Assignment 3 Problems with Solutions - Probability and Statistics | MATH 243, Assignments of Probability and Statistics

Material Type: Assignment; Professor: Moseley; Class: + Dis >4; Subject: Mathematics; University: University of Oregon; Term: Summer 2008;

Typology: Assignments

Pre 2010

Uploaded on 07/23/2009

koofers-user-j6s
koofers-user-j6s 🇺🇸

10 documents

1 / 2

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
Worksheet 3 8/25/08
Problem 1. Children aged 3 to 8, who were randomly surveyed, were asked their ages and
were measured for height (in inches). Here is the data:
age height
3 36
3 38
4 37
4 40
5 39
6 42
6 45
7 40
8 46
(a) Make a scatterplot of these data
(b) Before calculating anything, describe the pattern of the data and decide if there is a
positive or negative association.
The pattern looks linear with a positive association.
(c) Calculate the correlation of the data.
r=.8201
(d) Calculate and plot the least-squares regression line for this data.
ˆy= 32.19 + 1.59x
(e) What is the residual for the observation (7,40)
yˆy= 40 (32.19 + 1.59(7)) = 40 43.32 = 3.32
(f) From this data, predict the height of a 50 year old. (is it wise to do so?)
ˆy= 32.19 + 1.59(50) = 111.69in
This seems unwise as extrapolation can yield some strange results.
pf2

Partial preview of the text

Download Assignment 3 Problems with Solutions - Probability and Statistics | MATH 243 and more Assignments Probability and Statistics in PDF only on Docsity!

Worksheet 3 8/25/

Problem 1. Children aged 3 to 8, who were randomly surveyed, were asked their ages and were measured for height (in inches). Here is the data:

age height 3 36 3 38 4 37 4 40 5 39 6 42 6 45 7 40 8 46

(a) Make a scatterplot of these data

(b) Before calculating anything, describe the pattern of the data and decide if there is a positive or negative association. The pattern looks linear with a positive association. (c) Calculate the correlation of the data. r=. (d) Calculate and plot the least-squares regression line for this data.

ˆy = 32.19 + 1. 59 x (e) What is the residual for the observation (7,40)

y − yˆ = 40 − (32.19 + 1.59(7)) = 40 − 43 .32 = − 3. 32 (f) From this data, predict the height of a 50 year old. (is it wise to do so?)

yˆ = 32.19 + 1.59(50) = 111. 69 in This seems unwise as extrapolation can yield some strange results.

(g) A 55in tall 5 year old is added to the data. Is this influential to the correlation? Is this influential to the regression line? Adding the point (5,55) to the data drops the correlation to r = .4514 and changes the regression line to ˆy = 1. 53 x + 33.98. The correlation changes from strong to pretty weak, but the regression line doesn’t move much.