Least squares modelling (2014)

From Statistics for Engineering
Revision as of 15:47, 11 February 2014 by Kevin Dunn (talk | contribs)
Jump to navigation Jump to search
Class date(s): 29 January 2014 to 13 February 2014
Nuvola mimetypes pdf.png (PDF) Course slides
Download video: Link (plays in Google Chrome) [119M]

Download video: Link (plays in Google Chrome) [304 M]

Download video: Link (plays in Google Chrome) [291 M]

Download video: Link (plays in Google Chrome) [216 M]

Download video: Link (plays in Google Chrome) [276 M]

Download video: Link (plays in Google Chrome) [239 M]

Course notes and slides

Date Class number Video and audio files Other materials Reading (PID) Slides
29 January 04C Video (119 M) Audio (18 M) None Chapter 4 Nuvola mimetypes pdf.png Slides for class
30 January 04D Video (304 M) Audio (42 M) See the source code below
03 February 05A Video (291 M) Audio (41 M) See the source code below
05 February 05B Video (216 M) Audio (42 M) None
06 February 05C Video (276 M) Audio (42 M) Practice problems (with partial solutions)
10 February 06A Video (239 M) Audio (43 M) None

Software source code

Take a look at the software tutorial, particularly steps 16 to 22.

Code used in class

Least squares demo

x <- c(10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5)
y <- c(8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68)
plot(x,y)
model.ls <- lm(y ~ x)
summary(model.ls)

coef(model.ls)
confint(model.ls)

names(model.ls)
model.ls$resisduals
resid(model.ls)

plot(x, y)
abline(model.ls)