Bivariate Data - AP Statistics
Card 0 of 76
Use the following five number summary to determine if there are any outliers in the data set:
Minimum: 
Q1: 
Median: 
Q3: 
Maximum: 
Use the following five number summary to determine if there are any outliers in the data set:
Minimum:
Q1:
Median:
Q3:
Maximum:
An observation is an outlier if it falls more than 
above the upper quartile or more than 
below the lower quartile.
. The minimum value is 
so there are no outliers in the low end of the distribution.
. The maximum value is 
so there are no outliers in the high end of the distribution.
An observation is an outlier if it falls more than
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For a data set, the first quartile is 
, the third quartile is 
and the median is 
.
Based on this information, a new observation can be considered an outlier if it is greater than what?
For a data set, the first quartile is
Based on this information, a new observation can be considered an outlier if it is greater than what?
Use the 
criteria:
This states that anything less than 
or greater than 
will be an outlier.
Thus, we want to find
where 
.
Therefore, any new observation greater than 115 can be considered an outlier.
Use the
This states that anything less than
Thus, we want to find
Therefore, any new observation greater than 115 can be considered an outlier.
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Which values in the above data set are outliers?
Which values in the above data set are outliers?
Step 1: Recall the definition of an outlier as any value in a data set that is greater than 
or less than 
.
Step 2: Calculate the IQR, which is the third quartile minus the first quartile, or 
. To find 
and 
, first write the data in ascending order.
. Then, find the median, which is 
. Next, Find the median of data below 
, which is 
. Do the same for the data above 
to get 
. By finding the medians of the lower and upper halves of the data, you are able to find the value, 
that is greater than 25% of the data and 
, the value greater than 75% of the data.
Step 3: 
. No values less than 64.
. In the data set, 105 > 104, so it is an outlier.
Step 1: Recall the definition of an outlier as any value in a data set that is greater than
Step 2: Calculate the IQR, which is the third quartile minus the first quartile, or
Step 3:
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You are given the following information regarding a particular data set:
Q1: 
Q3: 
Assume that the numbers 
and 
are in the data set. How many of these numbers are outliers?
You are given the following information regarding a particular data set:
Q1:
Q3:
Assume that the numbers
In order to find the outliers, we can use the 
and 
formulas.
Only two numbers are outside of the calculated range and therefore are outliers: 
and 
.
In order to find the outliers, we can use the
Only two numbers are outside of the calculated range and therefore are outliers:
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Use the following five number summary to answer the question below:
Min: 
Q1: 
Med: 
Q3: 
Max: 
Which of the following is true regarding outliers?
Use the following five number summary to answer the question below:
Min:
Q1:
Med:
Q3:
Max:
Which of the following is true regarding outliers?
Using the 
and 
formulas, we can determine that both the minimum and maximum values of the data set are outliers.
This allows us to determine that there is at least one outlier in the upper side of the data set and at least one outlier in the lower side of the data set. Without any more information, we are not able to determine the exact number of outliers in the entire data set.
Using the
This allows us to determine that there is at least one outlier in the upper side of the data set and at least one outlier in the lower side of the data set. Without any more information, we are not able to determine the exact number of outliers in the entire data set.
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A certain distribution has a 1st quartile of 8 and a 3rd quartile of 16. Which of the following data points would be considered an outlier?
A certain distribution has a 1st quartile of 8 and a 3rd quartile of 16. Which of the following data points would be considered an outlier?
An outlier is any data point that falls 
above the 3rd quartile and below the first quartile. The inter-quartile range is 
and 
. The lower bound would be 
and the upper bound would be 
. The only possible answer outside of this range is 
.
An outlier is any data point that falls
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On a residual plot, the 
-axis displays the                      and the 
-axis displays                     .
On a residual plot, the
A residual plot shows the difference between the actual and expected value, or residual. This goes on the y-axis. The plot shows these residuals in relation to the independent variable.
A residual plot shows the difference between the actual and expected value, or residual. This goes on the y-axis. The plot shows these residuals in relation to the independent variable.
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A basketball coach wants to determine if a player's height can predict the number of points the player scores in a season. Which statistical test should the coach conduct?
A basketball coach wants to determine if a player's height can predict the number of points the player scores in a season. Which statistical test should the coach conduct?
Linear regression is the best option for determining whether the value of one variable predicts the value of a second variable. Since that is exactly what the coach is trying to do, he should use linear regression.
Linear regression is the best option for determining whether the value of one variable predicts the value of a second variable. Since that is exactly what the coach is trying to do, he should use linear regression.
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Use the following five number summary to determine if there are any outliers in the data set:
Minimum:
Q1:
Median:
Q3:
Maximum:
Use the following five number summary to determine if there are any outliers in the data set:
Minimum:
Q1:
Median:
Q3:
Maximum:
An observation is an outlier if it falls more than above the upper quartile or more than below the lower quartile.
. The minimum value is so there are no outliers in the low end of the distribution.
. The maximum value is so there are no outliers in the high end of the distribution.
An observation is an outlier if it falls more than
Compare your answer with the correct one above
For a data set, the first quartile is , the third quartile is and the median is .
Based on this information, a new observation can be considered an outlier if it is greater than what?
For a data set, the first quartile is
Based on this information, a new observation can be considered an outlier if it is greater than what?
Use the criteria:
This states that anything less than or greater than will be an outlier.
Thus, we want to find
where .
Therefore, any new observation greater than 115 can be considered an outlier.
Use the
This states that anything less than
Thus, we want to find
Therefore, any new observation greater than 115 can be considered an outlier.
Compare your answer with the correct one above
Which values in the above data set are outliers?
Which values in the above data set are outliers?
Step 1: Recall the definition of an outlier as any value in a data set that is greater than or less than .
Step 2: Calculate the IQR, which is the third quartile minus the first quartile, or . To find and , first write the data in ascending order.
. Then, find the median, which is . Next, Find the median of data below , which is . Do the same for the data above to get . By finding the medians of the lower and upper halves of the data, you are able to find the value, that is greater than 25% of the data and , the value greater than 75% of the data.
Step 3: . No values less than 64.
. In the data set, 105 > 104, so it is an outlier.
Step 1: Recall the definition of an outlier as any value in a data set that is greater than
Step 2: Calculate the IQR, which is the third quartile minus the first quartile, or
Step 3:
Compare your answer with the correct one above
You are given the following information regarding a particular data set:
Q1:
Q3:
Assume that the numbers and are in the data set. How many of these numbers are outliers?
You are given the following information regarding a particular data set:
Q1:
Q3:
Assume that the numbers
In order to find the outliers, we can use the and formulas.
Only two numbers are outside of the calculated range and therefore are outliers: and .
In order to find the outliers, we can use the
Only two numbers are outside of the calculated range and therefore are outliers:
Compare your answer with the correct one above
Use the following five number summary to answer the question below:
Min:
Q1:
Med:
Q3:
Max:
Which of the following is true regarding outliers?
Use the following five number summary to answer the question below:
Min:
Q1:
Med:
Q3:
Max:
Which of the following is true regarding outliers?
Using the and formulas, we can determine that both the minimum and maximum values of the data set are outliers.
This allows us to determine that there is at least one outlier in the upper side of the data set and at least one outlier in the lower side of the data set. Without any more information, we are not able to determine the exact number of outliers in the entire data set.
Using the
This allows us to determine that there is at least one outlier in the upper side of the data set and at least one outlier in the lower side of the data set. Without any more information, we are not able to determine the exact number of outliers in the entire data set.
Compare your answer with the correct one above
A certain distribution has a 1st quartile of 8 and a 3rd quartile of 16. Which of the following data points would be considered an outlier?
A certain distribution has a 1st quartile of 8 and a 3rd quartile of 16. Which of the following data points would be considered an outlier?
An outlier is any data point that falls above the 3rd quartile and below the first quartile. The inter-quartile range is and . The lower bound would be and the upper bound would be . The only possible answer outside of this range is .
An outlier is any data point that falls
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In a regression analysis, the y-variable should be the                        variable, and the x-variable should be the                        variable.
In a regression analysis, the y-variable should be the                        variable, and the x-variable should be the                        variable.
Regression tests seek to determine one variable's ability to predict another variable. In this analysis, one variable is dependent (the one predicted), and the other is independent (the variable that predicts). Therefore, the dependent variable is the y-variable and the independent variable is the x-variable.
Regression tests seek to determine one variable's ability to predict another variable. In this analysis, one variable is dependent (the one predicted), and the other is independent (the variable that predicts). Therefore, the dependent variable is the y-variable and the independent variable is the x-variable.
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If a data set has a perfect negative linear correlation, has a slope of 
and an explanatory variable standard deviation of 
, what is the standard deviation of the response variable?
If a data set has a perfect negative linear correlation, has a slope of
The key here is to utilize
.
"Perfect negative linear correlation" means 
, while the rest of the problem indicates 
and 
. This enables us to solve for 
.
The key here is to utilize
"Perfect negative linear correlation" means
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A least-squares regression line has equation 
and a correlation of 
. It is also known that 
. What is 
A least-squares regression line has equation
Use the formula 
.
Plug in the given values for 
and 
and this becomes an algebra problem.
Use the formula
Plug in the given values for
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A basketball coach wants to determine if a player's height can predict the number of points the player scores in a season. Which statistical test should the coach conduct?
A basketball coach wants to determine if a player's height can predict the number of points the player scores in a season. Which statistical test should the coach conduct?
Linear regression is the best option for determining whether the value of one variable predicts the value of a second variable. Since that is exactly what the coach is trying to do, he should use linear regression.
Linear regression is the best option for determining whether the value of one variable predicts the value of a second variable. Since that is exactly what the coach is trying to do, he should use linear regression.
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No explanation available
No explanation available
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What transformation should be done to the data set, with its residual shown below, to linearize the data?
What transformation should be done to the data set, with its residual shown below, to linearize the data?
Taking the log of a data set whose residual is nonrandom is effective in increasing the correleation coefficient and results in a more linear relationship.
Taking the log of a data set whose residual is nonrandom is effective in increasing the correleation coefficient and results in a more linear relationship.
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