Connect Hosted by ALEKS Online Access for Elementary Statistics
3rd Edition
ISBN: 9781260373769
Author: William Navidi
Publisher: MCGRAW-HILL HIGHER EDUCATION
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Chapter 13.3, Problem 19E
a.
To determine
To find:The regression equation for the data.
b.
To determine
To find: The value of variable
c.
To determine
To find: The confidence interval.
d.
To determine
To find: The prediction interval.
e.
To determine
To find: The percentage of variation in variable
f.
To determine
To find:Whether the given model is useful for prediction.
g.
To determine
To explain:The test for the hypothesis
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Students have asked these similar questions
a. what is the equation of the regression line?
b. interpret r of the regression line
c. interpret r^2 of the regression line
L1 - 4,15,12,11,8,6,7,2,7,14,20,3,13
L2 - 120,200,140,110,120,80,190,100,120,190,190,110,120
Does the Regression line give information about all the data points in the data set? Does the Regression line usually have all the points in the data set on it?
The new manager of an Information Technology company collected data for a sample of 20 computer programmers in the organization to perform a multiple regression analysis on the structure of their salaries. The aim of this manager in this exercise is to determine if the Salary (y) of a hired computer programmer was related to the years of Experience (??) in the organization and also the Score (??) of the programmers during their first interview aptitude test scores. The years of experience, score on the aptitude test and the corresponding annual salary (in thousands of Ghana cedis) for a sample of the 20 programmers is shown in the Regression statistics table below;
Experience (??)
(in years)
Score (??) (out of 100%)
Salary (y)
(GH¢ 000)
4
78
24
7
100
43
1
86
23.7
5
82
34.3
8
86
35.8
10
84
38
0
75
22.2
1
80
23.1
6
83
30
6
91
33
9
88
38
2
73
26.6
10
75
36.2
5
81
31.6…
Chapter 13 Solutions
Connect Hosted by ALEKS Online Access for Elementary Statistics
Ch. 13.1 - Prob. 7ECh. 13.1 - Prob. 8ECh. 13.1 - In Exercises 9 and 10, determine whether the...Ch. 13.1 - Prob. 10ECh. 13.1 - Prob. 11ECh. 13.1 - Prob. 12ECh. 13.1 - Prob. 13ECh. 13.1 - Prob. 14ECh. 13.1 - Prob. 15ECh. 13.1 - Prob. 16E
Ch. 13.1 - Prob. 17ECh. 13.1 - Prob. 18ECh. 13.1 - Prob. 19ECh. 13.1 - Prob. 20ECh. 13.1 - Prob. 21ECh. 13.1 - Prob. 22ECh. 13.1 - Prob. 23ECh. 13.1 - Prob. 24ECh. 13.1 - Prob. 25ECh. 13.1 - Prob. 26ECh. 13.1 - Prob. 27ECh. 13.1 - Prob. 28ECh. 13.1 - Prob. 26aECh. 13.1 - Calculator display: The following TI-84 Plus...Ch. 13.1 - Prob. 28aECh. 13.1 - Prob. 29ECh. 13.1 - Prob. 30ECh. 13.1 - Confidence interval for the conditional mean: In...Ch. 13.2 - Prob. 3ECh. 13.2 - Prob. 4ECh. 13.2 - Prob. 5ECh. 13.2 - Prob. 6ECh. 13.2 - Prob. 7ECh. 13.2 - Prob. 8ECh. 13.2 - Prob. 9ECh. 13.2 - Prob. 10ECh. 13.2 - Prob. 11ECh. 13.2 - Prob. 12ECh. 13.2 - Prob. 13ECh. 13.2 - Prob. 14ECh. 13.2 - Prob. 15ECh. 13.2 - Prob. 16ECh. 13.2 - Prob. 17ECh. 13.2 - Dry up: Use the data in Exercise 26 in Section...Ch. 13.2 - Prob. 19ECh. 13.2 - Prob. 20ECh. 13.2 - Prob. 21ECh. 13.3 - Prob. 7ECh. 13.3 - Prob. 8ECh. 13.3 - Prob. 9ECh. 13.3 - In Exercises 9 and 10, determine whether the...Ch. 13.3 - Prob. 11ECh. 13.3 - Prob. 12ECh. 13.3 - Prob. 13ECh. 13.3 - For the following data set: Construct the multiple...Ch. 13.3 - Engine emissions: In a laboratory test of a new...Ch. 13.3 - Prob. 16ECh. 13.3 - Prob. 17ECh. 13.3 - Prob. 18ECh. 13.3 - Prob. 19ECh. 13.3 - Prob. 20ECh. 13.3 - Prob. 21ECh. 13.3 - Prob. 22ECh. 13.3 - Prob. 23ECh. 13 - A confidence interval for 1 is to be constructed...Ch. 13 - A confidence interval for a mean response and a...Ch. 13 - Prob. 3CQCh. 13 - Prob. 4CQCh. 13 - Prob. 5CQCh. 13 - Prob. 6CQCh. 13 - Construct a 95% confidence interval for 1.Ch. 13 - Prob. 8CQCh. 13 - Prob. 9CQCh. 13 - Prob. 10CQCh. 13 - Prob. 11CQCh. 13 - Prob. 12CQCh. 13 - Prob. 13CQCh. 13 - Prob. 14CQCh. 13 - Prob. 15CQCh. 13 - Prob. 1RECh. 13 - Prob. 2RECh. 13 - Prob. 3RECh. 13 - Prob. 4RECh. 13 - Prob. 5RECh. 13 - Prob. 6RECh. 13 - Prob. 7RECh. 13 - Prob. 8RECh. 13 - Prob. 9RECh. 13 - Prob. 10RECh. 13 - Air pollution: Following are measurements of...Ch. 13 - Icy lakes: Following are data on maximum ice...Ch. 13 - Prob. 13RECh. 13 - Prob. 14RECh. 13 - Prob. 15RECh. 13 - Prob. 1WAICh. 13 - Prob. 2WAICh. 13 - Prob. 1CSCh. 13 - Prob. 2CSCh. 13 - Prob. 3CSCh. 13 - Prob. 4CSCh. 13 - Prob. 5CSCh. 13 - Prob. 6CSCh. 13 - Prob. 7CS
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- The new manager of an Information Technology company collected data for a sample of 20 computer programmers in the organization to perform a multiple regression analysis on the structure of their salaries. The aim of this manager in this exercise is to determine if the Salary (y) of a hired computer programmer was related to the years of Experience (??) in the organization and also the Score (??) of the programmers during their first interview aptitude test scores. The years of experience, score on the aptitude test and the corresponding annual salary (in thousands of Ghana cedis) for a sample of the 20 programmers is shown in the Regression statistics table below; Experience (??) (in years) Score (??) (out of 100%) Salary (y) (GH¢ 000) 4 78 24 7 100 43 1 86 23.7 5 82 34.3 8 86 35.8 10 84 38 0 75 22.2 1 80 23.1 6 83 30 6 91 33 9 88 38 2 73 26.6 10 75 36.2 5 81 31.6…arrow_forwardPlease do not give solution in image format thankuarrow_forwardLet’s assume a particular sample of Math classes contains several student GPAs from a minimum of 3.3 and a maximum of 4.0. The researcher would like to use a regression line to make estimations for future students who take this class and eventually graduate. Another student in a History class whose GPA is 3.1 would also like to use this regression line. (a) Can this regression line be used for the History student? Explain.arrow_forward
- when a regression is used as a method of predicting dependent variables from one or more independent variables. How are the independent variables different from each other yet related to the dependent variable?arrow_forwardA regression was run to determine if there is a relationship between the happiness index (y) and lifeexpectancy in years of a given country (x). The results of the regression were: y^=a+bx ; a=-0.423 ,b=0.07 a. Write the equation of the Least Squares Regression line.b. Find the value for the correlation coefficient, r?c. If a country increases its life expectancy, the happiness index will Increase or decrease ( circleone)d. If the life expectancy is increased by 1 year in a certain country, how much will the happinessindex change? Round to two decimal places.e. Use the regression line to predict the happiness index of a country with a life expectancy of 85years. Round to two decimal places.-arrow_forwardDescribe about how to place a regression line?arrow_forward
- Write a simple linear regression model with the total number of wins as the response variable and the average points scored as the predictor variable. Also, find the: 1 Null Hypothesis (statistical notation and its description in words) 2 Alternative Hypothesis (statistical notation and its description in words)arrow_forwardThe table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Studying 2 2.5 3 3.5 4 4.5 5 Midterm Grades 72 74 80 82 87 88 93 Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the value of the independent variable is increased by one unit, then find the change in the dependent variable yˆy^.arrow_forwardThe table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Studying 2 2.5 3 3.5 4 4.5 5 Midterm Grades 72 74 80 82 87 88 93 Find the value of the coefficient of determination. Round your answer to three decimal places.arrow_forward
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