= Forty observations were used to estimate y Be +B1x1 + B2x2 +. The regression results are shown in the accompanying table. Coefficients Intercept x1 x2 13.83 -2.53 0.29 Standard Error t Stat 2.42 5.71 p-Value 1.56E-06 0.15 -16.87 5.84E-19 0.06 4.83 2.38E-05 a. Interpret the point estimate for B1. As x1 increases by 1 unit, y is predicted to decrease by 2.53 units. As x1 increases by 1 unit, y is predicted to increase by 0.29 units. As x1 increases by 1 unit, y is predicted to decrease by 2.53 units, holding x2 as a constant. As x1 increases by 1 unit, y is predicted to increase by 0.29 units, holding x2 as a constant. b. What is the sample regression equation? (Negative value should be indicated with a minus sign. Round final answer to 2 decimal places.)

The following data gives the number of employees at the bookstore and the number of minutes students wait in line to buy books at the beginning of the term. The independent variable is the number of employees and the dependent variable is the number of minutes. What is the y intercept? SSxx = 56.857; SS=2095.714; SSxy=-322.571 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square XA356899 0.96 0.93 0.91 y 67 54 47 33 31 25 12

The data show the number of viewers for television stars with certain salaries. Find the regression equation, letting salary be the independent (x) variable. Find the best predicted number of viewers for a television star with a salary of $7 million. Is the result close to the actual number of viewers, 5.2 million? Use a significance level of 0.05. Salary (millions of $) 108 14 5 8 5.1 4.3 7.8 2.5 Viewers (millions) Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=+x (Round to three decimal places as needed.) X 1 1 5 8 5.7 8.2 10.9 4.4

The data show the bug chirps per minute at different temperatures. Find the regression​ equation, letting the first variable be the independent​ (x) variable. Find the best-predicted temperature for a time when a bug is chirping at the rate of 3000chirps per minute. Use a significance level of 0.05. What is wrong with this predicted​ value?   Chirps in 1 min 1004 952 1237 1116 1177 1249   Temperature ​(°​F) 83.1 76 95.1 87.5 92.1 88.4 What is the regression​ equation?   (^ over y)=_____+_____x ​(Round the​ x-coefficient to four decimal places as needed. Round the constant to two decimal places as​ needed.)   What is the best-predicted temperature for a time when a bug is chirping at the rate of 3000chirps per​ minute?   The best-predicted temperature when a bug is chirping at 3000 chirps per minute is ____°F. ​(Round to one decimal place as​ needed.)   What is wrong with this predicted​ value? Choose the correct answer below.   A. It is…

The data show the chest size and weight of several bears. Find the regression​ equation, letting chest size be the independent​ (x) variable. Then find the best predicted weight of a bear with a chest size of 63 inches. Is the result close to the actual weight of 562 ​pounds? Use a significance level of 0.05. Chest_size_(inches)   Weight_ (pounds)58                                  41450                                  31265                                  49959                                  45059                                  45648                                  260 What is the regression equation?^y = ____ + _____ x (round to one decimal place as needed.)What Is the best predicted weight of a bear with a chest size of 63 inches? ^y =____ pounds (round one decimal as needed)Is the result close to the actual weight of 452 pounds?(a) This result is very close to the actual weight of the bear.(b) This result is close to the actual weight of the bear.(c) This result is exactly…

An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:.   Summary measures       Multiple R 0.9689       R-Square 0.9387       Adj R-Square 0.9212       StErr of Estimate 72.218                 Regression coefficients         Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.

The data show the bug chirps per minute at different temperatures. Find the regression​ equation, letting the first variable be the independent​ (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted​ value? Chirps in 1 min  1118  796  1161  918  1103  1219 Temperature (F)   82    72.2   92.2   73   87.5   94.4

Is it reasonable to interpret the y-intercept of the regression line? Why or why not? One home that is 1,750 square feet is listed at $590,000. Is this home's price above or below average for a home of this size? Use residuals and show your work to answer. footaget) ## (Intercept) 21.14411 136.31449 0.155 0.87954 ## footage 0.28388 0.08516 3.333 0.00667 ** ## Residual standard error: 71.64 on 11 degrees of freedom ## Multiple R-squared: 0.5025, Adjusted R-squared: 0.4573 ## F-statistic: 11.11 on 1 and 11 DF, p-value: 0.006671

The data show the chest size and weight of several bears. Find the regression​ equation, letting chest size be the independent​ (x) variable. Then find the best predicted weight of a bear with a chest size of 58 inches. Is the result close to the actual weight of 662 pounds? Use a significance level of 0.05. Chest size (Inches) 46 57 53 41 40 40 Weight (Pounds) 384 580 542 358 306 320