(d) Calculate the residual corresponding to the first observation. (Round your answer to two decimal places.)Calculate the residual corresponding to the second observation. (Round your answer to two decimal places.)(e) Calculate a point estimate of o. (Round your answer to two decimal places.)Interpret the above point estimate of a.O Porosity measurements deviate from the least squares line by approximately the point estimate of sigma on average.O Porosity measurements deviate from sample mean porosity measurement by approximately the square of the point estimate of sigma on average.O The unit weights of concrete specimens deviate from the least squares line by approximately the point estimate of sigma on average.O Porosity measurements deviate from the least squares line by approximately the square of the point estimate of sigma on average.O The unit weights of concrete specimens deviate from the least squares line by approximately the square of the point estimate of sigma on average. No-fines concrete, made from a uniformly graded coarse aggregate and a cement-water paste, is beneficial in areas prone to excessive rainfall because of its excellent drainage properties. The article "Pavement Thickness Design for No-Fines Concrete Parkiremployed a least squares analysis in studying how y = porosity (%) is related to x = unit weight (pcf) in concrete specimens. Consider the following representative data.y =LUSE SALTRelevant summary quantities are Σ×, = 1640.9 ΣΥ, = 299.1, Σx2 = 180,022.35, Σxy = 32,248.04, Σy} = 6405.49.(a) Obtain the equation of the estimated regression line. (Round all numerical values to four decimal places.)Create a scatterplot of the data and graph the estimated line.120y125-115)x 99.2 101.2 102.6 103.1 105.3 107.2 108.9 110.7y 28.7 28.0 26.9 25.1 22.9 21.4 21.0 19.4x 112.0 112.6 113.5 114.0 115.0 115.5 120.1y 16.9 19.0 16.2 16.5 12.9 13.5 10.7105110-100O1015202530Xy3025201510o100 105110 115120125y3025201510100 105110 115120X125y125120115110-105100O1015202530X

Answered: (d) Calculate the residual…

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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1. Write down the multiple regression function?2. What is the multiple correlation and the multiple standard error of estimate?3. What is the coefficient of multiple determination and what does it mean?4. Of the three independent variables, which independent variable is not significant as a predictor (α = 0.05)?
Part of an R output relating X (independent variable) and Y (dependent variable) is shown below. This analysis is based on 11 observations. Using what you know about how all of these values are related, fill in the remaining blanks. Round your answers to 4 decimals in Model Summary box. Round your answers to 2 decimals in the ANOVA table and beyond. Model Summary (4 decimal places each) R R-Squared Adj. R-Squared Pred. R-Squared Source Regression Residual Total ANOVA (2 decimal places each, where necessary) Sum of Squares X Model (Intercept) 0.3632 0.515 1000 RMSE Parameter Estimates (2 decimal places each) Beta Coef. Var. df 29.48 5.7871 Std. Error Std. Beta 0.699 Mean Square -0.72 t F Sig. 3.79 0.0043 -3.091 0.0129 Sig 0.0129
Use the information below to answer the PULL STRENGTH questions that follow. Data from "Applied Statistics and Probability for Engineers", Fourth Edition, by Douglas C. Montgomery & George C. Runger. The Excel multiple regression output below is for predicting pull strength from wire length and die height. MULTIPLE REGRESSION EXCEL OUTPUT (PULL STRENGTH) SUMMARY OUTPUT Regression Statistics Multiple R 0.990523843 R Square 0.981137483 Adjusted R Square 0.979422709 Standard Error 2.288046833 Observations 25 ANOVA df S MS Significance F Regression 5990.771221 2995.385611 572.1671503 1.07546E-19 Residual 22 115.1734828 5.235158308 Total 24 6105.944704 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 2.263791434 1.060066238 2.135518851 0.04409945 0.065348623 4.462234246 Wire Length 2.744269643 0.093523844 29.34299438 3.90691E-19 2.550313062 2.938226225 Die Height 0.012527811 0.002798419 4.476746229 0.000188266 0.006724246 0.018331377
(d) Predict the value of moisture content when frying time is 20, in a way that conveys information about reliability and precision. (Calculate a 95% PI. Round your answers to two decimal places.) %, (e) Analyze the residuals from fitting the simple linear regression model to the transformed data and comment. (Select all that apply.) O The patterns in the residual plot and the normal probability plot are marginally acceptable. O There are four positive standardized residuals. O The patterns in the residuals plot and the normal probability plot are unacceptable. O Looking at the residual versus fits, one standardized residual is a bit large. O Looking at the residual versus fits, no standardized residuals look large. O There are only two positive standardized residuals, but two others are essentially 0.
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The data are the ages of criminals and their victims. The regression output is shown in a separate tab from the data. Do the data support that a prediction of victim age can be obtained given the data provided in the file? Cite the elements of the output you used to draw your conclusion.
5 prac
Waterbury Insurance Company wants to study the relationship between the amount of fire damage and the distance between the burning house and the neorest fire station. This information will be used in setting rates for insurance coverage. For o somple of 30 claims for the last year, the director of the actuarial department determined the distance from the fire station (x) and the amount of fire domage, in thousands of dollars (y). ANOVA table Source Regression Residual SS df MS F 1,870.5782 1,870.5782 41.39 1,265.4934 3,136.0716 28 45.1962 Total 29 Regression output Standard Variables Coefficients Error t(df-28) Intercept Distance-X 13.76815 3.106 2.914 3.77es e. 5861 6.43 Click here for the Excel Data File a-1. Determine the regression equation. (Round your answers to 3 decimal places.) y3D X. a-2 Is there a direct or indirect relotionship between the distance from the fire station and the amount of fire damage? The relationship between distance and damage is b. How much domage would…
Q) Plot the relationship between total operating costs and machine hours. Draw the regression line.
If a regression model of the form y= B,+B,x,+... + B,x, is fit to 132 observations on each variable and yields an R´value of 0.87, fill in the blanks in the following ANOVA table. Do all calculations to at least three decimal places. Source of Degrees of freedom Sums of Mean F statistic variation squares squares Regression 69 Error Total
Applied statistics Describe

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