Scatterplot and Linear Regression Review

Which is TRUE?
I.  Random scatter in the residuals indicates a linear model.
II.  If two variables are very strongly associated, then the correlation between them will be near 1 or -1.
III.  Changing the units of measurement for x or y changes the correlation coefficient.

• I only
• II only
• I and II only
• I, II and III

Tags

CCSS.HSS.ID.B.6B

What should a residual plot look like if a linear model is appropriate for our data?

• Distinct Pattern
• Curve
• Random with no pattern
• A few clumps

Tags

CCSS.HSS.ID.B.6B

Estimate the correlation coefficient for this scatterplot.

• r = 0.56

• r = -0.56

• r = 0.92

• r = -0.92

Tags

CCSS.HSS.ID.C.8

In a statistics course, a linear regression equation was computed to predict the final exam score based on the score on the first test of the term. The equation was y=25+0.7x where y is the final exam score and x is the score on the first test. George scored 80 on the first test. On the final exam George scored 85. What is the value of his residual?

• -4

• 4

• 4.5

• 5

• 81

Tags

CCSS.HSS.ID.B.6B

The equation shows the predicted monthly income for a waitress based on the number of hours worked. Interpret the SLOPE of the Least Squares Line Equation.

• Income typically decreases by $242.30 with each additional hour worked.

• The average income is $31.45

• For each additional hour worked, income typically increases by $31.45

• Waitresses typically make $242.30 every 31.45 hours.

Tags

CCSS.HSF.LE.B.5

To describe the form of a scatterplot, we use:

• Positive/Negative

• Linear/Non-Linear

• Weak/Moderate/Strong

• Outliers

Tags

CCSS.6.SP.B.5C

CCSS.HSS.ID.A.3

To describe the direction of a scatterplot, we use:

• Positive/Negative

• Linear/Non-Linear

• Weak/Moderate/Strong

• Outliers

Tags

CCSS.HSS.ID.B.5

To describe the strength of a scatterplot, we use:

• Positive/Negative

• Linear/Non-Linear

• Weak/Moderate/Strong

• Outliers

Tags

CCSS.HSS.ID.B.5

Estimating a value outside a set of data is...

• Dependent variable

• Independent variable

• Interpolation

• Extrapolation

The LSRL y = 1.4x + 47 attempts to predict students' scores on an upcoming test from the time spent studying (minutes). What does the y-intercept represent in this scenario?

• We predict that students will study an average of 47 minutes

• We predict that students will score an average of 47 points when they study 1.4 hours

• Students are predicted to earn 1.4 more points on average for each additional minute spent studying

• Students who do not study are predicted to earn 47 points on average.

Tags

CCSS.HSF.LE.B.5