Chapter 3 Practice

What should a residual plot look like?

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

Shown is a residual plot. Would a linear regression model of the data be most appropriate?

• YES
• NO

Which is the best CONTEXTUAL description of the y - intercept for the linear regression equation of the rats: Weight = 100 + 40(Time)?

• The y-intercept is 100, which means the rat is predicted to weigh 100 grams at birth. 
• The y-intercept is 100, which is where the graph crosses the y-axis. 
• 100 is where the data starts on the y-axis. 
• The y-intercept is 100, which means at a weight of 0, the rat will take 100 seconds.

Estimate the correlation coefficient for this scatterplot.

• r = 0.56

• r = -0.56

• r = 0.92

• r = -0.92

The correlation coefficient measures...

• whether there is a relationship between two variables.
• the strength of the relationship between two quantitative variables.
• whether a cause and effect relation exists between two variables.
• the strength of the linear relationship between two quantitative variables.

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

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.

Which of the following statements are supported by the scatterplot?
I.   There is a positive association between height and volume.
II.  There is an outlier in the plot. 
III.  As the height of a cherry tree increases, the volume of useable lumber it yields increases.

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

Suppose a straight line is fit to data having response variable y and explanatory variable x.  Predicting values of y for values of x outside the range of the observed data is called...

• extrapolation
• causation
• correlation
• interpolation

A person travels by car.  They record their miles driven in a data table.  Calculate the linear regression equation of this data.

• y = 61.93x - 1.79
• y = -1.79x + 61.93
• y = 0.016x + 0.03
• y = 0.03x + 0.016