Jorgenson Corporation has provided the following data for the first five months of the year: Machine Lubrication Cost Hours January 240 $ 1,500 February 320 $ 1,600 March 400 $ 1,740 April 300 $ 1,580 May 340 $ 1,680 Using the least-squares regression method of analysis, the estimated variable lubrication cost per machine hour is closest to: Multiple Choice О $1.40 $1.56 $0.80
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- The station manager of a local television station is interested in predicting the amount of television (in hours) that a person in the viewing area will watch. The explanatory variables are age (in years), education (highest level obtained, in years) and family size (number of people in household). The multiple regression output is shown below: Summary measures Multiple R R-Square Adj R-Square 0.6644 StErr of Estimate 0.5598 ANOVA Table Source df SS MS F p-value Explained 3 13.9682 4.6561 0.0000 Unexplained 18 5.6413 0.3134 Regression coefficients Coefficient Std Err t-value p-value Constant 1.683 1.1696 1.4389 0.1674 Age -0.0498 0.0199 -2.5018 0.0222 Education 0.2135 0.0503…A manager at a local bank analyzed the relationship between monthly salary and three independent variables: Length of service (measured in months), Gender (0 = female, 1= male), and Job type (0 = clerical, 1 = technical). The following ANOVA summarizes the %3D regression results. ANOVA Source of Variation df Sum of Squares Mean Square F Regression 3 1,004,346.771 334,782.257 5.96 Residual 26 1,461,134.596 56,197.48445 Total 29 2,465,481.367 Coefficients Standard Error t-Stat p-value Intercept 784.92 322.25 2.44 0.02 Service 9.19 3.20 2.87 0.01 Gender 222.78 89.00 2.50 0.02 Job -28.21 89.61 -0.31 0.76 The level of significance is 0.05. In the regression model, which of the following are dummy variables?The accompanying table shows a portion of data that refers to the property taxes owed by a homeowner (in $) and the size of the home (in square feet) in an affluent suburb 30 miles outside New York City. Click here for the Excel Data File Taxes Size 21,987 2,403 17,353 2,451 29,238 2,866 a. Estimate the sample regression equation that enables us to predict property Taxes on the basis of the size of the home. (Round your answers to 2 decimal places.) Taxes = + Size. b. Interpret the slope coefficient. O As Property Taxes increase by 1 dollar, the size of the house increases by 6.71 ft. O As Size increases by 1 square foot, the property taxes are predicted to increase by $6.71. c. Predict the property Taxes for a 1,400-square-foot home. (Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places.) Taxes
- Draw a scatter diagram of the data, treating age as the explanatory variable. What type of relation, if any, appears to exist between age and HDL cholesterol? Determine the least-squares regression equation from the sample data. Y=A manager at a local bank analyzed the relationship between monthly salary and three independent variables: Length of service (measured in months), Gender (0 = female, 1 = male), and Job type (0 = clerical, 1 = technical). The following ANOVA summarizes the regression results.ANOVA Source of Variation df Sum of Squares Mean Square F Regression 3 1,004,346.771 334,782.257 5.96 Residual 26 1,461,134.596 56,197.48445 Total 29 2,465,481.367 Coefficients Standard Error t-Stat p-value Intercept 784.92 322.25 2.44 0.02 Service 9.19 3.20 2.87 0.01 Gender 222.78 89.00 2.50 0.02 Job −28.21 89.61 −0.31 0.76 The level of significance is 0.05. Based on the hypothesis tests for the individual regression coefficients, ________. Multiple Choice all the regression coefficients are not equal to zero "Job" is the only significant variable in the model only months of service and gender are significantly related to monthly salary "Service"…A manager at a local bank analyzed the relationship between monthly salary and three independent variables: Length of service (measured in months), Gender (0 = female, 1 = male), and Job type (0 = clerical, 1 = technical). The following ANOVA summarizes the %D regression results. ANOVA Source of Variation df Sum of Squares Mean Square F Regression 3 1,004,346.771 334,782.257 5.96 Residual 26 1,461,134.596 56,197.48445 Total 29 2,465,481.367 Coefficients Standard Error t-Stat p-value Intercept 784.92 322.25 2.44 0.02 Service 9.19 3.20 2.87 0.01 Gender 222.78 89.00 2.50 0.02 Job -28.21 89.61 -0.31 0.76 Based on the ANOVA and a O.05 significance level, the global null hypothesis test of the multiple regression model
- A professor at the University of Alabama was interested in evaluating the relationship between family support and delinquency. Using data collected on 4545 families, the researcher used regression to analyze the relationship. The results are presented below. Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Family supportb . Enter a. Dependent Variable: Delinquency b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .249a .062 .062 1.59168 a. Predictors: (Constant), Family support ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 759.204 1 759.204 299.671 <.001b Residual 11479.107 4531 2.533 Total 12238.311 4532 a. Dependent Variable: Delinquency b. Predictors: (Constant), Family support…A professor at the University of Alabama was interested in evaluating the relationship between family support and delinquency. Using data collected on 4545 families, the researcher used regression to analyze the relationship. The results are presented below. Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Family supportb . Enter a. Dependent Variable: Delinquency b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .249a .062 .062 1.59168 a. Predictors: (Constant), Family support ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 759.204 1 759.204 299.671 <.001b Residual 11479.107 4531 2.533 Total 12238.311 4532 a. Dependent Variable: Delinquency b. Predictors: (Constant), Family support…A professor at the University of Alabama was interested in evaluating the relationship between family support and delinquency. Using data collected on 4545 families, the researcher used regression to analyze the relationship. The results are presented below. Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Family supportb . Enter a. Dependent Variable: Delinquency b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .249a .062 .062 1.59168 a. Predictors: (Constant), Family support ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 759.204 1 759.204 299.671 <.001b Residual 11479.107 4531 2.533 Total 12238.311 4532 a. Dependent Variable: Delinquency b. Predictors: (Constant), Family support…
- A researcher wants to predict the effect of the number of times a person eats every day and the number of times they exercise on BMI. What statistical test would work best? A. Pearson's R B. Spearman Rho C. Linear regression D. Multiple regressionIn baseball, two statistics, the ERA (Earned Run Average) and the WHIP (Walks and Hits per Inning Pitched), are used to measure the quality of pitchers. For both measures, smaller values indicate higher quality. The following computer output gives the results from predicting ERA by using WHIP in a least-squares regression for the 2017 baseball season. Variable DF Estimate SE T Intercept 1 -5.0 0.26 - 19.3 WHIP 1 6.8 0.14 47.4 Which of the following statements is the best interpretation of the value 6.8 shown in the output? ERA is predicted to increase by 6.8 units for each 1 unit increase of WHIP. WHIP is predicted to increase by 6.8 units for each 1 unit increase of ERA. For a pitcher with 0 units of WHIP, the ERA is predicted to be approximately 6.8 units. For a pitcher with 0 units of ERA, the WHIP is predicted to be approximately 6.8 units. Approximately 6.8% of the variability in ERA is due to its linear relationship with WHIP.The accompanying table shows a portion of a data set that refers to the property taxes owed by a homeowner (in $) and the size of the home (in square feet) in an affluent suburb 30 miles outside New York City. Click here for the Excel Data File Taxes Size 21,934 2,345 17,334 2,434 ⋮ ⋮ 29,294 2,861 a. Estimate the sample regression equation that enables us to predict property taxes on the basis of the size of the home. (Round your answers to 2 decimal places.) TaxesˆTaxes^ = + Size. b. Interpret the slope coefficient. multiple choice As Property Taxes increase by 1 dollar, the size of the house increases by 6.78 ft. As Size increases by 1 square foot, the property taxes are predicted to increase by $6.78. c. Predict the property taxes for a 1,500-square-foot home. (Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places.) TaxesˆTaxes^
