
Correlation Coefficient and Regression Concepts
Interactive Video
•
Mathematics
•
9th - 10th Grade
•
Practice Problem
•
Hard
Lucas Foster
FREE Resource
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10 questions
Show all answers
1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of using two different colors when plotting data points in relation to the line of best fit?
To indicate the time sequence of data points
To distinguish between data points above and below the line
To make the graph look more colorful
To highlight the most important data points
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does the correlation coefficient relate to the distance of data points from the line of best fit?
It measures the average distance of all data points
It standardizes and emphasizes the impact of outliers
It only considers the closest data points
It ignores any deviations from the line
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the least squares regression line primarily designed to do?
Ignore outliers in the data set
Minimize the sum of squared deviations
Create a perfect fit for all data points
Maximize the distance between data points
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does the term 'regression' in 'least squares regression line' imply?
Returning to a previous state
Advancing to a future prediction
Ignoring past data
Progressing forward to new data
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is the least squares regression line considered the 'best fit'?
It minimizes the sum of squared deviations
It perfectly aligns with all data points
It is the longest possible line through the data
It maximizes the correlation coefficient
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does a large sum of squared deviations indicate about the correlation coefficient?
A strong positive correlation
A weak or no correlation
An exact correlation
A strong negative correlation
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How is the correlation coefficient standardized?
By squaring and taking the square root of deviations
By ignoring outliers
By using a scale from 0 to 10
By averaging all data points
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