Stats
The table shows several values of X and their corresponding values of Y. Which of the following is the closest to the correlation between X and Y? X y 1 3 2 4 3 7 4 8 5 12
0.98
A tennis ball was thrown in the air. The height of the ball from the ground was recorded every millisecond from the time the ball was thrown until it reached the height from which it was thrown. The correlation between the time and height was computed to be 0. What does this correlation suggest about the relationship between the time and height?
A correlation of O suggests that there is no linear relationship. There may still be a non-linear relationship, perhaps a quadratic relationship in this example.
Dairy farmers are aware there is often a linear relationship between age, in years, of a dairy cow in the amount of milk produced, in gallons per week. The least squares regression line produced from a random sample is milk=40.8-1.1(Age). Based on the model, what is the difference in predicted amounts of milk produce between a cow of five years in a cow of 10 years?
A cow of five years is predicted to produce 5.5 more gallons per week
In a study to determine whether miles driven is a good predictor of trade-in value, 11 cars of the same age, make, model, and condition were randomly selected The following scatterplot shows trade-in value and mileage for those cars. Five of the points are labeled A, B, C, D, and E, respectively. Which of the five labeled points is the most influential with respect to a regression of trade-in value versus miles driven?
Answer E Correct. Point E does not follow the trend with respect to the other data and is probably an outlier. The value of the car is much higher than other cars with similar miles driven.
Researchers observed the grouping behavior of deer in different regions. The following scatterplot shows data collected on the size of the group and the percent of the region that was woodland.
As the percent of woodland increases, the number of deer observed in a group decreases quickly at first and then more slowly.
A botanist found a correlation between the length of an Aspenleaf and its surface area to be 0.94. Why does the correlation value of 0.94 not necessarily indicate that a linear model is the most appropriate model for the relationship between length of an aspen leaf and its surface area?
Even with a correlation value of 0.94, it is possible that the relationship could still be better represented by a non-linear model
Which of the following statements about least squares regression analysis is true? I. A point with a large residual is an outlier Il. A point with high leverage has a Y value that is not consistent with the other Y values in the set Ill. The removal of an influential point from the data set could change the value of the correlation coefficient
Ill only
A set of bivariate data was used to create a least-squares regression line. Which of the following is minimized by the line? Correct.
The least-squares regression line minimizes the sum of the squares of the residuals.
A restaurant manager collected data to predict monthly sales for the restaurant from monthly advertising expenses. The model created from the data showed that 36 percent of the variation in monthly sales could be explained by monthly advertising expenses. What was the value of the correlation coefficient?
The correlation coefficient is 0.60
For a random sample of 20 professional athletes, there is a strong, linear relationship between the number of hours they exercise per week and they're resting heart rate. For the athletes in the sample, those who exercise more hours per week tend to have lower resting heart rates than those who exercise less. Which of the following is a reasonable value for the correlation between the number of hours athletes exercise per week and they're resting heart rate?
-0.87
The scatterplot above shows the number of wins and the attendance per game for 30 baseball teams in 2017. Also shown are the least-squares regression line and computer output.
(a) Interpret the slope of the least-squares regression line in context The slope of the least-squares regression line is about 75. (b) Explain why it is not reasonable to use the least- squares regression model to predict attendance per game for 00 wins It is not reasonable to use the least- squares regression model to predict attendance per game for 00 wins because there should be O data for that. (c) What is the value of the correlation coefficient for the sample? The value of the correlation coefficient for the sample is 12.29%.
A researcher in Alaska measured the age (in months) and the weight (in pounds) of a random sample of adolescent moose.
0.59
square regression equalon (n(y^)=7-0.045x, where x represents the fuel efficiency, in miles per gallon, and y represents the predicted carbon dioxide emissions, in grams per mile. Which of the following is closest to the predicted carbon dioxide emissions, in grams per mile, for a car of this type with a fuel efficiency of 20 miles per gallon? A18 B 6.1 C 446 D 2.697 E1,250,000
C. When 20 is substituted for Xx, the resulting value on the right side of the equation is 6.1. The value of approximatelv 446 results from raisina ee
The least-squares regression line S ^=0.5+1.1LS^=0.5+1.1L models the relationship between the listing price and the actual sales price of 12 houses, with both amounts given in hundred-thousands of dollars. Let LL represent the listing price and SS represent the sales price. Which of the following is the best interpretation of the slope of the regression line? A For each hundred-thousand- dollar increase in the listing price, the sales price will increase by $1.1. B For each hundred-thousand- dollar increase in the listing price, the sales price will increase by $110,000. C For each hundred-thousand- dollar increase in the listing price, the sales price will decrease by $110,000. D For each hundred-thousand dollar increase in the listing price, the sales price is predicted to increase by $1.1. E For each hundred-thousand- dollar increase in the listing price, the sales price is predicted to increase by $110.000.
E For each hundred-thousand- dollar increase in the listing price, the sales price is predicted to increase by $110.000.
For a specific species of fish in a pond, a wildlife biologist wants to build a regression equation to predict the weight of a fish based on its length. The biologist collects a random sample of this species of fish and finds that the lengths vary from 0.75 to 1.35 inches. The biologist uses the data from the sample to create a single linear regression model. Would it be appropriate to use this model to predict the weight of a fish of this species that is 3 inches long?
No, because 3 inches falls above the maximum value of lengths in the sample.
A family would like to build a linear regression equation to predict the amount of grain harvested per acre of land on their farm. A) The total amount of rainfall recorded at their farm B) The type of crop planted in the plot the previous year C) The average daily temperature at their farm D) The variety of grain planted in the plot E) The amount of fertilizer applied to each plot of land
The amount of fertilizer applied to each plot of land
