The data used to fit the model in Exercise 1 are presented in the following table, along withthe residuals and the fitted values. Plot the residuals versus the fitted values. Does the plotindicate that the linear model is reasonable? Explain.Strength Manganese Thickness ResidualFitted Value47.77.48.0-0.11147.81150.98.810.0-0.70951.60951.78.810.00.09151.60951.98.810.00.29151.60950.08.17.1-0.51750.51750.58.19.00.79049.71050.08.17.1-0.51750.51749.78.19.0-0.01049.71050.68.19.00.89049.71047.77.27.20.21447.48647.17.37.8-0.46447.56445.07.311.8-0.86445.86447.67.38.00.12147.47945.77.311.8-0.16445.86447.07.38.7-0.18147.18145.77.311.7-0.20645.90648.87.38.71.61947.18145.87.37.8-1.76447.56448.57.39.01.44647.05448.67.67.80.04048.560

Scatterplot: A scatterplot is a type of data display that shows the relationship between two…

Answered: The data used to fit the model in…

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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Give the data: Y. 12,14, 15, 17,18, 15,16 X1. 8,8,6,5,3, 4,7 X2. 0,1,1,2, 3,3,4 Obtain the least square estimates of the parameters in the multiple regression model and test the over all significance using Anova.
A simple regression model developed for ten pairs of data resulted in a sum of squares of error, SSE = 125. The standard error of the estimate is 12.5 3.5 25 15.6 O 3.95
Interpret the model estimates for age and gender.
A. run a simple regression- dependent variable is Weeks, independent variable is Age. B. run a multiple regression with dependent variable weeks and independent variable-age, married, head, manager and sales. C. Create the regular and standardized residual plots for both. Please show the tables when entering values of the regression for both the outputs and the scatter plots.
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Data were gathered and displayed on the scatter plot. 15 y 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Which of the following is the best equation to model the data? ŷ = 11.5(0.81)* Oŷ = 8.1(0.115)* Oŷ= -0.115x+8.1 Oŷ=-0.81x+11.5
Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.1 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) 8.1 7.6 8.9 7.7 8.6 9.8 Weight (kg) 156 166 213 138 197 251 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y = (Round to one decimal place as needed.)
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 40 inches. Is the result close to the actual weight of 352 pounds? Use a significance level of 0.05. Chest size (inches) *Weight (pounds) 44 54 328 528 41 55 39 51 418 580 296 503 Click the icon to view the critical values of the Pearson correlation coefficient r. - What is the regression equation? x (Round to one decimal place as needed.)
A study was conducted to determine the relationship between starting salaries (RM thousands) for recent statistics graduates and their grade point averages in the major course. A linear regression model was fitted to the data and the estimates regression function was obtained. Part of the computer output for the above analysis is given below: ANOVA Model Sum of df Mean F Sig. Squares Square Regression 147.28 .000 Error 734.9 40.828 Total 6748.2 Coefficients Unstandardized Coefficients Model Sig. Std. B Error Constant GPA -8.42 3.007 3.395 0.2477 -2.48 12.14 0.011 0.000 (a) Complete the ANOVA table (blue boxes). (b) Write down the estimated regression function. Interpret the estimated parameters. (c) Test whether there is a linear association between salaries and grade point average. Use a = 0.05. (d) Determine the coefficient of determination for the model and interpret its meaning.
The attached images show linear regression analysis to evaluate the ability of independent variables full and part-time FTEs, number of Medicare certified beds and urban vs. rural setting to predict dependent variable, occupancy rate. How do you interpret these results, what are the basic assumptions for regression analysis?
Explain using evidence from graph a and graph b which graph indicates that the model for the data is a good fit .
The service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression shown below. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782 Total 2628.025 49 a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 20.2 4.357 4.636 .000 Hourly Wage -1.20 .088 -.946 -13.636 .000 a. Dependent Variable: Number of…

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