Exercise 3- Curve Fitting with PolynomialThe following data points were obtained from an experimental testXy00.55010.33420.33653.750342.2940.956a) Determine the second order polynomial that best fits the data points above.b) Make a plot of the data points and the curve that corresponds to thepolynomial.

The least-square equation is used to determine the equation which best fits the data set. If the…

Answered: Exercise 3-Curve Fitting with…

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 regression analysis, the coefficient of determination R2 measures the amount of variation in y that is:
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=a+bx b=-0.66 a=25.376 r²=0.390625 r=-0.625 Use this to predict the number of situps a person who watches 2.5 hours of TV can do. place. Round to one decimal
C is for cabbage - A researcher wants to know if the head weight of the cabbage is a good predictor for ascorbic acid (vitamin C) content. From a random sample of 60 cabbages, the researcher recorded the ascorbic acid content in milligrams and the weight of the cabbage head in pounds. Head weight is the X variable and Ascorbic acid content is the Y variable in this scenario. R output Coefficients: Estimate Std. Error t value Pr(>ltl) < 2e-16 *** (Intercept) 77.574 HeadWeight -7.567 3.096 25.052 1.131 -6.689 9.75e-09 *** --- Residual standard error: 7.668 on 58 degrees of freedom Multiple R-squared: 0.4355, Adjusted R-squared: 0.4257 F-statistic: 44.74 on 1 and 58 DF, p-value: 9.753e-09 What is the lower bound for a 90% confidence interval for the true slope B1 of the regression line? Give your answer to 4 decimal places. Your Answer: Answer
Grades The data set below shows midterm scores froman Introductory Statistics course. a) Fit a model predicting the second midterm score fromthe first.b) Comment on the model you found, including adiscussion of the assumptions and conditions forregression. Is the coefficient for the slope statisticallysignificant?c) A student comments that because the P-value for theslope is very small, Midterm 2 is very well predicted from Midterm 1. So, he reasons, next term the profes-sor can give just one midterm. What do you think?
Match the coefficient of determination to the scatter diagram. The scales on the x-axis and y-axis are the same for each scatter diagram (a) R2 0.90, (b) R2 0.58, (c) R2 =1 (a) Scatter diagram Explanatory (b) Scatter diagram (c) Scatter diagram Explanatory III Explanatory
Tire pressure (psi) and mileage (mpg) were recorded for a random sample of seven cars of thesame make and model. The extended data table (left) and fit model report (right) are based on aquadratic model. Calculate R2. Describe what this value means in the context of the problem.
Test the following data table to see whether the data are quadratic. x 0 4 16 32 44 y 4 12 16 12 20 Calculate the first-order and second-order differences.
Obtain the descriptive statistics for each type of fertilizer. Interpret the result
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were: y=a+bx b=-0.997 a=20.107 r2=0.950625 r=-0.975 Use this to predict the number of situps a person who watches 0.5 hours of TV can do.Round to one decimal place.
An agricultural experimenter divided a tract of land into seven plots of equal size. Each plot was treated with different levels of fertilizer to determine whether the level of fertilizer application affects yield. The results are shown below: Plot F 5.5 Level of Fertilizer, X Yleld (in Ibs), Y 3.0 3.5 4.0 4.5 5.0 6.0 53 57 56 58 62 59 63 What is the simple power equation for the given pairs of data? OY = 1.6266X^0.0216 OY = 0.2109X^0.0216 OY = 1.6266X^0.2155 %3D OY- 0.2109X^0.2155 O none of the above
ŷ = 0.524(2.645)x ŷ = 2.645(0.524)x ŷ = 2.645x + 0.524 ŷ = 0.524x + 2.645
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=a+bx b=-0.993 a=29.135 r2=0.463761 r=-0.681 Use this to predict the number of situps a person who watches 6 hours of TV can do. Round to one decimal place.

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