The​ least-squares regression equation is y=784.6x+12,431 where y is the median income and x is the percentage of 25 years and older with at least a​ bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7962. Predict the median income of a region in which 25​% of adults 25 years and older have at least a​ bachelor's degree.

A least squares regression line was calculated to relate the length​ (cm) of newborn boys to their weight in kg. The line is weight=−5.82+0.1601 length. A newborn was 48 cm long and weighed 3 kg. According to the regression​ model, what was his​ residual? What does that say about​ him?

From a regression equation r2= 0.39 and the slope = -2.8 What is the linear correlation coefficient r?

The correlation coefficient between the yearly returns of two mutual funds is 0.20. What does that mean about the strength and direction of the linear relationship between the returns of the two funds?

The least-squares regression equation is y = 689.9x + 14,803 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7256. Complete parts (a) through (d). (a) Predict the median income of a region in which 20% of adults 25 years and older have at least a bachelor's degree. (Round to the nearest dollar as needed.) . TOLED dian Income Media 55000- 20000- 15 20 25 30 35 40 45 50 55 60 Bachelor's 96 Q

The​ least-squares regression equation is y=620.6x+16,624 where y is the median income and x is the percentage of 25 years and older with at least a​ bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7004. In a particular​ region, 28.3 percent of adults 25 years and older have at least a​ bachelor's degree. The median income in this region is ​$37,389. Is this income higher than what you would​ expect? Why?

The​ least-squares regression equation is y=761.7x+13,208 where y is the median income and x is the percentage of 25 years and older with at least a​ bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7483.   Predict the median income of a region in which 20​% of adults 25 years and older have at least a​ bachelor's degree.

After performing a statistical regression on a set of data the value of the correlation coefficient r was -0.2041. What does that imply about the data?

Suppose there is a significant correlation between two variables. Describe a case under which it might be inappropriate to use the linear regression equation for prediction.

A trucking company considered a multiple regression model for relating the dependent variable y = total daily travel time for one of its drivers (hours) to the predictors x, = distance traveled (miles) and x, = the number of deliveries made. Suppose that the model equation is Y = -0.800 + 0.060x, + 0.900x2 + € (a) What is the mean value of travel time when distance traveled is 50 miles and four deliveries are made? 5.8 v hr (b) How would you interpret ß1 = 0.060, the coefficient of the predictor X1? o When the number of deliveries is held fixed, the average change in travel time associated with a one-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The total daily travel time increases by 0.060 hours when the distance traveled increases by 1. O When the number of deliveries is constant, the average change in travel time associated with a ten-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The average change in travel time associated with a…