9/6/2023

Complete Chapter 1 - Simple Linear Regression - See Sec1.4-5.ppt

• Outliers
• Influential points
• Demonstration
  • See GoogleDrive\Stat206-DataAnalysis-F2023\2eMarkkdown\Sec1.4.Rmd
  • See GoogleDrive\Stat206-DataAnalysis-F2023\2eMarkkdown\Sec1.5.Rmd

Class Activity on Robbery Rates - see robbery.dat, robbery.csv, and robbery.R

• A criminologist studying the relationship between population density and robbery rate in medium-sized U.S. cities collected the data for a random sample of 16 cities. The data for X = population density of the city (number of people per unit area) and Y = the robbery rate last year (number of robberies per 100,000 people) can be found in robbery.dat.
  • Obtain the estimated regression line. Plot the estimated regression line and the data. Does the simple linear regression model appear to provide a good fit?
  • Obtain point estimates of the following:
    • the difference in the mean robbery rate in cities that differ by one unit in population density
    • the mean robbery rate last year in cities with population density X = 60
    • the random error term for the 10th observation
    • the variance of the error terms

In class exercises

• Complete exercises 1.34, 1.36, and 1.39

R Lab

• Transformation examples from the PPT slides

Please read Sections 2.1-2.3 for class on Friday.