Question 1

Which of the following best describes the relationship shown in the scatterplot between carnivore body mass (kilograms) and abundance per 10,000 kg of prey?

Accepted Answer

There is a strong negative linear relationship.

Question 2

Given the regression equation $$\hat{y} = 61.4 + 2.4x$$, where $$x is $$femur length in centimeters, what is the predicted height for a femur length of 50 cm?

Accepted Answer

$$\hat{y} = 181.4$$

Question 3

What does the slope represent in the regression equation $$\hat{y} = a + bx$$?

Accepted Answer

The change in the predicted value of $$y$$ for a one-unit increase in $$x$$

Question 4

Which of the following is true about the least-squares regression line?

Accepted Answer

It minimizes the sum of squared residuals.

Question 5

If the correlation coefficient $$r = 0.9$$, what is the value of the coefficient of determination $$r^2$$?

Accepted Answer

$$r^2 = 0.81$

Question 6

Which of the following statements about correlation and regression is correct?

Accepted Answer

Correlation does not change when the units of measurement change.

Question 7

What is extrapolation in the context of regression analysis?

Accepted Answer

Using the regression line to predict $$y$$ for $$x$$ values outside the observed range.

Question 8

Which of the following is an example of a lurking variable?

Accepted Answer

Temperature affecting both ice cream sales and drowning rates.

Question 9

Suppose you find a strong positive correlation between the number of firefighters at a fire and the cost of damages. Which of the following is the best explanation?

Accepted Answer

Larger fires both require more firefighters and cause more damage.

Question 10

What does it mean if the residuals from a regression line are randomly scattered around zero in a residual plot?

Accepted Answer

The linear model is likely appropriate for the data.

Question 11

Which of the following best describes Simpson’s Paradox?

Accepted Answer

The direction of an association between two variables changes after including a third variable.

Question 12

Given the regression output: $$a = -3.63$$, $$b = 1.548$$, $$r^2 = 0.79$, r = 0.89$, what is the regression equation?

Accepted Answer

$$\hat{y} = 1.548x - 3.63$$

Question 13

Which of the following is NOT a caution to consider when analyzing association?

Accepted Answer

Assume causation if correlation is strong.

Question 14

If a data point has a large residual, what does this indicate?

Accepted Answer

The point is an outlier in the $$y$$ direction.

Question 15

Which formula is used to calculate the slope $$b of $$the least-squares regression line?

Accepted Answer

$$b = r \frac{s_y}{s_x}$$

Question 16

What is the effect of removing an influential data point from a regression analysis?

Accepted Answer

It can markedly change the regression line.

Question 17

Which of the following best describes the coefficient of determination $$r^2$$?

Accepted Answer

The proportion of the variance in $$y$$ explained by $$x.$$

Question 18

In the context of regression, what is a residual?

Accepted Answer

The difference between the observed $$y$$ and the predicted $$\hat{y}.$$

Question 19

Which of the following is a correct interpretation of a y-intercept in a regression equation?

Accepted Answer

The predicted value of $$y$$ when $$x = 0.$$

Question 20

Which of the following scenarios is most likely to result in confounding?

Accepted Answer

Two explanatory variables are both associated with the response variable and with each other.

Lesson 6

스터디 가이드 - 연습 문제

학습 가이드 - 플래시카드