The table below shows the average weekly wages​ (in dollars) for state government employees and federal government employees for 10 years. Construct and interpret a 95​% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is ​$841. The equation of the regression line is ModifyingAbove  .y=1.403x+9.259. Wages​ (state), x 724 747 800 803 839 897 901 939 951 956   Wages​ (federal), y 1,035 1,060 1,111 1,144 1,190 1,245 1,276 1,306 1,332 1,396 Construct and interpret a 95​% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $841. Select the correct choice below and fill in the answer boxes to complete your choice. ​(Round to the nearest cent as​ needed.)   A. There is a 95​% chance that the predicted average weekly wages of federal government employees is between ​$ and ​$, given a state average weekly wage of ​$841.

Dr. Lillian Fok, a New Orleans psychologist, specializes in treating patients who are agoraphobic (i.e., afraid to leave their homes). The following table indicates how many patients Dr. Fok has seen each year for the past 10 years. It also indicates what the robbery rate was in New Orleans during the same year. Year Number of Patients Robbery Rate per 1,000 Population The simple linear regression equation that shows the best relationship between the number of patients and the robbery rate is (round your responses to three decimal places) where y Number of Patients and x = Robbery Rate. = 1 2 3 4 6 7 36 33 40 41 55 60 58.3 61.1 73.4 75.7 81.1 89.0 101.1 5 40 8 54 94.8 9 58 103.3 10 61 116.2

The table shows the average weekly wages (in dollars) for state government employees and federal government employees for 8 years. The equation of the regression line is y = 1.405x – 12.307. Complete parts (a) and (b) below. Average Weekly Wages (state), x Average Weekly Wages (federal), y 751 760 791 817 835 881 924 951 1002 1047 1115 1149 1195 1250 1269 1300 (a) Find the coefficient of determination and interpret the result. 12 =O

Biochemical oxygen demand (BOD) measures organic pollutants in water by measuring the amount of oxygen consumed by microorganisms that break down these compounds. BOD is hard to measure accurately. Total organic carbon (TOC) is easy to measure, so it is common to measure TOC and use regression to predict BOD. A typical regression equation for water entering a municipal treatment plant is BOD = -55.47 + 1.505 TOC Both BOD and TOC are measured in milligrams per liter of water. (a) What does the slope of this line say about the relationship between BOD and TOC? TOC rises (falls) by 1.505 mg/l for every 55.47 mg/l increase (decrease) in BOD BOD rises (falls) by 55.47 mg/l for every 1 mg/l increase (decrease) in TOC TOC rises (falls) by 1.505 mg/l for every 1 mg/l increase (decrease) in BOD BOD rises (falls) by 1.505 mg/l for every 1 mg/l increase (decrease) in TOC

A researcher calculates a regression equation to predict an insurance premium based on a person's age. One person in the sample was observed to have a premium of $500 at 20 years old, but the predicted value was $400. VWhat is the value of the residual for this person? Next Page Page 10 of 14

Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right am blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 100 mm Hg Use a significance level of 0.05. Right Arm 103 102 95 78 Left Arm 175 169 147 146 144 m Click the icon to view the critical values of the Pearson correlation coetticient r The regression oquation is y =+O (Round to one decimal place as needed) Given that the systolic blood pressure in the right arm is 100 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg (Round to one decimal place as needed.)

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 784 838 822 949 1188 813   Temperature ​(°​F) 65.7 67.8 66.2 79.9 92.3 73.5   What is the regression​ equation?   What is the best-predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per​ minute?   The best predicted temperature when a bug is chirping at 3000chirps per minute are?   What is wrong with this predicted​ value? Choose the correct answer below.     A. It is unrealistically high. The value 3000 is far outside of the range of observed values.   B. The first variable should have been the dependent variable.   C. It is only an…

The table below shows the average weekly wages (in dollars) for state government employees and federal government employees for 10 years. Construct and interpret a 99% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $818. The equation of the regression line is y = 1.451x - 40.698. Wages (state), x Wages (federal), y 713 760 785 805 851 870 921 926 947 985 1,002 1,045 1,091 1,145 1,185 1,239 1,281 1,307 1,320 1,400 Construct and interpret a 99% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $818. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) O A. We can be 99% confident that when the average weekly wages of state government employees is $818, the average weekly wages of federal government employees will be between $ and…

Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child (height ), height of the mother ( mothersheight), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS df 208.008457 314.295372 37 2 104.004228 8.49446952 MS 522.303829 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared Root MSE = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .9567683 .4804261 34.92886 What is the predicted height for a child born to a mother…

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 760 1119 1125 850 965 785 D Temperature (°F) 70.5 90.8 90.8 74.7 77.1 69.9 What is the regression equation? (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.)