Suppose the following data were collected relating the selling price of a house to square footage and whether or not the house is made out of wood. Use statisticalsoftware to find the regression equation. Is there enough evidence to support the claim that on average wood houses are more expensive than other types of houses atthe 0.05 level of significance? If yes, type the regression equation in the spaces provided with answers rounded to two decimal places. Else, select "There is not enoughevidence."Copy DataSelling Prices of HousesPriceSqftWood (1 if wood, O if otherwise)254350367512343403629260071360919829021951194451230717782516131954212221122037528691185020176011801791830 172396164212084572585119281720701227427312115188913021227688320911805651993231283337325315934892160002820

Regression Analysis Regression analysis is a technique to evaluate the relationship between the…

Answered: Suppose the following data were…

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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Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right 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 85 mm Hg. Use a significance level of 0.05. Right Arm 100 99 93 79 78 P Left Arm 177 170 150 148 148 Click the icon to view the critical values of the Pearson correlation coefficient r |x. (Round to one decimal place as needed.) The regression equation is y = Given that the systolic blood pressure in the right arm is 85 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.)
** if there is not enough evidence to fill in the formula please clarify.
The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. With the aim of predicting the used selling price from the number of miles driven, we might examine the least-squares regression line, y=41.57 – 0.49.x. This line is shown in the scatter plot in Figure 1. Used selling price, Mileage, x (in thousands) (in thousands of dollars) 25.9 26.1 28.1 26.2 40- 21.1 31.4 24.0 27.5 35 27.2 30.9 38.7 21.4 30. 34.6 25.5 37.2 23.5 15.6 34.0 25- 23.8 28.0 20.9 30.9 20. 23.1 32.7 28.0 30.3 40 29.2 28.1 Figure 1 24.0 29.6 23.0 31.5 Send data to Excel
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.)
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 Price = B + ¢Bedroom + yHouse Size + ALot Size Predictor Coef SE Coef т P 37718 14177 2.66 ** Constant Bedrooms 2306 6994 0.33 0.742 House Size 74.3 52.98 0.164 Lot 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 P Regression 3 76501718347 25500572782 *** **** Residual 96 60109046053 626135896 Error Total 99 a) Write out the regression equation. b) Fill in the missing values *, ** *** and ****
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 Price = B + Bedroom + yHouse Size + ALot Size Predictor Coef SE Coef T P Constant 37718 2.66 ** Bedrooms 2306 0.33 74.3 -4.36 House Size Lot Size S-25023 Error Total R-Sq-56.0 % 96 99 14177 6994 52.98 17.02 0.742 0.164 -0.26 0.798 Source DE SS MS F Regression 3 76501718347 25500572782 *** Residual 60109046053 626135896 R-Sq (adj) -54.6% P d) Is y significantly different from -0.5? e) Perform the F test at the 1% level, making sure to state the null and alternative hypoteses. f) Give an interpretation to the term “R-sq” and comment on its value.
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…
Is the price of a smartphone affected by its display size, camera resolution, or internal storage capacity? Perform a regression analysis on the SmartphoneDCS dataset and answer the following questions. The level of significance is .05. Solve this problem using MegaStat, and write your complete answers in the Response Box so I know that you can interpret the output. Attach your MegaStat file so I can see that you used the correct analysis method to solve this problem. Download: SmartphoneDCS a. Do the data conform to Evans' Rule? What about Doane's Rule? Show the calculations to explain the reason for your answers. b. State the decision rule for this analysis, and be sure to include the critical value(s). c. Perform the regression and write the complete regression equation. d. Is the overall fit of the model significant or not? Use the output from the ANOVA table and your answer to part (b) to explain your decision. e. What are the values for the coefficient of determination and…
It is inferred that the tip amount has a relationship with value of a dinner bill. The collected data is presented in the table. Conduct a regression analysis. X - Bill ($) 33.46 50.68 87.92 98.84 63.60 107.34 Y - Tip ($) 5.70 5.00 8.08 17.00 12.00 16.00
Write the formula for the estimated regression line and interpret the slope of the estimated regression line, the intercept of the estimated regression line- Is it meaningful?, and the estimated R2. Based on the fitted regression model, what is the predicted ATST for a child who is 7 years old? What is the correlation between AGE and ATST? Does the residual plot suggest that the fitted regression line is inappropriate for these data? Explain why or why not. Suppose that a new subject is added to the study data and that subject is 12.5 years old with an ATST of 580 minutes. If the regression model were to be refit with this additional data point, would the new slope be greater than or less than -14.041? Justify your response.
A vocational counselor uses the number of days without employment to predict her clients' feelings of self efficacy, measured on a scale of 1 to 5, with higher numbers meaning that clients feel more secure in their job related abilities. The slope of the regression line is –1.02. Which statement is the best interpretation for this finding? a. For every 1-point increase in self-efficacy, there is an associated decrease in the number of days of unemployment. b. For every additional day of unemployment, there is an associated decrease in self-efficacy of 1.02 points. c. The least number of days a person can be unemployed and still feel self-efficacious is 3.98 points d. The decrease in self-efficacy of 1.02 points is caused by each additional day of unemployment
The following data represent the number of flash drives sold per day at a localcomputer shop and their prices.Price Units Sold34 336 432 635 530 938 240 1a. Develop the estimated regression equation that could be used to predict thequantity sold given the price. Interpret the slope.b. Did the estimated regression equation provide a good fit? Explain.c. Compute the sample correlation coefficient between the price and the number offlash drives sold. Use a= 0.01 to test the relationship between price and units sold.d. How many units can be sold per day if the price of flash drive is set to $28.

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