11A.DStyles StylesPane4. Below is a residual plot of a linear regression, please select the statement that is true.✓ Aˆ AAe x₂Residuals(Size)2.5020015010050-50-100-150-200vAa v P2 ΑνResiduals(Size)/Age"60Age100ell beat attained120a. All the assumptions are met.b. There is problem of heteroscedasticity.c. There is problem of collinearity.d. We should include higher order terms.2↓V¶ANOVADictate Sensitivity

Homoscedasticity is one of the major assumption that need to verify while using regression model. if…

Answered: 4. Below is a residual plot of a linear…

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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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
6, 5. Kathy painted a picture and posted it on a social media site. The table shows the number of people who responded that they liked the picture since Kathy posted it. Time Since Picture Was Posted 5. 7. (days) Number of Likes 11 21 38 Write the regression equation that best models the data. a. y = 0.96(1. 55)* b. y = 2.07(1. 38)* c. y = 0.536x – 0.857x + 1.81 d. y = 1.38x´ + 0.30x + 0. 43
A high R2 is all that is needed to determine if a regression is a good model of a causal process. A. True B. False
Select the appropriate interpretation for the slope of the linear regression equation below. Y (Dependent Variable) = Grade Point Average X (Independent Variable) = Average number of hours spent using electronic devices for entertainment purposes yhat = 4 - 0.125*X A. For every 1 hour more spent using electronic devices for entertainment per week then a person's GPA will increase on average by 0.125 points B. For every 1 GPA gained obtained by a student then on average that person will have watched 0.125 hours fewer of entertainment on electronic devices per week C. For every 1 GPA point lost by a student then on average that person will have watched 0.125 hours more of entertainment on electronic devices per week D. For every 1 hour more spent using electronic devices for entertainment per week then a person's GPA will decrease on average by 0.125 points
Please do not explain, just say the answer like A,B,C,D,
6 Given the following multiple linear regression where shell (shell gas stock) price. shell = 15 - 0.5 * (interest rate) + 0.4 * (oil price) + 0.003 * (S&P500). Estimate the price of shell when the interest rate is 22, oil price is $95, and S&P500 = 4000 A.64 B. 38 C. 15 D. 12
Refer to the data below which contains the percentage change (X) in a stock market index over the first five trading days of the year and percentage change (Y) in the index over the whole year. Complete parts a and b. (X) (Y) 1.4 0.2 14.7 - 9.4 -0.3 2.9 19.6 20.2 a. Estimate the linear regression of Y on X. A y₁ = taxi (Round to four decimal places as needed.) 2.2 - 3.8 - 1.9 27.7 - 1.3 5.7 22.6 2.4 -1.5 1.3 11.8 26 1.5 - 4.4 - 4.4 20.5 0.7 - 1.0
When examining a plot of the residuals produced by the regression model, which of the following statements are true The residuals should be both positive and negative values. They should expand outward, producing a conical shape as your predicted y value increases in size. The residuals should show a clear positive relationship. Low values of your independent variable should produce negative residuals, while high values of your independent variable should produce positive residuals. The residuals should produce a clear u-shaped patter. All values should be positive. The residuals should appear to be random, with a horizontal band around the x- axis. They should be both positive and negative values.
please assist with this non graded problem.
Needed to be solved part 36 and 37 correclty in 30 minutes and get the thumbs up. Please show neat and clean work
19
Q10. price based on two features. Suppose we are going to apply multivariate Linear Regression to predict the house x1 = age of the house (0 – 40) x2 = quality of neighbouring schools (0 – 3) h(8) = 0, + 8,x1 + 02x2 Suggest one way to scale the features. Describe what will happen if feature scaling is not performed before the training process is conducted.

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