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- Forest Fires and Acres Burned Numbers (in thousands) of forest fires over the year and the number (in hundred thousands) of acres burned for 7 recent years are shown. Number of fires x 69 58 47 84 62 57 72 Number of acres burned y 72 61 50 87 65 60 75 The correlation coefficient for the data is r=1 and α=0.05. Should regression analysis be done? The regression analysis should not be done. The regression analysis should be done. Find the equation of the regression line. y'′=a+bx a= b= Find y'′ when x=60 y'=.Forest Fires and Acres Burned Numbers (in thousands) of forest fires over the year and the number (in hundred thousands) of acres burned for 7 recent years are shown. Number of fires x 72 69 58 47 84 62 57 Number of acres burned y 74 71 60 49 86 64 59 Send data to Excel The correlation coefficient for the data is r= 1 and a=0.05. Should regression analysis be done? The regression analysis should not be done. The regression analysis should be done. Find the equation of the regression line. y'=a+bx a b. Find y' when x=55. y' = %3D toBookstore sales revisited Recall the data we saw inChapter 6, Exercise 3 for a bookstore. The manager wantsto predict Sales from Number of Sales People Working. Number of SalesPeople Working Sales (in $1000)2 103 117 139 1410 1810 2012 2015 2216 2220 26 Dependent variable is SalesR-squared = 93.2,s = 1.477 Variable CoefficientIntercept 8.1006Num_Workers 0.9134 a) Write the regression equation. Define the variablesused in your equation.b) What does the slope mean in this context?c) What does the y-intercept mean in this context? Is itmeaningful?d) If 18 people are working, what Sales do you predict?e) If sales for the 18 people are actually $25,000, what isthe value of the residual?f) Have we overestimated or underestimated the sales?
- Regression using excel Conduct a multiple regression analysis for the following data. Use Assessed Value as the dependent variable and all others as independent variables. Which of the following independent variables are satistically significant? Use p < 0.05 FloorArea (Sq.Ft.) Offices Entrances Age AssessedValue ($'000) 4790 4 2 8 1796 4720 3 2 12 1544 5940 4 2 2 2094 5720 4 2 34 1968 3660 3 2 38 1567 5000 4 2 31 1878 2990 2 1 19 949 2610 2 1 48 910 5650 4 2 42 1774 3570 2 1 4 1187 2930 3 2 15 1113 1280 2 1 31 671 4880 3 2 42 1678 1620 1 2 35 710 1820 2 1 17 678 4530 2 2 5 1585 2570 2 1 13 842 4690 2 2 45 1539 1280 1 1 45 433 4100 3 1 27 1268 3530 2 2 41 1251 3660 2 2 33 1094 1110 1 2 50 638…Measles and Mumps The data show the number of cases of measles and mumps for a recent 5-year period. Measles Cases Mumps Cases 47 135 67 57 205 816 437 1972 2567 384 Send data to Excel The correlation coefficient for the data is r=-0.671 and a=0.05. Should regression analysis be done? The regression analysis should not be done. O The regression analysis should be done. Find the equation of the regression line. Round the coefficients to at least three decimal places. y' = a + bx a b = Given a year with 160 cases of measles, predict the expected number of cases of mumps for that year. Round your answer to at least one decimal place. The number of cases of mumps for that year is Xe-i
- Answer items (c) and (d) onlyEffects on Selling Price of Houses Square Feet Number of Bedrooms Age Selling Price 2683 3 107100 1889 4 212400 2602 4 8 293400 1905 202200 2851 4 6 115400 1916 14 304600 1920 7 153900 1634 8 263500 1258 4 13 189500 Copy Data Step 2 of 2: Determine if a statistically significant linear relationship exists between the independent and dependent variables at the 0.05 level of significance. If the relationship is statistically significant, identify the multiple regression equation that best fits the data, rounding the answers to three decimal places. Otherwise, indicate that there is not enough evidence to show that the relationship is statistically significant. Answer E Tables E Keypad How to enter your answer (opens in new window) Keyboard Shortcuts Previous Step Answer Selecting a checkbox will replace the entered answer value(s) with the checkbox value. If the checkbox is not selected, the entered answer is used. |X2 + O There is not enough evidence. X3Correlation and Regression Assignment A sample of 10 adult men gave the following data on their heights and weights. Height (inches) X 62 62 63 65 66 67 68 68 70 72 Weight (pounds) Y 120 140 130 150 142 130 135 175 149 168 Generate the scatter plot of this data and graph the regression line in your plot. Include a picture of this graph and explain your impression of how well the line fits the data. State the equation of the regression line. Find r (correlation coefficient). Interpret the value of r in the context of this problem. Find the coefficient of determination, r2. Explain what it means in the context of this problem. Provide examples of other lurking variables that may have an effect on weight besides height. Use a 1% level of significance to test the claim that ρ > 0. Show all steps of your hypothesis test. Predict the weight of a man whose height is 60 inches. (h) Find…
- Forest Fires and Acres Burned Numbers (in thousands) of forest fires over the year and the number (in hundred thousands) of acres burned for 7 recent years are shown. Number of fires x 58 47 84 62 57 72 69 Number of acres burned y 54 43 80 58 53 68 65 The correlation coefficient for the data is =r1 and =α0.05 . Should regression analysis be done? The regression analysis should not be done. The regression analysis should be done. Find the equation of the regression line. =y′+abx =a =b Find y′ when =x50 . =y′The median household incomes in South Carolina from 2013 and 2017 are listed in the table below. (Source: “South Carolina Household Income.” Department of Numbers, https://www.deptofnumbers.com/income/south-carolina/) Year 2013 2014 2015 2016 2017 Median Household Income $46,548 $46,887 $48,876 $50,566 $50,570 Determine the linear regression model, I(t), where t = years since 2013 and I = Median Household Income. A. I(t) = 1172.3t + 46548 B. I(t) = 1172.3t + 46345 C. I(t) = 1172.3t D. I(t) = 1172.3t - 2E+06Data Set: {(-3, 4), (-2, 3), (–1,3), (0, 7), (1, 5), (2, 6), (3, 1)} 1. The regression line is: y = when the 2. Based on the regression line, we would expect the value of response variable to be explanatory variable is 0. 3. For each increase of 1 in of the explanatory variable, we can expect a(n) in the response variable. 4. If x = -3.5, the y = This is an example of 5. The correlation coefficient is r = (Round to the nearest hundredth.) Check of
