Stats Lsrl

What kind of correlation?

• Positive

• Negative

• No

The _________________ ____________________ measures the strength of the relationship between 2 variables.

• Correlation Coeffiecient

• Causation

• Independent Variable

The correlation coefficient, r, is always between ___ and ___.

• 0 and 1

• -1 and 1 

• -1 and 0

Estimate the value of r:

• 0.99

• 0.05

• -0.99

Choose the statistical question:

• What time did you wake up this morning?

• What time do 12th graders wake up each morning during the school week?

The LSRL shows the predicted income based on hours worked. Interpret the meaning of the y-intercept. Does this make sense to use?

• If a person works 0 hours, it is predicted they will make 31.45 dollars.

• If a person works 0 hours, it is predicted they will make $-242.3. This does not make sense to use in context.

• If a person works 0 hours, it is predicted they will make $-242.3. This does make sense to use in context.

• Each additional hour worked causes a decrease in pay by $242.3. This makes sense in context.

A data point on a scatterplot is (6,135). The LSRL predicts an input of 6 has an output of 127. The residual is...

• 0

• -8

• 8

• can't be determined

Data on x = the weight of a pickup truck (pounds) and y = distance (in feet) required for a truck traveling 40 miles per hour to come to a complete stop for 30 trucks was used to fit the least squares regression line y^hat = 26 + 0.05x. Which of the following statements is a correct interpretation of the value 26 in the equation of the regression line?

• On average, the stopping distance increases by 26 feet for each 1-pound increase in truck weight.

• On average, the truck weight increases by 26 pounds for each additional foot in stopping distance.

• Approximately 26% of the variation in the stopping distances can be explained by the linear relationship between stopping distance and truck weight.

• It is not reasonable to interpret the intercept in this setting because a weight of 0 does not make sense in the context of this setting.

• Outlier Rule
• Interpreting a residual plot
• Outlier
• What is a residual?

For each increase of 1 in the shoe size, we would expect the height to increase by 1.65 inches.
This is an example of?

• Interpret LSRL y-intercept

• Interpret LSRL slope

• Interpret LSRL "SE"

• Interpret LSRL "s"