Do a curve fit using the nonlinear regression method and use a polynomial model deformación esfuerzounitaria(Pa)0.134142032.060.2680.3570.6240.8921.161.432.052.593.033.794.374.825.175.445.715.936.066.26.336.476.556.64288890.44386796.04670860.15944582.121199688.21454794.42027059.42509692.629026943564590.94067908.444540154764279.249987015226228.15419281.45536492.35653703.25770914.15881230.35957072.66039809.8

Given the data as deformacion unitaria esfuerzo (Pa) 0.134 142032.06 0.268 288890.44 0.357…

Answered: Do a curve fit using the nonlinear…

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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In the picture, there is a summary of regression analysis. Please write the regression equation and interpret the p-value which is indicated in output as p-value: < 2.2e-16.
The annual expenditure for cell phones varies by the age of an individual. The average annual expenditure E(a) (in $) for individuals of age a (in years) is given below:a: 20, 30, 40, 50, 60, 70E(a): 502, 658, 649, 627, 476, 2131. Use quadratic regression to find the model that best represents the data.2. At what age is the yearly expenditure for cell phones the greatest? Round the answer to the nearest year. YOU MUST SHOW THE WORK FOR THIS PART!
What is the equation for a simple linear regression model with one predictor variable (x) and a response variable (y)?
Q5/ Use Linear Regression to fit the following data: 1 2 4 5 6. Y 4 9 10 10 8
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
Pls help with below homework-) Show transformation of switching equation into a regression equation.
The regression equation to predict the total world gross ticket sales from the opening weekend ticket sales is: WorldGross^=9.23+6.87⋅OpeningWeekend Interpret the y-intercept of the regression line in context.
Residual analysis is important when studying the potential for linear regression because it allows for assumptions to be ignored for a time while graphs are created. True O False
A particular article proposed a quadratic regression model to describe the relationship between x = degree of delignification during the processing of wood pulp for paper and y = total chlorine content. Suppose that the actual model is the following. y=245+ 75x-4x² + e (a) Mean chlorine content for x = 6 is ---Select--- mean chlorine content for x = 8. (b) What is the change in mean chlorine content when the degree of delignification increases from 8 to 9? What is the change in mean chlorine content when the degree of delignification increases from 12 to 13?
Give examples of where the use of regression analysis can be benificially be made.
Give an equation for a linear regression model for these data to predict the weight from the fuel capacity, Identify the vanables in your equation. • Justify how well your linear equation fits the model by discussing a scatter plot of the date and the correlation coetlicient in assessing the fit of your linear model. • Use your model to predict the total weight of a small motorcycle that has a tuel capacity of 6 gallons. and discuss the usefulness of this prediction.
A weight-loss clinic would like to have an equation to estimate the number of hours a person should exercise per week (Y), given their age (x1) and the number of calories they eat per day (x2). After collecting data, the clinic is able to perform linear regression and discovers that the correct coefficient for the "age" variable is 13, the correct coefficient for the "calories per day" variable is 23, and the "constant" is -16. Given this information, what would the prediction equation look like to allow the clinic to predict the number of hours of exercise given age and calories? Y =-16x1 + 23x2 + 13 OY= 23 - 16x1 + 13x2 OY= -16 + 13x1+ 23x2 OY = -16 +23x1 + 13x2

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