UNIT 4 — MILESTONE 4
Score 15/18
You passed this Milestone
15 questions were answered correctly.
3 questions were answered incorrectly.
1
The
... [Show More] table below shows the grade and reading level for 5 students.
Grade Reading Level
Student 1 2 6
Student 2 6 14
Student 3 5 12
Student 4 4 10
Student 5 1 4
For grade, the mean is 3.6 and the standard deviation is 2.1.
For reading level, the mean is 9.2 and the standard deviation is 4.1.
Using the formula below or Excel, find the correlation coefficient, r, for this set of students. Answer choices
are rounded to the nearest hundredth.
1.00
0.85
0.71
0.92
RATIONALE
In order to get the correlation, we can use the formula:
Correlation can be quickly calculated by using Excel. Enter the values and use the function "=CORREL(".
CONCEPT
Correlation
I need help with this question
2
This scatterplot shows the maintenance expense for a truck based on its years of service.
The equation for least regression line to this data set is ŷ = 76.82x + 88.56.
What is the predicted value (in dollars) for maintenance expenses when a truck is 7 years old?
$549
$473
$626
$703
RATIONALE
In order to get the predicted maintenance expense when the age of the truck is 7 years, we simply substitute the value
7 in our equation for x. So we can note that:
CONCEPT
Predictions from Best-Fit Lines
I need help with this question
3
Raoul lives in Minneapolis and he is planning his spring break trip. He creates the scatterplot below to assess how
much his trip will cost.
Which answer choice correctly indicates the explanatory and response variables for the scatterplot?
Explanatory variable: Distance
Response variable: Cost
Explanatory variable: Minneapolis
Response variable: Miles flown
Explanatory variable: Miles flown
Response variable: Minneapolis
Explanatory variable: Cost
Response variable: Distance
RATIONALE
The explanatory variable is what is along the horizontal axis, which is distance. The response variable is along the
vertical axis, which is cost.
CONCEPT
Explanatory and Response Variables
I need help with this question
4
Which of the following scatterplots shows a correlation affected by an influential point?
RATIONALE
An influential point will influence correlation that doesn't lie in the range of the other data. This graphs shows an
outlier that is above the other data and lower in the x-direction.
CONCEPT
Cautions about Correlation
I need help with this question
5
Peter analyzed a set of data with explanatory and response variables x and y.
He concluded the mean and standard deviation for x as 7.8 and 3.70, respectively.
He also concluded the mean and standard deviation for y as 12.2 and 4.15, respectively.
The correlation was found to be 0.964.
Select the correct slope and y-intercept for the least-squares line. Answer choices are rounded to the
hundredths place.
Slope = -1.08
y-intercept = 3.78
Slope = 1.08
y-intercept = -3.78
Slope = 1.08
y-intercept = 3.78
Slope = -1.08
y-intercept = -3.78
RATIONALE
We first want to get the slope. We can use the formula:
To then get the intercept, we can solve for the y-intercept by using the following formula:
y with hat on top equals b subscript 0 plus b subscript 1 x
We know the slope, b subscript 1, and we can use the mean of x and the mean of y for the variables x
and
y with hat on top to solve for the y-intercept, b subscript 0.
CONCEPT
Finding the Least-Squares Line
I need help with this question
6
In a study of 30 high school students, researchers found a high correlation, 0.93, between amount of exercise and
weight lost.
Which of the following statements is TRUE?
There is a strong positive linear association between weight loss and exercise, but the researchers have not
proven causation.
The researchers proved that exercise causes weight loss, but only for high school students.
The researchers proved that exercise causes weight loss.
93% of the high school students studied lost weight.
RATIONALE
Recall that correlation measures the strength and direction of linear association. So r= 0.93 indicates a strong positive
linear association. Recall also, that correlation doesn't imply causation. Causation is a direct change in one variable
causing a change in some outcome.
CONCEPT
Correlation and Causation
I need help with this question
7
For the data plotted in the scatterplot, the r
2 value was calculated to be 0.9846.
Which of the following sets of statements is true?
98.5% of the variation in age is explained by a linear relationship with yearly income.
The correlation coefficient, r, is 0.969.
98.5% of the variation in yearly income is explained by a nonlinear relationship with age.
The correlation coefficient, r, is 0.992.
98.5% of the variation in yearly income is explained by a linear relationship with age.
The correlation coefficient, r, is 0.992
98.5% of the variation in age is explained by a nonlinear relationship with yearly income.
The correlation coefficient, r, is 0.969.
RATIONALE
The coefficient of determination measures the percent of variation in the outcome, y, explained by the regression. So
a value of 0.9846 tells us the regression with age, x, can explain about 98.5% of the variation in income, y.
We can also note that r = .
CONCEPT
Coefficient of Determination/r^2
I need help with this question
8
A correlation coefficient between average temperature and coat sales is most likely to be __________.
between 1 and 2
between 0 and 1
between 0 and -1
between -1 and -2
RATIONALE
If we note that as temp goes up, we would expect coats to be less necessary, so coat sales would go down. So
correlation should be negative and this would be between 0 and -1.
CONCEPT
Positive and Negative Correlations
I need help with this question
9
Thomas was interested in learning more about the salary of a teacher. He believed as a teacher increases in age, the
annual earnings also increases. The age (in years) is plotted against the earnings (in dollars) as shown below.
Using the best-fit line, approximately how much money would a 45-year-old teacher make?
$50,000
$58,000
$48,000
$55,000
RATIONALE
To get a rough estimate of the salary of a 45 year-old, we go to the value of 45 on the horizontal axis and then see
where it falls on the best-fit line. This looks to be about $50,000.
CONCEPT
Best-Fit Line and Regression Line
I need help with this question
10
Jesse takes two data points from the weight and feed cost data set to calculate a slope, or average rate of change. A
hamster weighs half a pound and costs $2 per week to feed, while a Labrador Retriever weighs 62.5 pounds and costs
$10 per week to feed.
Using weight as the explanatory variable, what is the slope of a line between these two points? Answer choices
are rounded to the nearest hundredth.
$7.75 / lb.
$4.00 / lb.
$6.25 / lb.
$0.13 / lb.
RATIONALE
In order to get slope, we can use the formula:
s l o p e equals fraction numerator y 2 minus y 1 over denominator x 2 minus x 1 end fraction.
Using the information provided, the two points are: (0.5 lb., $2) and (62.5 lb., $10). We can note that:
CONCEPT
Linear Equation Algebra Review
I need help with this question
11
Brad reads a scatterplot that displays the relationship between the number of cars owned per household and the
average number of citizens who have health insurance in neighborhoods across the country. The plot shows a strong
positive correlation.
Brad recalls that correlation does not imply causation. In this example, Brad sees that increasing the number of cars
per household would not cause members of his community to purchase health insurance.
Identify the lurking variable that is causing an increase in both the number of cars owned and the average number of
citizens with health insurance.
Average annual salary per household
The number of different car brands
The number of citizens in the United States who do not have health insurance
Average health insurance costs in the United States
RATIONALE
Recall that a lurking variable is something that must be related to the outcome and explanatory variable that when
considered can help explain a relationship between 2 variables. Since higher income is positively related to owning
more cars and having health insurance, this variable would help explain why we see this association.
CONCEPT
Correlation and Causation
I need help with this question
12
Shawna finds a study of American women that had an equation to predict weight (in pounds) from height (in inches):
ŷ = -260 + 6.6x. Shawna’s height was 64 inches and her weight was 150 pounds.
What is the value of the residual for Shawna's weight and height?
-12.4 pounds
730 pounds
12.4 pounds
162.4 pounds
RATIONALE
Recall that to get the residual, we take the actual value - predicted value. So if the actual height of 64 inches and the
resulting actual weight is 150 pounds, we simply need the predicted weight. Using the regression line, we can say:
The predicted weight is 164.4 pounds. So the residual is:
CONCEPT
Residuals
I need help with this question
13
Which of the following scatterplots shows an outlier in the y-direction?
RATIONALE
To have an outlier in the y-direction the outlier must be in the range of x data but outside the range of y-data. This
outlier is outside of the data in the y direction, lying below all of the data.
CONCEPT
Outliers and Influential Points
I need help with this question
14
Which of the following is a guideline for establishing causality?
The experiment performed should be controlled and randomized.
Do not consider other possible causes.
Look for cases where correlation does not exist between the variables.
Check if the effect is present or absent when the response variable is present or absent.
RATIONALE
Finding an association inside an experimental design controls for many other outside influences and helps to ensure
that an explanatory variable always precedes the response. This helps to support, with strong confidence, that the
association is causal.
CONCEPT
Establishing Causality
I need help with this question
15
This scatterplot shows the performance of a thermocouple using the variables temperature difference and voltage.
Select the answer choice that accurately describes the data's form, direction, and strength in the scatterplot.
Form: The data pattern is nonlinear.
Direction: There is a positive association between temperature difference and voltage.
Strength: The data pattern is weak.
Form: The data pattern is linear.
Direction: There is a negative association between temperature difference and voltage.
Strength: The data pattern is strong.
Form: The data pattern is nonlinear.
Direction: There is a negative association between temperature difference and voltage.
Strength: The data pattern is weak.
Form: The data pattern is linear.
Direction: There is a positive association between temperature difference and voltage.
Strength: The data pattern is strong.
RATIONALE
If we look at the data, it looks as if a straight line captures the relationship, so the form is linear. The slope of the line
is positive, so it is increasing. Finally, since the dots are closely huddled around each other in a linear fashion, it
looks strong.
CONCEPT
Describing Scatterplots
I need help with this question
16
Which statement about correlation is FALSE?
Correlation is used to define the variables of only non-linearly related data sets.
The correlation of a data set can be positive, negative, or 0.
Correlation between the variables of the data set can be measured.
Correlation is the degree to which the two variables of a data set resemble each other.
RATIONALE
Recall that correlation is used for linear association between 2 quantitative variables, NOT for non-linearly related
variables.
CONCEPT
Correlation
I need help with this question
17
A bank manager declares, with help of a scatterplot, that the number of health insurances sold may have some
association with the number of inches it snows.
How many policies were sold when it snowed 2 to 4 inches?
350
470
210
240
RATIONALE
In order to find the total number of policies between 2 and 4 inches, we must add the three values of 10 in that
interval.
At 2 inches, there were 100 policies.
At 3 inches, there were 110 policies.
At 4 inches, there were 140 policies.
So the total is 100 + 110 + 140 = 350 policies.
CONCEPT
Scatterplot
I need help with this question
18
Data for price and thickness of soap is entered into a statistics software package and results in a regression equation
of ŷ = 0.4 + 0.2x.
What is the correct interpretation of the slope if the price is the response variable and the thickness is an
explanatory variable?
The price of the soap increases by $0.20, on average, when the thickness increases by 1 cm.
The price of the soap decreases by $0.20, on average, when the thickness increases by 1 cm.
The price of the soap decreases by $0.40, on average, when the thickness increases by 1 cm.
The price of the soap increases by $0.40, on average, when the thickness increases by 1 cm.
RATIONALE
When interpreting the linear slope, we generally substitute in a value of 1. So we can note that, in general, as x
increases by 1 unit the slope tells us how the outcome changes. So for this equation we can note as x (thickness)
increases by 1 cm, the outcome (price) will increase by $0.20 on average.
CONCEPT
Interpreting Intercept and Slope
I need help with this question
© 2020 SOPHIA Learning, LLC. SOPHIA is a registered trademark of SOPHIA Learning, LLC.
About
Contact Us
Privacy Policy
Terms of Use [Show Less]