Quiz B 4-17
4-18 Chapter 4 Correlation and Linear Regression
Quiz C 4-19
Chapter 4: Correlation and Linear Regression – Quiz C – Multiple Choice
Name_________________________
4.2.1 Make a scatterplot to display the relationship between two quantitative variables.
1. A study examined consumption levels of oil and carbon dioxide emissions for
sample of counties. The response variable in this study is
A. oil.
B. oil consumption.
C. carbon dioxide emissions.
D. countries.
E. none of the above.
4.2.1 Make a scatterplot to display the relationship between two quantitative variables.
2. A supermarket chain gathers data on the amount they spend on promotional material
(e.g., coupons, etc.) and sales revenue generated each quarter. The predictor variable is
A. sales revenue.
B. amount spent on promotional material.
C. number of coupons offered.
D. supermarket chains.
E. none of the above.
4.3.2 Summarize the strength of a linear relationship with a correlation, r.
3. The scatterplot shows monthly sales figures (in units) and number of months of
experience for a sample of salespeople.
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Exper ience
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Scatterplot of Sales vs Experience
4-20 Chapter 4 Correlation and Linear Regression
The association between monthly sales and level of experience can be described as
A. positive and weak.
B. negative and weak.
C. negative and strong.
D. positive and strong.
E. nonlinear.
4.3.2 Summarize the strength of a linear relationship with a correlation, r.
4. The scatterplot shows monthly sales figures (in units) and number of months of
experience for a sample of salespeople.
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Scatterplot of Sales vs Experience
The correlation between monthly sales and level of experience is most likely
A. -.235.
B. 0.
C. .180.
D. -.914.
E. .914.
Quiz C 4-21
4.3.2 Summarize the strength of a linear relationship with a correlation, r.
5. Shown below is a correlation table showing correlation coefficients between stock
price, earnings per share (EPS) and price/earnings (P/E) ratio for a sample of 19
publicly traded companies. Which of the following statements is false?
Correlations: Stock Price, EPS, PE
Stock Price EPS
EPS 0.875
PE 0.323 -0.111
A. EPS is the best predictor of stock price.
B. The strongest correlation is between EPS and stock price.
C. There is a weak negative association between PE and EPS.
D. PE is the best predictor of stock price.
E. The weakest correlation is between PE and EPS.
4.2.1 Make a scatterplot to display the relationship between two quantitative variables.
6. A small independent organic food store offers a variety of specialty coffees. To
determine whether price has an impact on sales, the managers kept track of how many
pounds of each variety of coffee were sold last month. Based on the scatterplot shown
below, which of the following statements is true?
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Price/Pound
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Scatterplot of Pounds Sold vs Price/Pound
A. The quantitative variable condition is satisfied.
B. The linearity condition is satisfied.
C. There are no obvious outliers.
D. All of the above.
E. None of the above.
4-22 Chapter 4 Correlation and Linear Regression
4.3.2 Summarize the strength of a linear relationship with a correlation, r.
7. A small independent organic food store offers a variety of specialty coffees. To
determine whether price has an impact on sales, the managers kept track of how many
pounds of each variety of coffee were sold last month.
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Price/Pound
Pounds Sold
Scatterplot of Pounds Sold vs Price/Pound
Based on the scatterplot, the linear relationship between number of pounds of coffee sold
per week and price is
A. strong and positive.
B. strong and negative.
C. weak and negative.
D. weak and positive.
E. nonexistent.
Quiz C 4-23
4.3.3 Model a linear relationship with a least squares regression model.
8. A small independent organic food store offers a variety of specialty coffees. To
determine whether price has an impact on sales, the managers kept track of how many
pounds of each variety of coffee were sold last month.
PRICE PER POUND POUNDS SOLD
$ 3.99 75
$ 5.99 60
$ 7.00 65
$ 12.00 45
$ 4.50 80
$ 7.50 70
$ 15.00 25
$ 10.00 35
$ 12.50 40
$ 8.99 50
Mean $ 8.75 54.50
Standard Deviation $ 3.63 18.33
Correlation -0.927
Based on the data and summary statistics shown below, the slope of the estimated
regression line that relates the response variable (monthly sales) to the predictor variable
(price per pound) is
A. 95. 459.
B. .858.
C. -4.681.
D. -.858.
E. -8.999.
4-24 Chapter 4 Correlation and Linear Regression
4.3.3 Model a linear relationship with a least squares regression model.
9. A small independent organic food store offers a variety of specialty coffees. To
determine whether price has an impact on sales, the managers kept track of how many
pounds of each variety of coffee were sold last month.
PRICE PER POUND POUNDS SOLD
$ 3.99 75
$ 5.99 60
$ 7.00 65
$ 12.00 45
$ 4.50 80
$ 7.50 70
$ 15.00 25
$ 10.00 35
$ 12.50 40
$ 8.99 50
Mean $ 8.75 54.50
Standard Deviation $ 3.63 18.33
Correlation -0.927
Based on the data and summary statistics, the intercept of the estimated regression line
that relates the response variable (monthly sales) to the predictor variable (price per
pound) is
A. 95.459.
B. .858.
C. -4.684.
D. -.858.
E. -8.999.
4.3.3 Model a linear relationship with a least squares regression model.
10. A small independent organic food store offers a variety of specialty coffees. To
determine whether price has an impact on sales, the managers kept track of how many
pounds of each variety of coffee were sold last month.
Based on the summary statistics shown below, what percent of the variability in the
number of pounds of coffee sold per week can be explained by price?
A. 95.47%
B. 100%
C. 85.9%
D. 55.6%
E. 4.68%
Mean $ 8.75 54.50
Standard Deviation $ 3.63 18.33
Correlation -0.927
Quiz C 4-25
4.6.11 Analyze and transform data in a nonlinear relationship.
11. A scatterplot shows a curved relationship between 2 variables. What methods can be
used to analyze the relationship between these variables?
A. Fit an exponential or quadratic function
B. Summarize the strength of the association by using correlation and R2.
C. Transform one or both of the variables by using square root, logarithm, or reciprocal
functions
D. A and C
E. All of the above
4-26 Chapter 4 Correlation and Linear Regression
Chapter 4: Correlation and Linear Regression – Quiz C – Key
Quiz D 4-27
Chapter 4: Correlation and Linear Regression – Quiz D – Multiple Choice
Name_________________________
4.1.1 Make a scatterplot to display the relationship between two quantitative variables.
1. A consumer research group examining the relationship between the price of meat (per
pound) and fat content (in grams) gathered data that produced the following scatterplot.
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Fa t Gr a ms
Pr ice /lb
Scatterplot of Price/lb vs Fat Grams
If the point in the lower left hand corner (2 grams of fat; $3.00 per pound) is removed, the
correlation would most likely
A. remain the same.
B. become positive.
C. become weaker negative.
D. become stronger negative.
E. become zero.
4.3.6 Conditions for correlation; define and interpret linear regression; make predictions.
2. Data were collected on monthly sales revenues (in $1,000s) and monthly advertising
expenditures ($100s) for a sample of drug stores. The regression line relating revenues
(Y) to advertising expenditure (X) is estimated to be xy 00.93.48
ˆ+−= . The correct
interpretation of the slope is that for each additional
A. $1 spent on advertising, predicted sales revenue increases by $9,000.
B. $100 spent on advertising, predicted sales revenue increases by $9,000.
C. $100 spent on advertising, predicted sales revenue decreases by $9,000.
D. $1,000 in sales revenue, advertising expenditures decrease by $48.30.
E. $100 in sales revenue, advertising expenditures decrease by $48.30.
4-28 Chapter 4 Correlation and Linear Regression
4.3.3 Model a linear relationship with a least squares regression model.
3. Data were collected on monthly sales revenues (in $1,000s) and monthly advertising
expenditures ($100s) for a sample of drug stores. The regression line relating revenues
(Y) to advertising expenditure (X) is estimated to be xy 00.93.48
ˆ+−= . The predicted
sales revenue for a month in which $1,000 was spent on advertising is
A. $50,000.
B. $851.70.
C. $8,951.70.
D. $41,700.
E. $90,000.
4.3.3 Examine the residuals from a linear model to assess the quality of the model.
4. A company studying the productivity of its employees on a new information system
was interested in determining if the age (X) of data entry operators influenced the number
of completed entries made per hour (Y). The regression equation is xy 145.0374.14
ˆ−= .
Suppose the actual completed entries per hour for an operator who is 35 years old was 8.
The residual is
A. -1.3
B. 2.6
C. -3.5
D. 1.3
E. -2.2
4.3.6 Summarize the strength of a linear relationship with a correlation, r.
5. A company studying the productivity of their employees on a new information system
was interested in determining if the age (X) of data entry operators influenced the number
of completed entries made per hour (Y). The regression equation is xy 145.0374.14
ˆ−= .
If sx=14.04 and sy=2.61, then the correlation coefficient between age and productivity is
A. .779
B. -.236
C. .575
D. -.929
E. -.779
4.3.6 Summarize the strength of a linear relationship with a correlation, r.
6. Suppose the correlation, r, between two variables x and y is -0.44. What would you
predict about a y value if the x value is 2 standard deviations above its mean?
A. It will be .88 standard deviations below its mean.
B. It will be .88 standard deviations above its mean.
C. It will be 2 standard deviations below its mean.
D. It will be .44 standard deviations below its mean.
E. It will be .44 standard deviations above its mean.
Quiz D 4-29
4.3.9 Model a linear relationship with a least squares regression model.
7. Suppose the correlation, r, between two variables x and y is -0.44. What percentage of
the variability in y cannot be explained by x?
A. 19%
B. 44%
C. 81%
D. 88%
E. 12%
4.8.5 Examine the residuals from a linear model to assess the quality of the model.
8. Linear regression was used to describe the trend in world population over time.
Below is a plot of the residuals versus predicted values. What does the plot of residuals
suggest?
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Residual
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(response is Population (millions))
A. An outlier is present in the data set.
B. The linearity condition is not satisfied.
C. A high leverage point is present in the data set.
D. The data are not normal.
E. The equal spread condition is not satisfied.
4-30 Chapter 4 Correlation and Linear Regression
4.8.5 Examine the residuals from a linear model to assess the quality of the model.
9. Based on the following residual plot, which condition / assumption for linear
regression is not satisfied?
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A. Linearity.
B. Quantitative Variables.
C. Equal Spread.
D. Outlier.
E. None of the above; all conditions are satisfied.
Quiz D 4-31
4.3.2 Summarize the strength of a linear relationship with a correlation, r.
10. For the following scatterplot,
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Scatterplot of y vs x
The likely correlation coefficient is
A. +0.35
B. +0.90
C. +0.77
D. -0.89
E. -1.00
4.11.6 Analyze and transform data in a nonlinear relationship.
11. The disadvantage of re-expressing variables is that
A. We have to explain the association in terms of the transformed variables.
B. We cannot use the standard regression models
C. It is not commonly used
D. It can be difficult
E. A and D
4-32 Chapter 4 Correlation and Linear Regression
Chapter 4: Correlation and Linear Regression – Quiz D – Key