a) Copy the numbers you were given for Weight and Pulse from your MyMathLab homework to this table: Weight (Ib) Pulse (bpm) 40 2006 19 150 400 43 1600 29 7t Make a scatter plot of the given points on the grid below. Be sure to label the x and y axes. hoqqu2 b) Looking at the scatterplot BEFORE you run your regression, how do you know that Power Regression will be appropriate for this data? c) Write the power regression equation in terms of x and y, where x is the independent variable, and y is the dependent variable: d) Use your regression equation to predict the heart rate of an animal that weighs 100 pounds. Show your work and give your answer in a complete sentence.
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- The accompanying data resulted from an experiment in which weld diameter and shear strength (in pounds) were determined for five different spot welds on steel. Below are the data collected and the regression equation. Diameter Strength 200.1 813.7 210.1 785.3 220.1 960.4 230.1 1118.0 240.0 1076.2 Strength = -941.6992 + 8.5988*Diameter The predicted y-hat value for a diameter of 201 is 864. if we observed a weld that had a diameter of 235 that had a strength 1000, what would be its residual?Use the sample data given in tablex 1 2 3 4 5y 3 4 1 2 1a) To Write the regression equation ̂y=mx+bb) Plot the regression equation and the scatter diagram on the same graphThe accompanying data resulted from an experiment in which weld diameter and shear strength (in pounds) were determined for five different spot welds on steel. Below are the data collected and the regression equation. Diameter Strength 200.1 813.7 210.1 785.3 220.1 960.4 230.1 1118.0 240.0 1076.2 Strength = -941.6992 + 8.5988*Diameter a)The predicted y-hat value for a diameter of 201 is 864. Interpret this predicted value. b)what is the predicted strength of a weld with a diameter of 51?
- The prelim grades (x) and midterm grades (y) of a sample of 10 MMW students is modeled by the regression line y = 12.0623 + 0.7771x. Estimate the prelim grade if the midterm grade is 83.The table below list weights (carats) and prices (dollars) of randomly selected diamonds Weight 0.3 0.4 0.5 0.5 1.0 0.7 Price 510 1151 1343 1410 5669 2277 Find r Test with a .05 level of significance H0 : ρ = 0 and H1: : ρ ≠ 0 Find m and b for simple regressionIn a manufacturing process the assembly line speed (feet per minute) was thought toaffect the number of defective parts found during the inspection process. To test thistheory, managers devised a situation in which the same batch of parts was inspectedvisually at a variety of line speeds. They collected the following data.Line SpeedNumber of DefectiveParts Found20 2120 1940 1530 1660 1440 17a. Develop the estimated regression equation that relates line speed to thenumber of defective parts found.b. At a .05 level of significance, determine whether line speed and number ofdefective parts found are related.c. Did the estimated regression equation provide a good fit to the data?d. Develop a 95% confidence interval to predict the mean number of defectiveparts for a line speed of 50 feet per minute.
- The following table displays the mathematics test scores for a random sample of college students, along with their final SY16C grades. a. Fit the regression line y = a+bx to the data and interpret the results. b. Use the regression equation to determine the SY16C grade for a college student who scored 60 on their achievement test. What would their SY16C grade be? Mathematics test (x) SY16C grades (y) 1 39 65 2 43 78 3 21 52 4 64 82 5 57 92 6 47 89 7 28 73 8 75 98 9 34 56Note:- please answer question B. using the blow information and image. 10. Using a sample of 546 observations, a researcher is interested infinding factors that influence house prices (measured in tenthousands). The researcher run regression of Hedonic price modelthat explain house prices using lot size, bed rooms, bath rooms,stories all are measured in number of units and dummy variables 2whether the house has air-conditioning, drive way, recreation room,glass show , full basement, garage place and preferred area the resultsare shown below.The following table displays the mathematics test scores for a random sample ofcollege students, along with their final SY16C grades.a. Fit the regression line y=a+bx to the data and interpret the results.b. Use the regression equation to determine the SY16C grade for a college student whoscored 60 on their achievement test. What would their SY16C gradebe? Mathematics test(x) SY16C grades(y)1 39 652 43 783 21 524 64 825 57 926 47 897 28 738 75…
- The authors of a paper were interested in how the distance a deer mouse will travel for food is related to the distance from the food to the nearest pile of debris. Distances were measured in meters. The data and computer output are given below. Distance from Debris Distance Traveled 6.94 0.00 5.23 6.13 5.21 11.29 7.10 14.35 8.16 12.03 5.50 22.72 9.19 20.11 9.05 26.16 9.36 30.65 Simple Linear Regression Results: Dependent Variable: Traveled Independent Variable: Debris Sample size: 9 R (correlation coefficient) = 0.5657 R-sq = 0.32002088 Estimate of error standard deviation 8.670711 Parameter estimates: Parameter Estimate Std. Err. Alternative DF T-Stat P-Value Intercept -7.6854587 13.332196 ≠ 0 7 -0.5764586 0.5824 Slope 3.2340908 1.7818117 ≠ 0 7 1.8150575 0.1124 a)What is the least squares regression line for the output given above? b) what is the predicted traveled distance given the distance from debris is 6.5 meters?The U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression in SPSS. The results are shown below. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782 Total 2628.025 49 a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t…The U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression in SPSS. The results are shown below. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782 Total 2628.025 49 a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t…