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 SS 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

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 SS 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

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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APPLIED STATISTICS UPVOTE WILL BE GIVEN. PLEASE ANSWER THE PROBLEM BY USING SPSS OR EXCEL ONLY (NOT HANDWRITTEN). NO LONG EXPLANATION NEEDED. TAKE A SCREENSHOTS AND ANSWER THE QUESTION IN SENTENCES. The electric energy consumed each month by a manufacturingwarehouse is imagined to be linked to the ambient temperature, the number of days monthly, average product concentration, and products produced. The annual data based on last year are presented in the following table: a. Estimate the multiple regression equation. b. What is the r-squared value and what is its implication? c. What variable/s are considered significant? What is the p-value?
This model regresses Salary in a financial services company onto education Q3: [educ] measured as highest grade level attained in formal education. Fill in the blanks and answer the questions at the end [4 points each except where stated. SEE DESCRIPTIVE STATS AT THE END OF THIS QUESTION - YOU MAY NEED IT IN THE QUESTIONS BELOW Regression Analysis: salary versus educ Analysis of Variance Source DF MS F-Value Р- Value Regression 0.000 1 60178217760 60178217760 365.38 Error 472 (а) (b) Total 473 1.37916E+11 *** ***NOTE: This number = 1.37916 х 1011 Model Summary R-sq R-sq (adj) R-sq (pred) 43.51% 42.98% (c) (d) Coefficients Term Coef SE Coef T-Value P-Value VIF Constant -18331 2822 -6.50 0.000 educ 3910 205 0.000 1.00 (е) Regression Equation salary = -18331 + 3910 educ f) Calculate the confidence interval for the prediction of 'salary' when educ = 12 g) Perform a hypothesis test to determine whether the model is usable. Show your work.
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. A professor at the University of Alabama was interested in evaluating the relationship between family support and delinquency. Using data collected on 4545 families, the researcher used regression to analyze the relationship. The results are presented below. Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Family supportb . Enter a. Dependent Variable: Delinquency b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .249a .062 .062 1.59168 a. Predictors: (Constant), Family support ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 759.204 1 759.204 299.671 <.001b Residual 11479.107 4531 2.533 Total 12238.311 4532 a. Dependent Variable: Delinquency b. Predictors: (Constant), Family support…
Cautions: DO NOT use a regression analysis calculator. You can use a calculator for step by step analysis like calculating means, sum of the deviations of squares, e.g. but you are not allowed to use a whole regression analysis calculator.
See attached image for the introduction. How much variation in price has been explained by the total number of square feet?
See the attached image for the introduction. Question: Fill in a blank ANOVA table.
Hi I know that you can’t answer all questions. Can you just answer question 6 please.
The personnel director of a large hospital is interested in determining the relationship (if any) between an employee’s age and the number of sick days the employee takes per year. The director randomly selects ten employees and records their age and the number of sick days which they took in the previous year. Employee 1 2 3 4 5 6 7 8 9 10 Age 30 50 40 55 30 28 60 25 30 45 Sick Days 7 4 3 2 9 10 0 8 5 2 Copy Data The estimated regression line and the standard error are given. Sick Days=14.310162−0.2369(Age) se=1.682207se=1.682207 Find the 95% confidence interval for the average number of sick days an employee will take per year, given the employee is 26. Round your answer to two decimal places.
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