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Linear Regression Review

Total questions: 32

Worksheet time: 2hrs 28mins

Name
Class
Date
1.
Which of the following statements is not true of the correlation r between the lengths in inches and weights in pounds of a sample of brook trout?
a)
r must take a value between -1 and 1.
b)
r is measured in inches.
c)
If longer trout tend to also be heavier, then r>0.
d)
r would not change if we measured the lengths of the trout in centimeters instead of inches.
2.
Which is TRUE?
I.  Random scatter in the residuals indicates a linear model.
II.  If two variables are very strongly associated, then the correlation between them will be near 1 or -1.
III.  Changing the units of measurement for x or y changes the correlation coefficient.
a)
I only
b)
II only
c)
I and II only
d)
I, II and III
3.
What should a residual plot look like?
a)
Distinct Pattern
b)
Curve
c)
Random with no pattern
d)
A few clumps
4.
Shown is a residual plot. Would a linear regression model of the data be most appropriate?
a)
YES
b)
NO
5.
Describe the correlation in the graph shown.
a)
Strong Negative
b)
Strong Positive
c)
Weak Negative
d)
Weak Positive
6.
Which is the best CONTEXTUAL description of the y - intercept for the linear regression equation of the rats: Weight = 100 + 40(Time)?
a)
The y-intercept is 100, which means the rat is predicted to weigh 100 grams at birth. 
b)
The y-intercept is 100, which is where the graph crosses the y-axis. 
c)
100 is where the data starts on the y-axis. 
d)
The y-intercept is 100, which means at a weight of 0, the rat will take 100 seconds.
7.

Estimate the correlation coefficient for this scatterplot.

a)

r = 0.56

b)

r = -0.56

c)

r = 0.92

d)

r = -0.92

8.
The correlation coefficient measures...
a)
whether there is a relationship between two variables.
b)
the strength of the relationship between two quantitative variables.
c)
whether a cause and effect relation exists between two variables.
d)
the strength of the linear relationship between two quantitative variables.
9.

In a statistics course, a linear regression equation was computed to predict the final exam score based on the score on the first test of the term. The equation was y=25+0.7x where y is the final exam score and x is the score on the first test. George scored 80 on the first test. On the final exam George scored 85. What is the value of his residual?

a)

-4

b)

4

c)

4.5

d)

5

e)

81

10.
Interpret the slope of the Least Squares Line Equation.
a)
Income increases by 31.45 with each additional 2 hours worked.
b)
Income increases by 31.45
c)
As hours increases by one, income will increase by 31.45.
d)
As hours increases by one, income will decrease by 242.3.
11.
Which of the following statements are supported by the scatterplot?
I.   There is a positive association between height and volume.
II.  There is an outlier in the plot. 
III.  As the height of a cherry tree increases, the volume of useable lumber it yields increases.
a)
I only
b)
II only
c)
I and II only
d)
I, II, and III
12.
“Least-squares” in the term “least-squares regression line” refers to...
a)
Minimizing the sum of the squares of all values of the explanatory variable.
b)
Minimizing the sum of the squares of all values of the response variable.
c)
Minimizing the products of each value of the response variable and the predicted value based on the regression equation.
d)
Minimizing the sum of the squares of the residuals.
13.
Suppose a straight line is fit to data having response variable y and explanatory variable x.  Predicting values of y for values of x outside the range of the observed data is called...
a)
extrapolation
b)
causation
c)
correlation
d)
interpolation
14.
A person travels by car.  They record their miles driven in a data table.  Calculate the linear regression equation of this data.
a)
y = 61.93x - 1.79
b)
y = -1.79x + 61.93
c)
y = 0.016x + 0.03
d)
y = 0.03x + 0.016
15.

Which correlation coefficient is most accurate for this data set?

a)

r = - 0.3

b)

r = 0.99

c)

r = -0.99

16.

What does a correlation coefficient of r=−0.01

imply about the graph of a data set? Select all that apply.

a)

A linear model is not a good fit for the data.

b)

A linear model is a good fit for the data.

17.

Which graph most clearly shows a positive correlation between the x- and y−values?

a)
b)
c)
18.

Which graph most clearly shows a no correlation between the x- and y−values?

a)
b)
c)
19.

Which graph most clearly shows a negative correlation between the x- and y−values?

a)
b)
c)
20.
Describe the correlation in the graph shown.
a)
Strong Negative
b)
Strong Positive
c)
Weak Negative
d)
Weak Positive
21.
Describe the correlation in the graph shown.
a)
Strong Negative
b)
Strong Positive
c)
Weak Negative
d)
Weak Positive
22.
What type of association does this graph have?
a)
positive
b)
negative
c)
none
d)
all of the above
23.
What would be the correlation between study time and test grades?
a)
positive
b)
negative
c)
none
d)
cannot be determined
24.
What letter do we use to represent correlation coefficient?
a)
c
b)
r
c)
p
d)
h
25.
What type of correlation does this graph show?
a)
Positive
b)
negative
c)
none
d)
all of the above
26.
What would be the correlation be between the "number of times a student skips class" and their "exam score"?
a)
positive correlation
b)
negative correlation
c)
no correlation
d)
cannot be determined
27.

Is r = -0.87 strong, moderate, or weak?

a)

strong

b)

moderate

c)

weak

28.
What is the interquartile range (IQR)?
a)
4
b)
64
c)
60
d)
8
29.
What is the range of the data shown?
a)
85
b)
90
c)
80
d)
70
30.
About how many male math teachers were there in 2006?
a)
15
b)
30
c)
35
d)
45
31.
The equation of the trend line is y= 1/2x +1
How many laps can a bicycle make around the park in 23 minutes?
a)
44 laps
b)
12 1/2 laps
c)
23 laps
32.

What is the equation of the line of best fit?

a)

y = -25x + 170

b)

y = -5/8x + 170

c)

y = 25x + 170

d)

y = 5/8x + 170

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