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- Lyme disease is an inflammatory disease that results in a skin rash and flulike symptoms. It is transmitted through the bite of an infected deer tick. The following data represent the number of reported cases of Lyme disease and the number of drowning deaths for a rural county. Cases_of_Lyme_Disease Drowning_Deaths Month3 0 J2 1 F2 2 M4 1 A5 3 M15 9 J22 17 J13 5 A6 3…0.6.3 Question 3 Is the cubic effect significant? How about quadratic and linear effects? Analysis of Variance Table Response: EX Df Sum Sq Mean Sq F value Pr(>F) MET 1 332 332 0.1246 0.7257742 I(MET^2) 1 37504 37504 14.0572 0.0005141 *** I(MET^3) 1 7245 7245 2.7154 0.1065086 Residuals 44 117390 2668 > anova(quadModel) Analysis of Variance Table Response: EX Df Sum Sq Mean Sq F value Pr(>F) MET 1 332 332 0.120 0.7306134 I(MET^2) 1 37504 37504 13.541 0.0006216 *** Residuals 45 124635 2770 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1A multiple regression analysis produced the following tables. Summary Output Regression Statistics Multiple R 0.978724022 R Square 0.957900711 Adjusted R Square 0.952287472 Standard Error 67.67055418 Observations 18 ANOVA df SS MS F Significance F Regression 2 1562918.941 781459.5 170.6503 4.80907E-11 Residual 15 68689.55855 4579.304 Total 17 1631608.5 Coefficients Standard Error t Stat P-value Intercept 1959.709718 306.4905312 6.39403 1.21E-05 X1 -0.469657287 0.264557168 -1.77526 0.096144 X2 -2.163344882 0.278361425 -7.77171 1.23E-06 For x1= 360 and x2 = 220, the…
- A multiple regression analysis produced the following tables. Summary Output Regression Statistics Multiple R 0.978724022 R Square 0.957900711 Adjusted R Square 0.952287472 Standard Error 67.67055418 Observations 18 ANOVA df SS MS F Significance F Regression 2 1562918.941 781459.5 170.6503 4.80907E-11 Residual 15 68689.55855 4579.304 Total 17 1631608.5 Coefficients Standard Error t Stat P-value Intercept 1959.709718 306.4905312 6.39403 1.21E-05 X1 -0.469657287 0.264557168 -1.77526 0.096144 X2 -2.163344882 0.278361425 -7.77171 1.23E-06 These results indicate that…A multiple regression analysis produced the following tables. Summary Output Regression Statistics Multiple R 0.978724022 R Square 0.957900711 Adjusted R Square 0.952287472 Standard Error 67.67055418 Observations 18 ANOVA df SS MS F Significance F Regression 2 1562918.941 781459.5 170.6503 4.80907E-11 Residual 15 68689.55855 4579.304 Total 17 1631608.5 Coefficients Standard Error t Stat P-value Intercept 1959.709718 306.4905312 6.39403 1.21E-05 X1 -0.469657287 0.264557168 -1.77526 0.096144 X2 -2.163344882 0.278361425 -7.77171 1.23E-06 Using α = 0.01 to test the…A multiple regression analysis produced the following tables. Summary Output Regression Statistics Multiple R 0.978724022 R Square 0.957900711 Adjusted R Square 0.952287472 Standard Error 67.67055418 Observations 18 ANOVA df SS MS F Significance F Regression 2 1562918.941 781459.5 170.6503 4.80907E-11 Residual 15 68689.55855 4579.304 Total 17 1631608.5 Coefficients Standard Error t Stat P-value Intercept 1959.709718 306.4905312 6.39403 1.21E-05 X1 -0.469657287 0.264557168 -1.77526 0.096144 X2 -2.163344882 0.278361425 -7.77171 1.23E-06 The regression equation for…
- A multiple regression analysis produced the following tables. Summary Output Regression Statistics Multiple R 0.978724022 R Square 0.957900711 Adjusted R Square 0.952287472 Standard Error 67.67055418 Observations 18 ANOVA df SS MS F Significance F Regression 2 1562918.941 781459.5 170.6503 4.80907E-11 Residual 15 68689.55855 4579.304 Total 17 1631608.5 Coefficients Standard Error t Stat P-value Intercept 1959.709718 306.4905312 6.39403 1.21E-05 X1 -0.469657287 0.264557168 -1.77526 0.096144 X2 -2.163344882 0.278361425 -7.77171 1.23E-06 Using α = 0.01 to test the…A new vaccination is being used in a laboratory experiment to investigate whether it is effective. There are 273273 subjects in the study. Is there sufficient evidence to determine if vaccination and disease status are related? Vaccination Status Diseased Not Diseased Total Vaccinated 5757 6262 119119 Not Vaccinated 8383 7171 154154 Total 140140 133133 273273 Copy Data Step 6 of 8 : Find the critical value of the test at the 0.0050.005 level of significance. Round your answer to three decimal places.Consider the following model that relates the percentage of a household’s budget spent on milk MK to total expenditure TOTEXP, age of the household head AGE, and the number of children in the household NK. MK = ?1 + ?2ln(??????)+ ?3??+ ?4???+? This model was estimated using 1400 observations from Bahrain. An incomplete version of this output is provided in the following table. Regressor Coefficient Standard Error T-Ratio C 1.4515 2.2019 0.212 ln(??????) 2.7648 0.4842 2.206 ?? -1.4541 0.6395 -0.2141 ??? -0.1503 0.0235 -5.5214 Instructions: Interpret each of the estimates ?2 ,?3 ??? ?4
- An educational researcher wishes to know if there is a difference in academic performance for college freshmen that live on campus and those that commute. Data was collected from 275 students. Can we conclude that freshman housing location and academic performance are related? Location Average Below Average Above Average Total On campus 100 57 58 215 Off campus 30 17 13 60 Total 130 74 71 275 Copy Data Step 6 of 8 : Find the critical value of the test at the 0.05level of significance. Round your answer to three decimal places. Step 7 of 8: Make decision to reject or fail to reject/ is there enough evidence Step 8 of 8: State the conclusion of the hypothesis test at the 0.05 level of significanceObserved Data: 21 smooth/yellow 42 smooth/light-yellow 19 smooth/white 22 wrinkled/yellow 39 wrinkled/light-yellow 20 wrinkled/white Please fill in the table Chi Squared problems: E=expected O=Observed Phenotype Observed Expected O-E (O-E)2 (O-E)2/EA statistical program is recommended. The owner of Showtime Movie Theaters, Inc., would like to predict weekly gross revenue as a function of advertising expenditures. Historical data for a sample of eight weeks follow. WeeklyGrossRevenue($1,000s) TelevisionAdvertising($1,000s) NewspaperAdvertising($1,000s) 96 5.0 1.5 90 2.0 2.0 95 4.0 1.5 92 2.5 2.5 95 3.0 3.3 94 3.5 2.3 94 2.5 4.2 94 3.0 2.5 (a) Develop an estimated regression equation with the amount of television advertising as the independent variable. (Round your numerical values to two decimal places. Let x1 represent the amount of television advertising in $1,000s and y represent the weekly gross revenue in $1,000s.) ŷ = ?? (b) Develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables. (Round your numerical values to two decimal places. Let x1 represent the amount of television advertising in $1,000s, x2 represent the amount of…
