Alumni Contributions Numbers for the amount of an alumnus's contribution (in dollars) and the years the alumnus has been out of school are shown. Theregression line equation is y' = 401.506-23.922x. The standard error of the estimate is S100.93. Find the 90% interval when x = 13 years. Round youranswers to two decimal places as needed.estYear x265 10 312400350 150 30050元Contribution y 250Send data to ExcelThe 90% rediction interval is|

Step 1: ObservationsX Y Xi2 Yi2 Xi . Yi…

Answered: Alumni Contributions Numbers for the…

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 show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 760 1119 1125 850 965 785 D Temperature (°F) 70.5 90.8 90.8 74.7 77.1 69.9 What is the regression equation? (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.)
Two variables are defined, a regression equation is given, and one data point is given. Hgt = height in inches Age = age in years of a child Hgt = 24.5+2.78(Age) The data point is a child 12 years old who is 60 inches tall. X Your answer is incorrect. (a) Find the predicted value for the data point and compute the residual. Enter the exact answers. Predicted value = i Residual = inches inches
please solve all parts as soon as possible
A regression was run to determine if there is a relationship between hours of study per week (x) and the final exam Scores (y). The results of the regression were: y=ax+b a=6.179 b=28.96 r²=0.937024 r=0.968 Use this to predict the final exam score of a student who studies 4 hours per week, and please round your answer to a whole number.
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 ₂2 = VIF₁ = (Round to three decimal places as needed.) Is there reason to suspect the existence of collinearity? OA. No. The VIF for each independent variable is less than 5. B. Yes. The VIF for each independent variable is greater than 5. OC. No. The VIF for each independent variable is greater than 5. OD. Yes. The VIF for each independent variable is less than 5. wwwwwww Table of Data Standby Total Staff Hours Present 245 330 274 358 195 197 117 153 115 275 200 236 336 339 321 303 286 329 352 323 Remote Hours 417 655 524 385 349 350 388 153 278 479 Print Done
Explain how to find se
Write down the two regression equation.
The volume (in cubic feet) of a black cherry tree can be modeled by the equation y = - 50.8 + 0.3x, +4.5x,, where x, is the tree's height (in feet) and x, is the tree's diameter (in inches). Use the multiple regression equation to predict the y-values for the values of the independent variables. x, = 70, x, = 8.6 The predicted volume is cubic feet. (Round to one decimal place as needed.)
The linear regression equation for predicting systolic blood pressure from age is: y = 54 + 1.6*x Find the residual for a person who is 32 years of age with a systolic blood pressure of 103.9 (round your answer to one decimal place)
The regression equation between two variables X and y is Y =1.6+2.2 X Find the predicted Y value for X =13?
The slope, b represents * O predicted value of Y when X = 0. O the estimated average change in Y per unit change in X. O variation around the line of regression. O the predicted value of Y.

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