The local utility company surveys 12 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 appears below:Bill 13.45 + 4.39*SizeCoefficients Estimate Std. Error(Intercept) 13.45Size4.390.540.2We are 90% confident that the mean annual electric bill increases by betweendollars anddollars for every additional square foot in home size. Round your answers to three decimal places andenter in increasing order.

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Answered: The local utility company surveys 12…

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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The local utility company surveys 19 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 11.45 +3.26 size. Coefficients Estimate Std. Error (Intercept) Size 11.45 3.26 0.83 0.35 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
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the fight arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05. Right Arm 102 94 80 79 Left Arm 177 172 143 143 143 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y = 35.5+ 1.36 x. (Round to one decimal place as needed.) Given that the systolic blood pressure in the right arm is 90 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. H T 101 6 & 7 4 L' 101 √₁ √ 1. (,) Copyright ©2022 Pearson Education Inc. All rights reserved. Terms of Use | Privacy Policy | Permissions | Contact Us | U H J 8 K fio More P 87°F Next + X insert (4) prt sc backspace 7:50 PM 8/5/202 enter delet ho
The accompanying table shows a portion of data consisting of the selling price, the age, and the mileage for 20 used sedans. Selling Price 13,532 13,740 Age Miles 8. 61,456 54,394 11,954 9 42,428 Click here for the Excel Data File a. Determine the sample regression equation that enables us to predict the price of a sedan on the basis of its age and mileage. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) [If you are using R to obtain the tput, then first enter the following command at the prompt: optic scientific notation.] ipen=D10). This will ensure that the output is not in Pr ice + Age + Miles. b. Interpret the slope coefficient of Age. The slope coefficient of Age is -359.04, which suggests that for every additional year of age, the predicted price of car decreases by $359.04. O The slope coefficient of Age is -0.09, which suggests that for every additional year of age, the predicted price of car decreases by $0.09. The slope…
please do all parts! The estimated regression equation for this data set is y=4.4878+1.9549x. Part A: (in image) Part B: is the linear function is the appropriate regression function for this data set? Part C: do the residuals have a constant variance? Part D: are the residuals independent? Part E: are the error terms are normally distributed? y x22 821 818 846 2241 2254 2276 3258 3268 32
Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sq ft.The data pertaining to 100 houses were processed using MINITAB and the following is an extract of the output obtained:The regression equation is ????? = ? + ???????? + ?????? ???? + ???? ????PredictorConstantBedroomsHouse SizeLot SizeS= 25023Coef SE Coef 37718 141772306 699474.3 52.98 -4.36 17.02T P 2.66 ** 0.33 0.742* 0.164 -0.26 0.798R-Sq=56.0% R-Sq(adj)=54.6%Source DF SS MS F P Regression 3 76501718347 25500572782 *** ****ResidualError 96 60109046053 626135896 Total 99a) Write out the regression equation. b) Fill in the missing values *, **, *** and ****. c) Use the p-value approach to determine if ? is significant at the 5% significance leveld) Is ? significantly different from -0.5? e) Perform the F test at the 1% level, making sure to state the null and alternative hypotheses. f) Give an interpretation to the term “R-sq” and comment on…
Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sq ft. The data pertaining to 100 houses were processed using MINITAB and the following is an extract of the output obtained: The regression equation is ????? = ? + ???????? + ?????? ???? + ???? ????Predictor Coef SE Coef T PConstant 37718 14177 2.66 **Bedrooms 2306 6994 0.33 0.742 House Size 74.3 52.98 * 0.164Lot Size -4.36 17.02 -0.26 0.798 S= 25023 R-Sq=56.0% R-Sq(adj)=54.6% Source DF SS MS F PRegression 3 76501718347 25500572782 *** ****Residual Error 96 60109046053…
Use the following data to estimate a regression with femur length as the x-variable and tibia length as the y-variable. Femur Length Tibia Length 18.7 14.2 20.5 15.9 16.2 13.1 15.0 12.4 19.0 16.2 21.3 15.8 21.0 16.2 14.3 12.1 15.8 13.0 14.3 13.8 18.8 18.7 Is the following statement true or false: "The intercept of the regression is equal to (approximately) 3.59" True False
Below, we see a snippet of a dataset that consists of 128 observations and 15 variables. We present two models describing the linear relationship between the BodyFat and other variables. Density BodyFat Age Weight Height Neck Chest Abdomen Hip Thigh Knee Ankle 1 1.0708 12.3 23 154.25 85.2 94.5 59.0 37.3 21.9 2 1.0853 6.1 22 173.25 83.0 98.7 58.7 37.3 3 1.0414 87.9 99.2 59.6 38.9 4 1.0751 86.4 101.2 5 1.0340 100.0 101.9 63.2 42.2 6 1.0502 94.4 107.8 66.0 42.0 23.4 24.0 67.75 36.2 93.1 72.25 38.5 93.6 25.3 22 154.00 66.25 34.0 95.8 10.4 26 184.75 72.25 37.4 101.8 28.7 24 184.25 71.25 34.4 97.3 20.9 24 210.25 74.75 39.0 104.5 60.1 37.3 22.8 24.0 25.6 Biceps Forearm Wrist 1 32.0 27.4 17.1 2 30.5 28.9 18.2 3 28.8 25.2 16.6 32.4 29.4 18.2 5 32.2 27.7 17.7 6 35.7 30.6 18.8 Analysis of Variance Table Model 1: x$BodyFat ~ x$Neck Model 2: x$BodyFat x$Neck + xSWeight + x$Height F Pr (>F) Res. Df RSS Df Sum of Sq 1 250 13348.1 2 248 9461.4 2 3886.7 50.939 < 2.2e-16 *** --- Signif. codes: 0***…
Interpret the values of the regression coefficients (intercept and slope)in this context. Note whether the intercept is meaningful (valid) for this data set.
. 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…
Marks obtained by students in a Chemistry test was adjusted because of an error in one of the questions. The marks are now classified as:A. Primary dataB. Secondary dataC. Regression dataD. External data
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 appears below: Bill = 15.9 + 4.45*Size Coefficients Estimate Std. Error (Intercept) 15.9 0.3 Size 4.45 0.57 We are 98% confident that the mean annual electric bill increases by between dollars and dollars for every additional square foot in home size.

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