You are given a set of data (xj, Yi) for linear regression analysis and find out these data are not linearly related by plotting (x¡, Yi) with graphical analysis. After using logtransformation of data from (x¡ , yi) to (X¡ = log X¡ , Y¡ = log yi ) and a linear fitting line were derived, Y = A + BX, for the power-law relationship y = k x". You tried to findthe power-law equation's kand n by transforming back from A and B in that linear relation equation Y = A + BX. Which one of the below can be the correct result?There might be more than one correct answer; please select all of the correct answers.O logA=kO n= logBO 10A=KO en=Bn = BO A=logKO eK=AO eB=n

From the given information, xi, yi for linear regression analysis and out these data are not…

Answered: You are given a set of data (xj, y¡)…

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 relationship between the entrance exam score (x) of MBA students and their GPA (y) upon graduation is given by ŷ = -0. 57 +0. 013x Based on this model, the predicted GPA of a student who had 200 for the entrance exam score is (Keep 2 decimal places) and the value of the slope of this fitted regression line is (Keep all the decimal places)
Data is collected on the distance of several hikes (in miles), along with the amount of time (in minutes) the hike is expected to take. All hikes in the data set are between 0.5 miles and 10 miles long, and the relationship between distance and time is linear and strong. The regression equation to predict time based on distance is as follows: Predicted time = –266 + 31.48 (distance). Suppose we want to use the regression equation to predict the time it takes to complete a particular hike. For which one of the following distances would using the regression equation result in extrapolation? 1 mile 4.5 miles 6.8 miles 9 miles None of the above distances would result in extrapolation.
Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed lemon/crash data, where lemon imports are in metric tons and the fatality rates are per 100,000 people, find the best predicted crash fatality rate for a year in which there are 425 metric tons of lemon imports. Is the prediction worthwhile? Lemon Imports Crash Fatality Rate 226 270 16 15.8 364 481 521 15.5 15.4 14.9
A particular article presented data on y = tar content (grains/100 ft³) of a gas stream as a function of x₁ = rotor speed (rev/min) and x₂ = gas inlet temperature (°F). The following regression model using X₁, X2, X3 = ×₂² and ×4 = X₁X₂ was suggested. (mean y value) = 86.5 – 0.121x₁ +5.07x2 - 0.0706x3 + 0.001x4 (a) According to this model, what is the mean y value (in grains/100 ft³) if x₁ = 3,400 and x₂ = 55. grains/100 ft³ (b) For this particular model, does it make sense to interpret the value of ₂ as the average change in tar content associated with a 1-degree increase in gas inlet temperature when rotor speed is held constant? Explain. Yes, since there are no other terms involving X2. O Yes, since there are other terms involving X₂. ● No, since there are other terms involving X2. O No, since there are no other terms involving X2.
Use the linear regression model yˆ=−24.8x+564.38 to predict the y-value for x=63.
Use the linear regression model yˆ=−21.9x+927.93 to predict the y-value for x=37.
Given that the systolic blood pressure in the right arm is 90 mm Hg, the best systolic blood pressure in the left arm is how many mm Hg?
A weight-loss clinic wants to use regression analysis to build a model for weight loss of a client (measured in pounds), Two variables thought to affect weight loss are client's length of time on the weight-loss program and time of session These variables are described below: Y-BO+B1'X+82'D 83'X'D+E Y-Weight loss (in pounds) X- Length of time in weight-loss program (in months) D-1 if morning session. O if not in terms of the Bs in the model, what is the difference between the weight loss of an individual who has spent 3 months in the program when attending the morning session, and an individual who has spent 2 months in the program when attending the evening session? OB1+83 OB1+82-83 OB1+82+283 O81+82+383
For10 observations on supply (X) and price (Y) the following data are obtained ΣΧ-130, ΣΧ-2280, ΣΥ-5506 , ΣΧΥ-3467, ΣΥ-220 Obtain regression line Y on X and estimate supply when price is 16.
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
It has been hypothesized that overall academic success for first-year college students as measured by grade point average (GPA) is a function of IQ scores = X1, and hours spent studying each week = X2. Suppose the regression equation is: Y = -5.7 + 0.02X1 +0.5X2 1) What is the predicted GPA for a student with an IQ of 100 and 40 hours spent studying per week? 2)Will the independent variables be endogenous? State what it means by endogenous, and explain why that will be the case. 3) If you have a choice to change the variables or add/drop variables, what would be your set of independent variables, and explain why you chose those variables.
What is the linear equation (y = mx + b form) that best approximates the relationship between advertising dollars spent(x) and sales revenue(y) based on the above 8 months of data? (round to 2 decimals for the slope and the y intercept)

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