A statistical program is recommended. Consider the following data for a dependent variable y and two independent variables,  x1  and  x2. x1 x2 y 30 12 94 47 10 108 25 17 112 51 16 178 40 5 94 51 19 175 74 7 170 36 12 117 59 13 142 76 16 211 (a) Develop an estimated regression equation relating y to  x1.  (Round your numerical values to one decimal place.) ŷ =        Predict y if  x1 = 51.  (Round your answer to one decimal place.)   (b) Develop an estimated regression equation relating y to  x2.  (Round your numerical values to one decimal place.) ŷ =        Predict y if  x2 = 16.  (Round your answer to one decimal place.)   (c) Develop an estimated regression equation relating y to  x1 and x2.  (Round your numerical values to one decimal place.) ŷ =        Predict y if  x1 = 51  and  x2 = 16.  (Round your answer to one decimal place.)

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line.​ (The pair of variables have a significant​ correlation.) Then use the regression equation to predict the value of y for each of the given​ x-values, if meaningful. The table below shows the heights​ (in feet) and the number of stories of six notable buildings in a city.

Are the years of education of a child dependent on the years of education of their parent? The table shows the number of years of education of parent and the number of years of education of their child.   Years of Education of Parent 13 9 7 12 12 10 11 Years of Education of their Child 13 11 7 16 17 9 17   If there is a significant linear correlation between the variables, determine the regression equation. Are the years of education of a child dependent on the years of education of their parent? The table shows the number of years of education of parent and the number of years of education of their child.   Years of Education of Parent 13 9 7 12 12 10 11 Years of Education of their Child 13 11 7 16 17 9 17   If there is a significant linear correlation between the variables, determine the regression equation. y’ = 1.5x - 3 There is no significant correlation between the variables,…

Is there a relationship between the weight and price of a mountain​ bike? The following data set gives the weights and prices for ten mountain bikes. Let the expanatory variable x be the weight in pounds and the response variable y be the​ bike's price.   Weight​ (LB) 32 33 29 29 34 37 28 30 34 30   Price​ ($) 980 350 430 710 930 160 590 530 180 1090     a. Construct a scatterplot. Interpret. b. Find the regression equation. Interpret the slope in context. Does the​ y-intercept have contextual​ meaning? c. You decide to purchase a mountain bike that weighs 33 pounds. What is the predicted price for the​ bike? a. Which scatterplot below correctly shows the​ data?     A.       254001200xy         A coordinate system has a horizontal x-axis labeled from 25 to 40 in increments of 1 and a vertical y-axis labeled from 0 to 1200 in increments of 50. A cluster of plotted points that form a line that falls from left to…

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line.​ (The pair of variables have a significant​ correlation.) Then use the regression equation to predict the value of y for each of the given​ x-values, if meaningful. The number of hours 6 students spent for a test and their scores on that test are shown below.   Hours spent studying, x 1 2 2 3 4 6       Test​ score, y 39 44 52 47 64 70         ​(a)x=2hours ​(b)x=3.5hours ​(c)x=13hours ​(d)x=1.5hours       Find the regression equation.   y=______  x+ (_____) ​(Round the slope to three decimal places as needed. Round the​ y-intercept to two decimal places as​ needed.)

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. (a) x 503 feet (c) x = 318 feet (b) x = 649 feet (d) x = 724 feet 766 494 484 520 44 620 508 Height, x Stories, y 51 46 43 39 38 (c) Predict the value of y for x=318. Choose the correct answer below. O A. 34 B. 50 O C. 47 O D. not meaningful (d) Predict the value of y for x=724. Choose the correct answer below. O A. 34 O B. 41 50

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (Each pair of variables has a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The caloric content and the sodium content (in milligrams) for 6 beef hot dogs are shown in the table below. Calories, x Sodium, y (a) x= 150 calories (c) x= 120 calories (b) x= 90 calories (d) x=60 calories 160 180 130 130 80 190 415 465 350 380 270 540 (c) Predict the value of y for x= 120. Choose the correct answer below O A. 347 151 O B. 279.801 O C. 414.501 O D. not meaningful (d) Predict the value of y for x= 60. Choose the correct answer below. O A. 414.501 O B. 212.451 tes O C. 279.801

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 90% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $825. The equation of the regression line is y = 1.597x- 169.203. Wages (state), x Wages (federal), y 742 775 783 802 842 890 912 939 949 954 1,002 1,039 1,097 1,145 1,199 1,242 1,271 1,299 1,335 1,398 Construct and interpret a 90% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $825. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) O A. There is a 90% chance that the predicted average weekly wages of federal government employees is between S and S given a state average weekly wage of $825. O B. We can be 90%…

A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ý = 81 + 4x. Years of Annual Sales Salesperson Experience ($1,000s) 80 3 97 3 102 4 4 102 103 6. 8 101 10 119 8 10 123 9. 11 127 10 13 136 (a) Compute SST, SSR, and SSE. SST = SSR = SSE = (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) r2 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line did…

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The number of hours 6 students spent for a test and their scores on that test are shown below. Hours spent studying, x 1 2 2 Test score, y 39 45 50 48 3 5 6 62 72 (a) x = 2 hours (c) x = 15 hours (b) x = 3.5 hours (d) x = 4.5 hours Find the regression equation. y = x+ y= (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.)