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 themodel. 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 1VIF₂ =(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 DataStandby Total Staff RemoteHoursPresentHours244338413272357658198339523ITI197333382114326347156303351115282381276320159203353276238327473X

The given data is as follows: Standby HoursRemote HoursTotal Staff…

Answered: The data provided give the number 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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Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine y when x = 7.
The table gives the amount of money (in dollars) spent on football by a major university. Letting x represent the number of years since 2006, and letting y represent the amount of money spent on football, in thousands of dollars, use the regression capabilities of a graphing calculator to find the equation of the line of best fit. Round values off to the nearest hundredth. Then, use your equation to make the following predictions. Year 2006 Dollars spent on football 173,000 The equation of the line of best fit is: Hint 2009 2010 2011 2007 2008 19 000 210,000 223,000 243,000 271,000 Predict the amount of money that will be spent on football in the year 2022. Predict the amount of money that will be spent on football in the year 2041.
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What is the slope and intercept for the regression equation given this data?X = -5, 34, 56, 0, 61Y = 13, 63, 46, -23, 50
Input 'X' Family Median Percentiles OutPut 'Y' Percent Of Children Attending College 100 94 1 25 Find The Linear equation using the above data.
Please help me with the breakdown and steps on this equation.
Fill in all boxes for part a through c please. Thank you!
Use the Regression tool on the accompanying wedding data, using the wedding cost as the dependent variable and attendance as the independent variable. Complete parts a through c. Click the icon to view the wedding data. X a. What is the regression model? Wedding Cost = +(xAttendance (Round to three decimal places as needed.) Wedding Attendance and Cost Wedding Cost 62700 Attendance 300 54000 350 48000 150 40000 200 36000 250 32000 150 31000 250 29500 300 27000 250 27000 200 26000 150 25000 200 23000 200 22000 200 22000 200 21000 200 20000 100 19000 150 19000 200 18000 150 17000 100 16000 100 13000 150 8000 50 5000 50 Wedding Cost Attendance
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.)
Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child ( height), height of the mother ( mothersheight ), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS 208.008457 314.295372 522.303829 df 2 104.004228 8.49446952 37 MS 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared = Root MSE = = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .9567683 .4804261 34.92886 What are the null and alternative hypotheses…
Refer to the data set:(-1, 2), (1, 3), (1, 5), (2, 7), (3, 8), (4, 11)Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine y when x = 10
The file Galton on D2L contains the 928 observations Francis Galton used in 1885 to estimate the relationship between the heights of parents and the heights of their children. The column Children refers to the height (in inches) of a child, and the column Mid-Parents refers to the average height (in inches) of the mother and father of that child. You can download this file into Excel and Minitab. a. Calculate the regression Height of Children = a +b (Height of Mid-Parents). b. Calculate the average for Height of Children, and calculate the average Height of Mid-Parents. c. Create a new variable in Minitab which is the Height of Children measured in terms of deviations from its mean. Call this new variable y. Also, create a new variable in Minitab with is the Height of Mid-Parents measured in terms of deviations from its mean. Call this new variable x. Calculate the regression y = a + bx. You can create the new y and x variables in Excel of Minitab, whichever you find more convenient.…

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