EBook Problems GLM Regress - Revision history

Tdlee: Text replacement - "{{translate|pageName=http://wiki.stat.ucla.edu/socr/" to ""{{translate|pageName=http://wiki.socr.umich.edu/"

2020-03-03T19:24:09Z

Text replacement - "{{translate|pageName=http://wiki.stat.ucla.edu/socr/" to ""{{translate|pageName=http://wiki.socr.umich.edu/"

JayZzz at 01:35, 8 January 2009

2009-01-08T01:35:57Z

JayZzz at 21:43, 30 December 2008

2008-12-30T21:43:50Z

JayZzz at 21:39, 30 December 2008

2008-12-30T21:39:39Z

JayZzz at 20:24, 30 December 2008

2008-12-30T20:24:35Z

JayZzz at 19:59, 30 December 2008

2008-12-30T19:59:47Z

JayZzz: /* Problem 4 */

2008-11-27T08:25:16Z

‎Problem 4

JayZzz: New page: == EBook Problems Set - Regression== ===Problem 1=== Use the information from the Heights of Fathers and Sons to write t...

2008-11-27T08:24:32Z

New page: == EBook Problems Set - Regression== ===Problem 1=== Use the information from the Heights of Fathers and Sons to write t...

New page

==[[EBook_Problems | EBook Problems Set]] - [[AP_Statistics_Curriculum_2007_GLM_Regress |Regression]]==

===Problem 1===
Use the information from the Heights of Fathers and Sons to write the linear model that best predicts the height of the son from the height of the father.

*Choose one answer.

:''(a) Son's height = 35 + 0.5*Father's height'''

:''(b) Son's height = 1.00 + 1.00* Father's height''

:''(c) The model cannot be determined without the actual data''

:''(d) Son's height = 0.5 + 35*Father's height''
{{hidden|Answer|(a)}}

===Problem 2===
A congressional report investigates the relationship between income of parents and educational attainment of their daughters. Data are from a sample of families with daughters age 18-24. Average parental income is $29,300, average educational attainment of the daughters is 13.1 years of schooling completed, and the correlation is 0.37.

The regression line for predicting daughter’s education from parental income is reported as: Predicted education = 0.000617*(income) + 8.1

Is the following statement true or false? "The above line is the regression line to predict education from income."

:''(a)True.''

:''(b)False.''
{{hidden|Answer|(a)}}

===Problem 3===
In the early 1900's when Francis Galton and Karl Pearson measured 1078 pairs of fathers and their grown-up sons, they calculated that the mean height for fathers was about 68 inches with deviation of 3 inches. For their sons, the mean height was 69 inches with deviation of 3 inches. (The actual numbers are slightly smaller, but we will work with these values to keep the calculations simple.) The correlation coefficient was 0.50. Use the information to calculate the slope of the linear model that predicts the height of the son from the height of the father.

*Choose one answer.

:''(a) 0.50''

:''(b) The slope cannot be determined without the actual data''

:''(c) 35.00''

:''(d) 3/3 = 1.00''
{{hidden|Answer|(a)}}

===Problem 4===
Suppose that wildlife researchers monitor the local alligator population by taking aerial photograhs on a regular schedule. They determine that the best fitting linear model to predict weight in pounds from the length of the gators inches is:

'''Weight = -393 + 5.9*Length with r2 = 0.836.'''

'''Which of the following statements is true?'''

*Choose one answer.

:''(a) A gator that is about 10 inches above average in length is about 59 pounds above the average weight of these gators.''

:''(b) The correlation between a gator's length and weight is 0.836.''

:''(c) The correlation between a gator's height and weight cannot be determined without the actual data.''

:''(d) The correlation between a gator's height and weigth is about -0.914.''
{{hidden|Answer|(a)}}

<hr>
* [[AP_Statistics_Curriculum_2007_GLM_Regress | Back to Ebook]]
* SOCR Home page: http://www.socr.ucla.edu

{{translate|pageName=http://wiki.stat.ucla.edu/socr/index.php?title=EBook_Problems_GLM_Regress}}

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 :''(d) No, because this means that the relation between health and exercise of the elderly is non-linear, so a linear model should not be fitted
 {{hidden|Answer|(d)}}
 <hr>

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 :''(d) response variable
 {{hidden|Answer|(a) , (d)}}
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 :''(d) There is a positive correlation between the rise in temperature and the sale of popsicles.
 {{hidden|Answer|(c) , (d)}}
 <hr>

← Older revision		Revision as of 20:24, 30 December 2008
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		+	===Problem 10===
		+	An ice cream truck owner collects data on the number of sales made each day and the average temperature that day. He computes a regression line for predicting the number of sales based on how far the daily temperature is from freezing (32 degrees Fahrenheit) and finds sales = 0.22 + 1.8*(degrees over 32 Fahrenheit). Identify the y-intercept.
		+
		+	*Choose one answer.
		+
		+	:''(a) We can't tell from the information given
		+
		+	:''(b) 32
		+
		+	:''(c) 0.22
		+
		+	:''(d) 1.8
		+	{{hidden\|Answer\|(c)}}
		+
		+	===Problem 11===
		+	Find the regression equation for predicting final score from midterm score, based on the following information:
		+
		+	Average midterm score=70, SD=10
		+	Average final score=55, SD=20
		+	r=0.60
		+
		+	*Choose one answer
		+
		+	:''(a) Predicted final score=1.2*(midterm score)-29
		+
		+	:''(b) Predicted final score=1.2*(midterm score)-34
		+
		+	:''(c) Predicted final score=0.3*(midterm score)-34
		+
		+	:''(d) Predicted final score=0.3*(midterm score)-29
		+	{{hidden\|Answer\|(a)}}
		+
		+	===Problem 12===
		+	The scores of midterm and final exams for a random sample of Stats 10 students can be summarized as follows:
		+
		+	Mean of midterm score = 36.92; SD of midterm score = 37.79
		+	Mean of final score = 24.71; SD of final score= 25.21
		+	r= 0.978
		+
		+	Predict the final score for a student that got a midterm score of 35.
		+
		+	*Choose one answer.
		+
		+	:''(a) 23.44
		+
		+	:''(b) 0.62
		+
		+	:''(c) 25.21
		+
		+	:''(d) 35
		+	{{hidden\|Answer\|(a)}}
		+
		+	===Problem 13===
		+	A popsicle truck owner collects data to predict the number of sales made each day (Y) from the average temperature of that day (in Fahrenheit) (X) . He finds that the regression line is "Predicted Y = 0.26 + 1.8 X" . What does the 1.8 mean?
		+
		+	*Choose at least one answer.
		+
		+	:''(a) The correlation between the temperature and the sale of the popsicles is 1.8.
		+
		+	:''(b) The intercept of the regression line would be 1.8.
		+
		+	:''(c) On average, the truck driver sells 1.8 more popsicles on days that are 1 degree warmer than today.
		+
		+	:''(d) There is a positive correlation between the rise in temperature and the sale of popsicles.
		+	{{hidden\|Answer\|(c) , (d)}}

	<hr>		<hr>

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 :''(d) The correlation between a gator's height and weigth is about -0.914.''
 {{hidden|Answer|(a)}}
 <hr>
-* [[AP_Statistics_Curriculum_2007_GLM_Regress | Back to Ebook]]
+* [[EBook | Back to Ebook]]
 * SOCR Home page: http://www.socr.ucla.edu
-{{translate|pageName=http://wiki.stat.ucla.edu/socr/index.php?title=EBook_Problems_GLM_Regress}}
+{{translate|pageName=http://wiki.stat.ucla.edu/socr/index.php/EBook_Problems_GLM_Regress}}

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 Suppose that wildlife researchers monitor the local alligator population by taking aerial photograhs on a regular schedule. They determine that the best fitting linear model to predict weight in pounds from the length of the gators inches is:
-'''Weight = -393 + 5.9*Length with r2 = 0.836.'''
+Weight = -393 + 5.9*Length with r2 = 0.836.
-'''Which of the following statements is true?'''
+Which of the following statements is true?
 *Choose one answer.