yX1*2X361 6048727238 416278ML44 43 4742 28 26514247MHL3819826 23 15361514MHL Consider the accompanying set of dependent and independent variables. The variable x3 is qualitative with H = High,M = Medium, and L=Low. Complete parts a and b below.Click the icon to view the set of variables.a. Using technology, construct a regression model using all three independent variables. (Let variable Cond1 be one ofthe dummy variables for x3 and label it x3 in the equation. Assign a 1 to High for this variable. Let Cond2 be anotherdummy variable for x3 and label it x4 in the equation. Assign a 1 to Medium for this variable.)Complete the regression equation for the model below, where x3 represents the dummy variable Cond1, and x4represents the dummy variable Cond2.X4ý =LCDxt CDX + (DXg+ CDXa(Round to three decimal places as needed.)

The data for the variables are given…

Answered: Consider the accompanying set of…

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 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
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 672 pounds? Use a significance level of 0.05. Chest size (inches) Weight (pounds) 46 57 53 41 40 40 384 580 542 358 306 320
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y 519 (a) x = 500 feet (c) x = 810 feet (b) x = 649 feet (d) x = 732 feet 775 619 508 491 474 36 53 47 44 43 37 Find the regression equation. y=x+ O (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Choose the correct graph below. OA. 60- 60- 60- 60- 800 Height (feet) 800 Height (feet) 800 Height (feet) Height (feet) (a) Predict the value of y for x = 500. Choose the correct answer below. A. 52 В. 40 C. 48 D. not meaningful (b) Predict the value of y for x = 649. Choose the correct answer below. A. 56 В. 48 O C. 40…
Critical Values of the Pearson Correlation Coefficientr a= 0.05 a= 0.01 NOTE: To test H, p-0 against H,: p#0, reject H, jr the absolute value of r is preater than the critical yalue in the table. 0.950 0.990 0.959 0.917 0.875 5 0.878 6 0.811 0.754 7 0.834 0.798 8 0.707 0.666 0.632 0.602 9 10 0.765 11 0.735 12 0.576 0.708 13 0.553 0.684 0.532 0.514 0.497 0.482 14 0.661 15 0.641 16 0.623 17 18 0.606 0.590 0.468 19 0.456 0.444 0.575 20 0.561 25 0.396 0.505 0.463 30 0.361 35 0.335 0.430 40 0.312 0.402 45 0.294 0.279 0.378 0.361 50 60 0.254 0.330 70 80 0.236 0.305 0.286 0.220 90 0.207 0.196 0.269 100 0.256 a= 0.05 a=0.01
You are conducting a multiple regression analysis to determine if there is a linear relationship between y, the number of applications to in-state universities and two factors: ×1, the state's unemployment rate and ×2, the average grant provided to in-state students. Ten observations are available. The analysis produced the estimated regression equation below. y = 6.5 + 2.2×1 + 4.6×2 The standard error for the intercept, unemployment rate are 9.34, and 1.23, respectively. Is the coefficient for the unemployment rate variable statistically significant at the 5% significance level? Explain. A) No, since stat = 1.39 for this coefficient, which is less than 2.365. B) No, since the p-value for this coefficient is .072, which is greater than 05 C) None of these D) No, since the p-value for this coefficient is .072, which is greater than .05
For the provided data, develop a regression model for overall satisfaction as a function of years of service and department that has the largest R. Note that the categorical variable department has multiple levels and will require the use of multiple dummy variables. Which department, if any, has the highest impact on roduction satisfaction? Production Production E Click the icon to view the job satisfaction data. Determine the regression model for overall satisfaction as a function of years of service and department that has the largest R. Let "Administrative" be the baseline department, let X, represent Maintenance, let X, represent Management, let X, represent Production, let X, represent Quality Control, and let Xg represent Shipping / Receiving, coding each department variable with a 1 if the person is in that department and 0 otherwise. In addition, let Xe represent Years. Enter the terms of the equation so that the X-values are in ascending numerical order by base. Select the…
The following data show the brand, price (S), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ý = 22.522 + 0.335x, where x = price ($) and y = overall score. Brand Price ($) Score A 180 78 B 150 73 95 63 70 58 E 70 38 35 26 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST = SSR = SSE = (b) Compute the coefficient of determination rt. (Round your answer to three decimal places.) 12 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. O The least squares line did not provide a good fit as a small proportion of the variability in y…
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 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 48 inches. Is the result close to the actual weight of 538 pounds? Use a significance level of 0.05.
The following data show the brand, price ($), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ý = 22.525 + 0.325x, where x = price ($) and y overall score. %3D Brand Price ($) Score 180 78 B. 150 71 C 95 59 70 54 E 70 40 35 28 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST SSR %3D SSE = (b) Compute the coefficient of determination . (Round your answer to three decimal places.) 1 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. O The least squares line did not provide a good fit as a large proportion of the variability in y…
Show your work please.
Refer to the data set: x -1 1 -2 3 0 2 y 9 2 15 1 4 1.5 Part a: Make a scatterplot and determine which type of model best fits the data.Part b: Find the regression equation, round decimals to one place.Part c: Use the equation from Part b to determine y when x = 5.

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