Introduction to Probability and Statistics
14th Edition
ISBN: 9781133103752
Author: Mendenhall, William
Publisher: Cengage Learning
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Chapter 13.5, Problem 13.19E
i.
To determine
To explain: identifyingtwo variables that are quantitative.
ii.
To determine
To identify: two possible prediction equations.
iii.
To determine
To graph: The
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A regression between foot length(response variable in cm) and height (eexplanatory variable in inches) for 33 students resulted in the following regression equation:
y^=10,9+0,23X
one student in the sample was 73 inches tall with a foot length of 29cm.What is the predicted foot length for
A)33cm
B)17,57cm
C)27,69cm
D)29cm
Given are five observations for two variables, x and y.
xi
1
2
3
4
5
yi
4
6
6
11
13
Develop the estimated regression equation by computing the values of
b0
and
b1
using
b1 =
Σ(xi − x)(yi − y)
Σ(xi − x)2
and
b0 = y − b1x.
ŷ =
(e)
Use the estimated regression equation to predict the value of y when
x = 2.
The estimated regression equation for a model involving two independent variables and 10 observations follows.
Y=25.7067 + 0.2795x1 + 0.7337x2
A. Interpret b1 and b2 in this estimated trgression equation.
B1 = ?
B2 = ?
Thank you
Chapter 13 Solutions
Introduction to Probability and Statistics
Ch. 13.4 - Prob. 13.1ECh. 13.4 - Prob. 13.2ECh. 13.4 - Suppose that you fit the model E(y)=0+1x1+2x2+3x3...Ch. 13.4 - Prob. 13.4ECh. 13.4 - Prob. 13.5ECh. 13.4 - Prob. 13.6ECh. 13.4 - Prob. 13.7ECh. 13.4 - Prob. 13.8ECh. 13.4 - Prob. 13.9ECh. 13.4 - College Textbooks A publisher of college textbooks...
Ch. 13.4 - Prob. 13.11ECh. 13.4 - Prob. 13.12ECh. 13.4 - Prob. 13.13ECh. 13.4 - Prob. 13.14ECh. 13.4 - Prob. 13.15ECh. 13.4 - Prob. 13.16ECh. 13.5 - Prob. 13.17ECh. 13.5 - Prob. 13.18ECh. 13.5 - Prob. 13.19ECh. 13.5 - Prob. 13.20ECh. 13.5 - Prob. 13.21ECh. 13.5 - Prob. 13.22ECh. 13.5 - Prob. 13.23ECh. 13.5 - Construction Projects In a study to examine the...Ch. 13 - Prob. 13.25SECh. 13 - Prob. 13.26SECh. 13 - Prob. 13.28SECh. 13 - Prob. 13.29SECh. 13 - Prob. 13.30SECh. 13 - Prob. 13.31SECh. 13 - Prob. 13.32SECh. 13 - Prob. 13.33SECh. 13 - Quality Control A manufacturer recorded the number...Ch. 13 - Prob. 13.35SECh. 13 - Prob. 13.36SE
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- The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.arrow_forwardOlympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forwardLife Expectancy The following table shows the average life expectancy, in years, of a child born in the given year42 Life expectancy 2005 77.6 2007 78.1 2009 78.5 2011 78.7 2013 78.8 a. Find the equation of the regression line, and explain the meaning of its slope. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 2019? e. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 1580?2300arrow_forward
- A multiple linear regression model based on a sample of 13 weeks is developed to predict standby hours based on the total staff present and remote hours. The SSR is 23,638.17 and the SSE is 33,273.99. c. Compute the coefficient of multiple determination, r2, and interpret its meaning. (Round to four decimal places as needed.)arrow_forwardIn a regression model involving 30 observations, the following estimated regression equation was obtained.ŷ = 170 + 34x1 – 3x2 + 8x3 + 58x4 + 3x5For this model, SSR = 1,740 and SST = 2,000.The degrees of freedom associated with SSE are _____. a. 6 b. 5 c. 24 d. 19arrow_forward
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