ELEMENTARY STATISTICS-ALEKS ACCESS CODE
3rd Edition
ISBN: 9781265787219
Author: Navidi
Publisher: MCG
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Chapter 13, Problem 1RE
a.
To determine
To find:The regression equation for the data.
b.
To determine
To find: The confidence interval for the data.
c.
To determine
To find:Whether the weight is useful in predicting the mileage.
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Use the shoe print lengths and heights shown below to find the regression equation, letting shoe print lengths be the predictor (x) variable. Then find the best predicted height of a male who has a shoe print length of
28.5
cm. Would the result be helpful to police crime scene investigators in trying to describe the male? Use a significance level of
α=0.05.
Shoe Print (cm)
29.1
29.1
31.8
31.9
27.5
Foot Length (cm)
25.7
25.4
27.9
26.7
25.1
Height (cm)
175.4
177.8
185.2
175.4
173.2
The best predicted height is
enter your response here
cm.
(Round to two decimal places as needed.)
Would the result be helpful?
A.
No, because the description would be the same regardless of shoe print length.
B.
Yes, because the description would be based on an actual shoe print length.
C.
Yes, because the correlation is strong, so the predicted…
Use the pizza cost and the subway fare in the table below to find the regression equation, letting pizza cost be the predictor (x) variable. (Pizza cost is in dollars per slice, subway fare and CPI are
in dollars.) What is the best predicted subway fare when pizza costs $3.96 per slice? Use a significance level of 0.05.
Year
Pizza Cost
Subway Fare
CPI
1960 1973 1986 1995 2002 2003 2009 2013 2015 2019
0.151 0.350 1.000 1.249 1.753 2.001 2.247 2.304 2.747 2.999
0.152 0.349 0.998 1.351 1.503 2.003 2.250 2.551 2.752 2.746
29.6 44.4 109.6 152.4 180.0 184.0 214.5 233.0 237.0 252.2
The regression equation is ŷ = + (x.
(Round the y-intercept to four decimal places as needed. Round the slope to three decimal places as needed.)
A regression model to predict Y, the state burglary rate per 100,000 people, used the following four state predictors: X₁ = median age,
X₂ = number of bankruptcies per 1.000 population, X3 = federal expenditures per capita (a leading predictor), and X4 = high school
graduation percentage.
Click here for the Excel Data File
(a) Using the sample size of 50 people, calculate the calc and p-value in the table given below. (Negative values should be indicated
by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Round your answers to 4 decimal places.)
Predictor
Intercept
AgeMed
Bankrupt
FedSpend
HSGrad%
Answer is complete but not entirely correct.
*calc
5.2526
-2.1764✔✔
1.4101✔
Coefficient
4,198.5808
-27.3540
17.4893
-0.0124
-29.0314
SE
799.3395
12.5687
12.4033
0.0176
7.1268
-0.7045
-4.0736
p-value
0.0000
0.0348
0.2935
0.4848
0.0002
Chapter 13 Solutions
ELEMENTARY STATISTICS-ALEKS ACCESS CODE
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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- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardA regression model to predict Y, the state burglary rate per 100,000 people, used the following four state predictors: X₁ = median age, X₂ = number of bankruptcies per 1,000 population, X3 = federal expenditures per capita (a leading predictor), and X4 = high school graduation percentage. Click here for the Excel Data File (a) Using the sample size of 50 people, calculate the tcalc and p-value in the table given below. (Negative values should be indicated by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Round your answers to 4 decimal places.) Predictor Intercept AgeMed Bankrupt FedSpend HSGrad% Coefficient t-value = 4,198.5808 -27.3540 17.4893 -0.0124 -29.0314 SE 799.3395 12.5687 12.4033 0.0176 7.1268 tcalc p-value (b-1) What is the critical value of Student's t in Appendix D for a two-tailed test at a = .01? (Round your answer to 3 decimal places.)arrow_forwardA regression model to predict Y, the state burglary rate per 100,000 people, used the following four state predictors: X1 = median age, X2 = number of bankruptcies per 1,000 population, X3 = federal expenditures per capita (a leading predictor), and X4 = high school graduation percentage. Click here for the Excel Data File (a) Using the sample size of 45 people, calculate the tcalc and p-value in the table given below. (Negative values should be indicated by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Round your t-values to 3 decimal places and p- values to 4 decimal places.) Predictor Intercept AgeMed Coefficient SE tcalc p-value 4,641.0430 798.0634 -28.8630 12.4684 Bankrupt 20.1604 12.1079 FedSpend HSGrad% -0.0181 0.0181 -30.3196 7.1136 (b-1) What is the critical value of Student's tin Appendix D for a two-tailed test at a = .01? (Round your answer to 3 decimal places.) -value =arrow_forward
- The accompanying data represent the weights of various domestic cars and their gas mileages in the city. The linear correlation coefficient between the weight of a car and its miles per gallon in the city is r= - 0.977. The least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable is -0.0061x +41.3297. Complete parts (a) and (b) below. V = Click the icon to view the data table. (a) What proportion of the variability in miles per gallon is explained by the relation between weight of the car and miles per gallon? The proportion of the variability in miles per gallon explained by the relation between weight of the car and miles per gallon is %. (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. % of the variance in is by the linear model. (Round to one decimal place as needed.) Data Table Full data set Miles per Miles per Weight (pounds), x Weight (pounds), x Car Car Gallon, y Gallon, y 1…arrow_forwardUse the pizza cost and the subway fare in the table below to find the regression equation, letting pizza cost be the predictor (x) variable. (Pizza cost is in dollars per slice, subway fare and CPI are in dollars.) What is the best predicted subway fare when pizza costs $4.09 per slice? Use a significance level of 0.05. Year Pizza Cost Subway Fare CPI 1960 1973 1986 1995 2002 2003 2009 2013 2015 2019 0.146 0.348 1.000 1.249 1.750 2.001 2.248 2.300 2.754 2.998 0.151 0.347 0.998 1.353 1.498 2.003 2.247 2.549 2.747 2.750 29.6 44.4 109.6 152.4 180.0 184.0 214.5 233.0 237.0 252.2 The regression equation is y = + (x. (Round the y-intercept to four decimal places as needed. Round the slope to three decimal places as needed.)arrow_forwardHelp with: Draw a scatter diagram of the data, treating age as the explanatory variable. What type of relation, if any, appears to exist between age and HDL cholesterol? Determine the least-squares regression equation from the sample data Assuming the residuals are normally distributed, test whether a linear relation exists between age and HDL cholesterol levels at the α=0.01 level of significance. What are the null and alternative hypotheses? What is the P Value: What conclusion can be drawn at α=0.01 level of significance? Assuming the residuals are normally distributed, construct a 95% confidence interval about the slope of the true least-squares regression line. Lower bound Upper bound For a 42-year-old male patient who visits the doctor's office, would using the least-squares regression line obtained in part (b) to predict the HDL cholesterol of this patient be recommended? A good estimate for the HDL cholesterol of this patient isarrow_forward
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- 2arrow_forwardWrite the multiple regression equation for miles per gallon as the response variable. Use weight and horsepower as predictor variables. How might the car rental company use this model?arrow_forwardA football coach is looking for a way to identify players that are "under weight". The coach decides to get data for each player's height (x, in inches) and weight (y, in pounds), then does a linear regression. The results are: 58+3.7x, r = 0.86 and the standard error is Se = 10.4. Since there is a strong linear correlation the coach, who also majored in Statistics, decides to identify all "outliers" in the data. Obviously, any player whose weight is above the regression line is not "under weight". So the only outliers the coach is interested in are those that are below the regression line. What is the lowest weight possible for a 75 inch player to not be considered "under weight"? Do not round. Submit Question ctor GSearch or type URL & % 24 6 7arrow_forward
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