Question 9The following table gives information on the limited tread warranties (in thousands of miles)and the prices of 12 randomly selected tires at a national tire retailer as of July 2009.Warranty(thousands of 60 70 75 50 80 55 65 65 70 65 60 65miles)Price per tire(RM)9570 94 90121Taking warranty length as an independent variablefind the least square regression line, y = a + bx70 84 80 92 79 66 95price per tire as a dependent variable,

It is given that the warranty length as an independent variable(x) and price per tire as a dependent…

Answered: Question 9 The following table gives…

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 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.
Outside Sr # Tempera (Number Sales X(outsid Y(sales) X*2 Y*2 Y=-5 e ture of Air tempera (Degrees Conditio ture) F) ning units sold) 68 3 68 3 4624 204 1.636 72 72 5 5184 25 360 4.883 78 7 3 78 7. 6084 49 546 9.754 81 12 4 81 12 6561 144 972 12.19 84 15 84 15 7056 225 1260 14.62 86 16 6 86 16 7396 256 1376 16.25 89 22 89 22 7921 484 1958 18.68 91 18 91 18 8281 324 1638 20.31 93 19 9 93 19 8649 361 1767 21.93 94 26 10 94 26 8836 676 2444 22.74 836 143 7059 2553 12525 143 sum Y= Plot a scatter diagram for the data provided on the table above and the linear regression line calculated in topic (b). Consider that: Y: number of air conditioning units sold X: outside temperature (degrees F) rds English (U.S.) Text Predictions: On acer
c. Which of the following is the correct graph of the given data? -ОА. 10 1012 12 10 O C. 12- 81012 1012 Using the graph, explain the dramatic difference between the answers to parts (a) and (b). Choose the correct answer below. OA. The point (10,10) is not an outier, but it does have a strong effect on the least squares line and the correlation coefficient. OB. The point (10,10) does not have any effect on the least squares line and the correlation coefficient. OC. The point (10,10) is an outler that has a weak effect on the least squares line and the correlation coefficient. OD. The point (10,10) is an outlier that has a strong effect on the least squares line and the correlation coefficient.
From the scatter plot, which of the following is TRUE? Scatterplot of Apartment Price vs Distance Apartment Price (in RM 1000) 250 225 200 175 150 0 1 Distance from Commercial Area (km) O a. We can conclude a significant linear relationship between apartment price and distance from the commercial area at a 1% significance level. regression line, the smaller the r. O b. The value of the coefficient of correlation is negative.
Below are the results of Multiple Regression Analysis. Write down the interpretation of the following tables: R Model R Square .560a .314 Change Statistics F Change df1df2 1 .314 7.210 9 142 a. Predictors: (Constant), Horsepower, Wheelbase, Fuel efficiency, Width, Price in thousands, Fuel capacity, Length, Engine size, Curb weight b. Dependent Variable: Sales in thousands Adjusted R Square Model Summaryb .270 Std. Error of the Estimate 58.895390 R Square Change Sig. F Change .000
Include the explanation please.
Data on the price ($1000s) and the size (1000 ft2 sqaured) for 156 randomly selected houses that have been sold is summarized in the accompanying regression output and plots. Complete parts a and b a) Check the assumptions and conditions for inference. Select all that apply. A. The residuals look random. B. The residuals are nearly normal. C. The scatterplot looks straight enough. D. The residual plot and the scatterplot show consistent variability. E. None of the assumptions and conditions for inference are satisfied.
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
The personnel director of a large hospital is interested in determining the relationship (if any) between an employee's age and the number of sick days the employee takes per year. The director randomly selects ten employees and records their age and the number of sick days which they took in the previous year. Employee 1 2 3 4 5 6 7 8 Age 30 50 40 55 30 28 60 25 Sick Days 7 4 3 2 9 10 0 8 Copy Data The estimated regression line and the standard error are given. Sick Days = 14.310162 - 0.2369(Age 9 10 30 45 5 2 Se = 1.682207 Find the 95% confidence interval for the average number of sick days an employee will take per year, given the employee is 26. Round your answer to two decimal places.
The table below shows the age of 8 individuals and the packs of medicine they consume each year. Age 17 27 54 35 42 31 46 23 Medicine (in packs) 17 7 12 6 2 a. Determine the correlation coefficient r between the two variables. Answer rounded to at least 4 decimal places. b. Determine the slope coefficient B1 for the regression of medicine packs consumed on age. Answer rounded to at least 4 decimal places. c. Determine the y-intercept Bo for the regression of medicine packs consumed on age. Answer rounded to at least 4 decimal places. d. What will be the expected packs of medicines consumed per year by a person of age 33. Answer rounded to at least 4 decimal places. packs

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