U.S. Gross3.962020to this candy. (Be sure to include the units.)C) What does that residual say about her candy?Section 28.200000a50picture will make? We have data on a number of recentreleases that includes the USGross (in $M), the Budget(SM), the Run Time (minutes), and the average numberof Stars awarded by reviewers. The first several entriesin the data table look like this:What (if anything) doethe following Assumptregression?a) Linearity conditionb) Equal Spread condc) Normality assumptRun TimeBudget($M)USGrossMovie(minutes)Stars(W$)56.094360White Noise1012.6. Movie residuals Fortogram of the residuaAssumptions and CoCoach Carter67.264877451363.Elektra24.409722652.Racing Stripes49.7725221103.Assault on20.0408956040Precinct 13Are We There82.674398942.Yet?Alone in the5.1785691.5zeol -150Dark3.50s diw bosisIndigo51.10048625We want a regression model to predict USGross. Parts ofthe regression output computed in Excel look like this:a) Linearity conditib) Nearly Normal cc) Equal Spread coDependent variable is: USGross($)R squaredSection 28.447.4% R squared (adjusted) = 46.0%s = 46.41 with 120 – 4 = 116 degrees of freedomTanimy emil el oldeis7. Movie tests In theExercise 3,%D%3DDelSE(Coeff) t-ratio P-value -RVariableCoefficienta) What is the nullStars in this tabIntercept-22.989825.70-0.8950.3729Budget($)1.134420.12978.75b) What is the t-stc) What is the P-vd) Complete the bhypothesis?Stars24.97245.8844.24Run Time-0.4032960.2513 -1.600.1113a) Write the multiple regression equation.b) What is the interpretation of the coefficient of Budgetin this regression model?8. More movie testa) What is the nu4. Movie profits again A middle manager at an entertain-ment company, upon seeing the analysis of Exercise 3,concludes that longer movies make less money. He arguesthat his company's films should all be cut by 30 minutesto improve their gross. Explain the flaw in his interpretationof this model. 10 moioflooosRun Time in thb) What is the t-sc) Why is this t-sd) What is the P-do e) Complete thehypothesis?

From the given excel output, we have intercept a = -22.9898 Coefficient of budget variable b1=…

Answered: We want a regression model to predict…

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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Can youm please assist with a,b and c. Please do it clear enough so i can understand. Thanks
Question 9 of 15 You want to estimate a model on car production (units) based on the previous year data on the number of cars sold (units), price of cars ($/unit), and total sales of cars ($). The regression would be car production on the number of cars sold, price of cars, total sales of cars, and a constant. Describe the problem with this model. Maximum number of characters (including HTML tags added by text editor): 32,000 Show Rich-Text Editor (and character count)
The information below represents the relationship between the selling price (Y, in $1,000) of a home, the square footage of the home (), and the number of rooms in the home (). The data represents 60 homes sold in a particular area of East Lansing, Michigan and was analyzed using multiple linear regression and simple regression for each independent variable. The first two tables relate to the multiple regression analysis. Summary measures Multiple R 0.9408 R-Square 0.8851 Adj R-Square 0.8660 StErr of Estimate 20.8430 Regression coefficients Coefficient Std Err t-value p-value Constant -13.9705 49.1585 -0.2842 0.7811 Size 7.4336 1.0092 7.3657 0.0000 Number of Rooms 5.3055 8.2767 0.6410 0.5336 The following table is for a simple regression model using only size. (= 0.8812) Coefficient Std Err t-value p-value Constant 14.771 19.691 0.7502 0.4665 Size 7.816 0.796 9.8190 0.0000 The following table…
A doctor wanted to determine whether there is a relation between a male’s age and his HDL (so-called good cholesterol). He randomly selected 9 of his patients and determined their HDL cholesterol. The data is reported in the table below: a) Compute the regression equation for HDL as a function of age. b)Interpret the meaning of the regression parameters.
Use the regression line to make the appropriate prediction. A random sample of records of electricity usage of homes in the month of July gives the amount of electricity used and size (in square feet) of 135 homes. A regression was done to predict the amount of eleçtricity used (in kilowatt-hours) (y) from size (x). The residuals plot indicated that a inear model is appropriate. The model is y = 0.2x+ 1271. How much electricity would you predict would be used in a house that is 2471 square feet? 494.2 kilowatt-hours O 6000.00 kilowatt-hours O 1765.2 kilowatt-hours O 776.8 kilowatt-hours 3742.2 kilowatt-hours
The accompanying data are the number of wins and the earned run averages (mean number of earned runs allowed per nine innings pitched) for eight baseball pitchers in a recent season. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x = 5 wins (b) x = 10 wins (c) x = 19 wins (d) x = 15 wins Click the icon to view the table of numbers of wins and earned run average. The equation of the regression line is y = x+. (Round to two decimal places as needed.)
For a linear regression modelling how the vocabulary size of a sixth grader is affected by the amount of television watched per day, the residuals are defined as the differences between Pick the actual amounts of television watched in the dataşet the actual vocabulary sizes in the dataset the vocabulary sizes predicted by the regression line
The data provided give the number of standby hours based on total staff present, X₁, and remote hours, X₂. Perform a multiple regression analysis using the data provided and determine the VIF for each independent variable in the model. Is there reason to suspect the existence of collinearity? Click the icon to view the data. Determine the VIF for each independent variable in the model. VIF 1 VIF₂ = (Round to three decimal places as needed.) Is there reason to suspect the existence of collinearity? = A. No. The VIF for each independent variable is greater than 5. B. Yes. The VIF for each independent variable is less than 5. C. No. The VIF for each independent variable is less than 5. D. Yes. The VIF for each independent variable is greater than 5. Table of Data Standby Total Staff Remote Hours Present Hours 244 338 413 272 357 658 198 339 523 ITI 197 333 382 114 326 347 156 303 351 115 282 381 276 320 159 203 353 276 238 327 473 X
please assist, the question states to "click on the datafile logo to reference the data" but the data is listed already in the question. when clicking that link, it opens up the same information in excel so please do not reject because the data is there already.
please answer quickly
The accompanying data are the number of wins and the earned run averages (mean number of earned runs allowed per nine innings pitched) for eight baseball pitchers in a recent season. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x = 5 wins (c) x = 19 wins (d) x = 15 wins (b)x= 10 wins Click the icon to view the table of numbers of wins and earned run average. .. The equation of the regression line is y=x+ X+ (Round to two decimal places as needed.) Construct a scatter plot of the data and draw the regression line. Choose the correct graph below. OA. B. O C. O D. AERA AERA 6+ Q AERA 6+ 4- 4- 2- 2- 0- 6 12 18 24 0 12 18 24 6 12 18 24 Wins 12 18 24 Wins Wins Wins (a) Predict the ERA for 5 wins, if it is meaningful. Select the correct…
A diligent statistics student recorded the length of hisfaithful #2 pencil as he worked away on his homework.He discovered a strong linear relationship between thenumber of hours that he worked and the length of hispencil. Here is the regression analysis for these data.Dependent variable: length (cm)R2 = 92.3, R2 1adj2 = 89.5,coeff se t ratio p valueconstant 17.047 0.128 23.58 60.0001time (hr) -1.914 0.047 35.28 60.0001 a) Write the equation of the least square regressionline.b) Interpret R2 in this context.c) Interpret the equation in this context.d) This student’s girlfriend tried out his model on apencil she had used for 5 hours, and found a residualof -0.88 cm. How long was her pencil at that time?e) Should she have expected this model to describe therate for her pencils? Why or why not?

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