Intro Stats
4th Edition
ISBN: 9780321826275
Author: Richard D. De Veaux
Publisher: PEARSON
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Chapter 7, Problem 35E
a.
To determine
Identify the misinterpretation for given statement to predict bird’s wingspan from bird’s height.
b.
To determine
Identify the misinterpretation for given statement to predict bird’s wingspan from bird’s height.
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Researchers are interested in predicting the height of a child based on the heights of their mother
and father. Data were collected, which included height of the child (height ), height of the mother (
mothersheight), and height of the father (fathersheight ). The initial analysis used the heights of the
parents to predict the height of the child (all units are inches). The results of the analysis, a multiple
regression, are presented below.
.
regress height mothersheight fathersheight
Source
Model
Residual
Total
height
mothersheight
fathersheight
_cons
SS
df
208.008457
314.295372 37
2 104.004228
8.49446952
MS
522.303829 39 13.3924059
Coef. Std. Err.
.6579529 .1474763
.2003584 .1382237
9.804327 12.39987
t P>|t|
4.46 0.000
C 0.156
0.79 0.434
Number of obs =
F( 2, 37) =
Prob > F
R-squared
Adj R-squared
Root MSE
=
.3591375
-.0797093
-15.32021
=
40
12.24
0.0001
0.3983
0.3657
2.9145
[95% Conf. Interval]
.9567683
.4804261
34.92886
What is the predicted height for a child born to a mother…
Researchers are interested in predicting the height of a child based on the heights of their mother
and father. Data were collected, which included height of the child (height ), height of the mother (
mothersheight ), and height of the father (fathersheight ). The initial analysis used the heights of the
parents to predict the height of the child (all units are inches). The results of the analysis, a multiple
regression, are presented below.
.
regress height mothersheight fathersheight
Source
Model
Residual
Total
height
mothersheight
fathersheight
_cons
SS
208.008457
314.295372
522.303829
df
2
37
Interpret the intercept of this model.
104.004228
8.49446952
Coef. Std. Err.
MS
39 13.3924059
.6579529 .1474763
.2003584 .1382237
9.804327 12.39987
t P>|t|
4.46 0.000
C 0.156
0.79 0.434
Number of obs =
F( 2,
37) =
Prob > F
R-squared
Adj R-squared =
Root MSE
40
12.24
0.0001
0.3983
0.3657
2.9145
[95% Conf. Interval]
.3591375
-.0797093
-15.32021
.9567683
.4804261
34.92886
Researchers are interested in predicting the height of a child based on the heights of their mother
and father. Data were collected, which included height of the child (height ), height of the mother (
mothersheight), and height of the father (fathersheight ). The initial analysis used the heights of the
parents to predict the height of the child (all units are inches). The results of the analysis, a multiple
regression, are presented below.
.
regress height mothersheight fathersheight
Source
Model
Residual
Total
height
mothersheight
fathersheight
_cons
SS
208.008457
314.295372
522.303829
df
104.004228
2
37 8.49446952
MS
39 13.3924059
Coef. Std. Err.
.6579529 .1474763
.2003584 .1382237
9.804327 12.39987
Interpret the slope associated with mother's height.
t P>|t|
4.46 0.000
с 0.156
0.79 0.434
Number of obs =
F( 2,
37) =
Prob > F
R-squared
Adj R-squared =
Root MSE
=
=
.3591375
-.0797093
-15.32021
=
40
12.24
0.0001
0.3983
0.3657
2.9145
[95% Conf. Intervall
9567683
.4804261
34.92886
Chapter 7 Solutions
Intro Stats
Ch. 7.4 - A scatterplot of house Price (in dollars) vs....Ch. 7.4 - A scatterplot of house Price (in dollars) vs....Ch. 7.4 - A scatterplot of house Price (in dollars) vs....Ch. 7.4 - A scatterplot of house Price (in dollars) vs....Ch. 7.4 - A scatterplot of house Price (in dollars) vs....Ch. 7.4 - Prob. 6JCCh. 7.6 - Prob. 7JCCh. 7.6 - Prob. 8JCCh. 7.6 - Prob. 9JCCh. 7 - True or false If false, explain briefly. a) We...
Ch. 7 - True or false II If false, explain briefly. a)...Ch. 7 - Prob. 3ECh. 7 - Prob. 4ECh. 7 - Bookstore sales revisited Recall the data we saw...Ch. 7 - Prob. 6ECh. 7 - Prob. 7ECh. 7 - Prob. 8ECh. 7 - Bookstore sales once more Here are the residuals...Ch. 7 - Prob. 10ECh. 7 - Prob. 11ECh. 7 - Prob. 12ECh. 7 - Prob. 13ECh. 7 - 14. Disk drives last time Here is a scatterplot of...Ch. 7 - Prob. 15ECh. 7 - Prob. 16ECh. 7 - More cereal Exercise 15 describes a regression...Ch. 7 - Prob. 18ECh. 7 - Another bowl In Exercise 15, the regression model...Ch. 7 - Prob. 20ECh. 7 - Cereal again The correlation between a cereals...Ch. 7 - Prob. 22ECh. 7 - Prob. 23ECh. 7 - Prob. 24ECh. 7 - Prob. 25ECh. 7 - Prob. 26ECh. 7 - Prob. 27ECh. 7 - Residuals Tell what each of the residual plots...Ch. 7 - Real estate A random sample of records of home...Ch. 7 - 30. Roller coaster The Mitch Hawker poll ranked...Ch. 7 - Prob. 31ECh. 7 - Prob. 32ECh. 7 - Real estate again The regression of Price on Size...Ch. 7 - Prob. 34ECh. 7 - Prob. 35ECh. 7 - More misinterpretations A Sociology student...Ch. 7 - Real estate redux The regression of Price on Size...Ch. 7 - 38. Another ride The regression of Duration of a...Ch. 7 - Prob. 39ECh. 7 - Prob. 40ECh. 7 - Prob. 41ECh. 7 - Prob. 42ECh. 7 - Prob. 43ECh. 7 - Prob. 44ECh. 7 - Prob. 45ECh. 7 - 46. Second inning 2010 Consider again the...Ch. 7 - Prob. 47ECh. 7 - Prob. 48ECh. 7 - Prob. 49ECh. 7 - Prob. 50ECh. 7 - Online clothes An online clothing retailer keeps...Ch. 7 - Online clothes II For the online clothing retailer...Ch. 7 - Prob. 53ECh. 7 - Success in college Colleges use SAT scores in the...Ch. 7 - SAT, take 2 Suppose we wanted to use SAT math...Ch. 7 - Prob. 56ECh. 7 - Prob. 57ECh. 7 - Prob. 58ECh. 7 - Prob. 59ECh. 7 - Drug abuse revisited Chapter 6, Exercise 42...Ch. 7 - Prob. 61ECh. 7 - Prob. 62ECh. 7 - Prob. 63ECh. 7 - 64. Chicken Chicken sandwiches are often...Ch. 7 - Prob. 65ECh. 7 - Prob. 66ECh. 7 - Prob. 67ECh. 7 - Prob. 68ECh. 7 - Prob. 69ECh. 7 - 70. Birthrates 2009 The table shows the number of...Ch. 7 - Prob. 71ECh. 7 - Prob. 72ECh. 7 - Prob. 73ECh. 7 - Prob. 74ECh. 7 - Hard water In an investigation of environmental...Ch. 7 - 76. Gators Wildlife researchers monitor many...Ch. 7 - Prob. 77ECh. 7 - Least squares Consider the four points (200,1950),...
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- Olympic 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_forwardFind the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardWhat does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_forward
- Find the mean hourly cost when the cell phone described above is used for 240 minutes.arrow_forwardResearchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child (height ), height of the mother ( mothersheight ), and height of the father ( fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS df 208.008457 314.295372 37 522.303829 2 104.004228 8.49446952 MS 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 Calculate the test statistic that is labeled "C" in the output. t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, Prob > F 37) = R-squared Adj R-squared = Root MSE 40 12.24 0.0001 = 0.3983 0.3657 2.9145 .3591375 -.0797093 -15.32021 = [95% Conf. Intervall .9567683 .4804261…arrow_forwardA doctor wanted to determine whether there is a relation between a male’s age and his HDL (so-called good cholesterol). He randomly selected 9 of his patients and determined their HDL cholesterol. The data is reported in the table below: a) Compute the regression equation for HDL as a function of age. b)Interpret the meaning of the regression parameters.arrow_forward
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