Argued that the parameter value that maximizes the log-likelihood also maximizes the likelihood, since log is an increasing function.
    • Reviewed some calculus related to finding a maximum of a univariate function
  • After class, please do the following:
    • Problem Set 1 due Fri, Feb 15

Mon, Feb 11

  • In Class, we will work on: Sections 8.1 through 8.5
    • Finish first example of maximum likelihood from last class
    • Second example: pdf Solutions: pdf. See also R part and solutions posted as lab 3 on GitHub.

Wed, Feb 13

  • In Class, we will work on: Sections 8.1 through 8.5
    • Maximum likelihood for models with more than 1 parameter.
    • Example of normal distribution. Lecture notes: pdf
    • Example of simple linear regression, variance known
      • Handout: pdf
      • Solutions: pdf. Solutions to R part posted on GitHub.

Fri, Feb 15

  • In Class, we will work on: Sections 8.1 through 8.5
    • Wrap up examples from last class
      • Talk through the likelihood for the normal distribution model with 2 unknown parameters: html Rmd
      • Highlights from solutions for simple linear regression handout, posted for Wed, Feb 13
    • Introduce methods for numerical maximization of functions.

Mon, Feb 18

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    • Numerical maximimization of the likelihood function

Wed, Feb 20

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Fri, Feb 22

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Mon, Feb 25

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Wed, Feb 27

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Fri, Mar 1

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Mon, Mar 4

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Wed, Mar 6

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Fri, Mar 8

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Spring Break: safe travels!

Mon, Mar 18

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Wed, Mar 20

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Fri, Mar 22

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Mon, Mar 15

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Wed, Mar 27

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Fri, Mar 29

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Mon, Apr 1

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Tue, Apr 3

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Fri, Apr 5

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Mon, Apr 8

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Wed, Apr 10

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Fri, Apr 12

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Mon, Apr 15

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Wed, Apr 17

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Fri, Apr 19

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Mon, Apr 22

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Wed, Apr 24

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Fri, Apr 26

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Mon, Apr 29

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We will have a cumulative final exam.