Question

Question 1

Which of the following choices best describes the scatterplot shown below?

Scatterplot of C2 vs C1 . : c2 : . . 5 ¿ ó ś c1 10 12 14 16

Group of answer choices

Linear, negative, strong

No form, weak

Linear, positive, strong

Curved, weak

Question 2

Which of the following choices is most likely to be the correlation of the data in the scatterplot shown below?

Group of answer choices

-0.14

1.04

0.86

-0.92

Question 3

Most roller coasters get their speed by dropping down a steep initial incline, so it makes sense that we can predict the speed of the coaster using the height of that initial drop. Data was collected on the top speed and height of the initial drop for each of 15 roller coasters from around the world. In this context, what is the explanatory variable used here?

Group of answer choices

Initial drop

Roller coasters

Number of people riding

Top speed

Question 4

Most roller coasters get their speed by dropping down a steep initial incline, so it makes sense that we can predict the speed of the coaster using the height of that initial drop. Data was collected on the top speed and height of the initial drop for each of 15 roller coasters from around the world. Suppose that the correlation between these two variables is 0.828. Which of the following statements can we conclude solely on the basis of this correlation? Please be sure to check all statements that we can conclude from this correlation.

Group of answer choices

The coasters with a smaller initial drop tend to have a lower top speed.

The relationship between these two variables is most likely not a linear relationship.

The coasters with a larger initial drop tend to have a higher top speed.

Changing the coasters initial drop will cause a change in the coaster's top speed

There isn't much of a relationship between these two variables.

Question 5

For many people, breakfast cereal is an important source of fiber in their diets. Cereals also contain potassium, a mineral shown to be associated with maintaining a healthy blood pressure. An analysis used the amount of fiber (in grams) to predict the potassium content (in milligrams) in servings of 14 breakfast cereals. Suppose that the data for these cereals is given below. What is the equation of the regression line found using this data?

Amount of fiber	10	0	1	0	2	1	2	4	1	4	1	2	5	7
Potassium content	300	15	45	50	80	75	100	135	60	155	80	105	165	240

Group of answer choices

y-hat = 37.47 + 27.009x

y-hat = 2.86 + 2.878x

y-hat = 114.64 + 78.873x

y-hat = -1.27 + .036x

Question 6

A professor asked students to report how many hours per week they had spent studying or working for that class during the semester. Then, the professor used these results and the students' final exam scores with least squares regression to create an equation that predicted the final exam score based on the student's self-reported number of hours per week he or she spent studying. The equation was y-hat = 62.1 + 3.9x. Based on these results, what final exam score would the professor predict for a student who said that he had spent 2 hours per week studying or working for the class? Please give an exact answer in the box below with no units or rounding.

Question 7

A professor asked students to report how many hours per week they had spent studying or working for that class during the semester. Then, the professor used these results and the students' final exam scores with least squares regression to create an equation that predicted the final exam score based on the student's self-reported number of hours per week he or she spent studying. The equation was y-hat = 62.1 + 3.9x. Which of the following choices give the best interpretation for the slope of the professor's line?

Group of answer choices

For every additional hour of self-reported studying, the final exam score is predicted to increase by 3.9 points.

For every additional hour of self-reported studying, the final exam score will increase by 3.9 points.

For every point on the final exam, the self-reported amount of time studying is predicted to increase by 62.1 hours.

For every point on the final exam, the self-reported amount of time studying will increase by 62.1 hours.

For every point on the final exam, the self-reported amount of time studying will increase by 3.9 hours.

For every point on the final exam, the self-reported amount of time studying is predicted to increase by 3.9 hours.

For every additional hour of self-reported studying, the final exam score will increase by 62.1 points.

For every additional hour of self-reported studying, the final exam score is predicted to increase by 62.1 points.

Question 8

A farmer has been working with an experimental new fertilizer, and has tried using different amounts of the fertilizer on different parts of his corn crops. After one season, he collected data from all his fields and created a regression line to predict the amount of yield from the field (in pounds) based on the amount of fertilizer used on the field (in pounds). His equation was y-hat = 1194 + 10.19x. He used anywhere from 0 to 60 pounds of fertilizer on his fields during his experiments. Can he interpret the y-intercept of his line? If so, give the interpretation. If not, explain why not.

Group of answer choices

Yes, he can interpret the y-intercept. When he gets no yield, he will use 1194 pounds of fertilizer.

No, he can't interpret the y-intercept because getting a yield of 0 pounds is not relevant in the context of these data.

Yes, he can interpret the y-intercept. When he gets no yield, he predicts that he will use 1194 pounds of fertilizer.

No, he can't interpret the y-intercept because using 0 pounds of fertilizer is not relevant in the context of these data.

Yes, he can interpret the y-intercept. When he uses no fertilizer, he predicts a yield of 1194 pounds.

Yes, he can interpret the y-intercept. When he uses no fertilizer, he will get a yield of 1194 pounds.

Question 9

A farmer has been working with an experimental new fertilizer, and has tried using different amounts of the fertilizer on different parts of his corn crops. After one season, he collected data from all his fields and created a regression line to predict the amount of yield from the field (in pounds) based on the amount of fertilizer used on the field (in pounds). His equation was y-hat = 1194 + 10.19x. One field he measured used 32 pounds of fertilizer and that field yielded 1573 pounds. What is the residual for this field? Please give the exact answer without rounding or units.

Question 10

A marketing professional has been collecting data for several weeks on both the amount of money her company spends advertising a product during the week and the amount of revenue earned from sales of that product during the week. She then made a least squares line with the goal of using the week's advertising expenditures to predict the week's sales revenue for the product. The correlation for her data was 0.512. What is the value of R² for her regression, and what does it tell us about the relationship between these variables?

Group of answer choices

For this data, R² is 26.2%, so 26.2% of changes in the weekly amounts spent advertising a product are explained by the model using the weekly sales revenue for that product.

For this data, R² is 71.6%, so 71.6% of changes in the weekly sales revenues for a product are explained by the model using the weekly amount spent advertising that product.

For this data, R² is 71.6%, so 71.6% of changes in the weekly amounts spent advertising a product are explained by the model using the weekly sales revenue for that product.

For this data, R² is 26.2%, so 26.2% of changes in the weekly sales revenues for a product are explained by the model using the weekly amount spent advertising that product.

Question 11

Consider a scatterplot with a positive linear form that has one case with an x-value near the mean of the others and a y-value much larger than the others. Is this outlier likely to be an influential point? What will happen to the correlation when this outlier is removed?

Group of answer choices

Yes, this outlier is likely to be influential, and removing it should weaken the correlation of the rest of the data.

No, this outlier is not likely to be influential, but removing it should strengthen the correlation of the rest of the data.

No, this outlier is not likely to be influential, but removing it should weaken the correlation of the rest of the data.

Yes, this outlier is likely to be influential, and removing it should strengthen the correlation of the rest of the data.

Question 12

A linear regression model has been made, and the value of R² for the model was 88.4%. The scatterplot of the data showed a strong linear pattern, but the residual plot for the model showed a random scatter of points with no real form. Based on these results, is this linear regression model likely to be a good model to use for making predictions?

Group of answer choices

Yes, there are no issues with anything reported here that raise doubts about the usefulness of our model's predictions.

No, there are issues with both the residual plot and the value of R²that raise doubts about the usefulness of this model's predictions.

No, the form of the scatterplot and high value of R² means that the predictions using this model are not likely to be useful.

No, the residual plot having no form means that the predictions using this model are not as useful as they could be using another model.

Answer 1

Q1. As C2 decreases as C1 increases. And the scatter plot has dense points it means

Linear, negative, strong

=================

Q2. The scatter plot is positively correlated in its value can be at max = 1

So,

r= 0.86