Relationship Between Two Quantitative Variables

Interpret the slope of the Least Squares Line Equation.

• Income increases by 31.45 with each additional 2 hours worked.

• Income increases by 31.45

• As hours increases by one, income will increase by 31.45.

• As hours increases by one, income will decrease by 242.3.

Least-squares in the term least-squares regression line refers to...

• Minimizing the sum of the squares of all values of the explanatory variable.

• Minimizing the sum of the squares of all values of the response variable.

• Minimizing the products of each value of the response variable and the predicted value based on the regression equation.

• Minimizing the sum of the squares of the residuals.

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

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

• Scatter Plot

• Causation

• Correlation

• Line of best fit

What does the value, r= -0.89, tell us about the linear relationship of two quantitative variables?

• Strong positive

• Weak positive

• Strong negative

• Weak negative

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

What type of association does this graph have?

• positive 

• negative

• none

• all of the above

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

• $207.30

• $449.60

• $691.90

• $543.30

Determine the correlation coefficient of the given data:

• .9820

• -.9643

• 1.409

• .6864

Is this line an accurate line estimate of best fit for the data?

• Yes

• No