The local utility company surveys 19 randomly selected customers. For each survey participant, thecompany collects the following: annual electric bill (in dollars) and home size (in square feet). Output froma regression analysis is as follows: bill 11.45 +3.26 size.Coefficients Estimate Std. Error(Intercept)Size11.453.260.830.35Round your answer to three decimal places, and round any interim calculations to four decimal places.We are 99% confident that the mean annual electric bill increases by betweendollars for every additional square foot in home size.dollars and

Answered: The local utility company surveys 19…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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. 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…
onsider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume(units) Total Cost($) 400 3,900 450 4,900 550 5,500 600 5,900 700 6,400 750 7,100 This data was used to develop an estimated regression equation, ŷ = 970.67 + 8.08x, relating production volume and cost for a particular manufacturing operation. Use ? = 0.05 to test whether the production volume is significantly related to the total cost. (Use the F test.) Set up the ANOVA table. (Round your p-value to three decimal places and all other values to two decimal places.) Sourceof Variation Sumof Squares Degreesof Freedom MeanSquare F p-value Regression Error Total Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value = What is your conclusion? Reject H0. We conclude that the relationship between production…
The local utility company surveys 12 randomly selected customers. For each survey participant, the company collects the following: annual electric bill (in dollars) and home size (in square feet). Output from a regression analysis is as follows: bill = 16.2 + 3.58. size. Coefficients Estimate Std. Error (Intercept) 16.2 Size 3.58 0.47 0.68 Round your answer to three decimal places, and round any interim calculations to four decimal places. We are 99% confident that the mean annual electric bill increases by between dollars for every additional square foot in home size. dollars and
A. Write the equation of the regression line. B. Interpret each one of the slopes in this context. C. Calculate the estimated number of days absent and the residual for the first observation in the data set: a student who is aboriginal, male, a slow learner, and missed 2 days of school.
For Data Set 9 in Appendix B, “Bear Measurements,” we get this regression equation: Weight = -274 + 0.426 Length + 12.1 Chest Size, with R2 = 0.928. Interpret the multiple coefficient of determination – what does this value tell us?
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 40inches. Is the result close to the actual weight of 352pounds? Use a significance level of 0.05.a. Chest size (inches) 41 54 44 55 39 51 Weight (pounds) 328 528 418 580 296 503 a.What is the regression equation? b. The best predicted weight for a bear with a chest size of 39 inches is _______pounds.
A large school district is reevaluating its teachers' salaries. They have decided to use regression analysis to predict mean teacher salaries at each elementary school. The researcher uses years of experience to predict salary. The resulting equation was: Where Y=salary and X=years of experience. The raw data is given in the table below, Salary $54,000.00 $42,140.00 $36,195.00 $45,000.00 $58,950.00 $56,890.00 $53,250.00 $49,800.00 $35,820.00 $74,390,00 $28,900.00 $38,690.00 $78,070.00 $64,205.00 $20,000.00 Years of experience 13 9 7 9 14 13 14 11 8 22 5 8 23 18 2 a) Use Excel to estimate the regression equation (copy and paste your output). What is the coefficient on experience? b) What is the correlation coefficient? c) What is the coefficient of determination, and how do you interpret it? d) Discuss the overall significance of the model
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 58 inches. Is the result close to the actual weight of 572 pounds? Use a significance level of 0.05. Chest size (inches) 46 57 53 41 40 40 Weight (pounds) 384 580 542 358 306 320 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=nothing+nothingx (Round to one decimal place as needed.)
The following table gives the data for the grades on the midterm exam and the grades on the final exam. Determine the equation of the regression line, y = bo + b₁x. Round the slope and y-intercept to the nearest thousandth. Grades on Midterm and Final Exams Grades on Midterm 78 70 84 97 82 75 75 88 67 76 89 79 88 100 Grades on Final 71 80 77 77 72 65
Use the data given below to answer a -c. Heigh (IN) 71 70.5 71 72 70 66.5 70 71 Weight (lb) 125 119 128 128 119 127 123 105 Find the regression equation letting the first variable (height) to be the independent variable (x). Find the best-predicted weight for a person whose height is 69in. What percentage of the variation in weight can be explained by the linear relationship between height and weight.
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
Listed below are the numbers of commuters and the number of parking spaces at different Metro-North railroad stations. Use technology (calculator) to help you answer the following, and round to the 3 decimal places where rounding is necessary. . a. Find the linear regression line y = a + bx. b. Are the variables positively or negatively related? c. Find and interpret r2. Make sure to include what it means specific to this data set. d. Use your regression line to make a prediction for the number of parking spaces for a station with 900 e. Identify and interpret the slope of the linear model.

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