Statistics - Line of Best Fit

What is a line of best fit used for?

• To make predictions
• To make the graph look pretty
• To connect the points
• To make it look like you know what you are doing

Which sentence describes the relationship shown on this scatter plot?

• As the temperature decreases, the visitors increase. 
• As the temperature increases, the number of visitors increases. 
• As the visitors increase, the temperature decreases. 
• No correlation

What type of linear relationship does this graph show?

• Positive
• negative
• none
• all of the above

Tags

CCSS.HSF-LE.A.1B

What would be the correlation between study time and test grades?

• positive
• negative
• none
• cannot be determined

Determine the predicted income if 22 hours were worked on an assembly job.

• $207.30

• $449.60

• $691.90

• $543.30

Tags

CCSS.HSF.LE.B.5

This measures how strong or weak the relationship between 2 quantitative variables are.

• Scatter Plot

• Residual

• Correlation coefficient

• standard error

What does the value, r= -0.95, tell us about the linear relationship of two quantitative variables?

• Strong positive

• Weak positive

• Strong negative

• Weak negative

Tags

CCSS.HSS.ID.B.5

Residuals are . . . 

• possible models not explored by the researcher
• variation in the response variable that is explained by the model
• the difference between the actual y value and the observed y value

• data collected from individuals that is not consistent with the rest of the group

Tags

CCSS.HSS.ID.B.6B

predicted math grade = 65 + 2.56(study hours)

What does the slope mean in context?

• The model predicts an increase of 2.56 points in the math grade per hour studied, on average.

• The model predicts an increase of 65 points in the math grade per hour studied, on average.

• The model predicts an increase of 2.56 study hours per math grade, on average.

• The model predicts an increase of 2.56 study hours, on average.

Which of the following best describes a 'negative correlation' between two variables?

• As one variable increases, the other variable also increases.

• As one variable increases, the other variable decreases.

• Both variables remain constant as the other changes.

• There is no predictable change in the variables.