(3) Report SSE, s², and s as shown on the output. (Round your answers to 4 decimal places.)SSEs^2(4) Calculate the Amodel) statistic by using the explained variation, the unexplained variation, and other relevant quantities. (Roundyour answer to 2 decimal places.)F(model)の We give JMP output of regression analysis. Above output we give the regression model and the number of observations, n, used toperform the regression analysis under consideration. Using the model, sample size n, and output:Model: y = 6e + 61x1 + 62x2 + 63x3 + &Sample size: n = 30%3DSummary of FitRSquareRSquare AdjRoot Mean Square ErrorMean of ResponseObservations (or Sum Wgts)ped0.9810830.9789000.2406718.382667nt30Analysis of VarianceSum ofМeanSourcedfSquaresSquareF RatioModel3.78.10440026.03480449.4761Error261.5059860.05790Prob > FC. Total2979.610387<.0001*

Given that From the given output

Answered: We give JMP output of regression…

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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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.
Describe the difference between the visually fitted and the calculated regression lines you placed on the scatterplot. Describe any discrepancy in the scatterplot of the raw data, and how it might explain the difference between your fitted lines. Does the difference between the visual fit and the regression fit reveal a flaw or a strength in the regression prediction, and why?
d) Hence test whether ? is significant. Give reasons for your answer.
See the attached image for the introduction. Question: Fill in a blank ANOVA table.
A real estate builder wishes to determine how house size (House) is influenced by family income (Income) and family size (Size). House size is measured in meter square and income is measured in IDR millions. The builder randomly selected 50 families and ran the multiple regression. Partial Microsoft Excel output is provided below: Which of the independent variables in the model are significant at the 5% level? Formulate the hypothesis and explain the answer. How far can you rely upon this model? Or, what is the percentage variation in House explained by the model? What is the predicted house size (in hundreds of square feet) for an individual earning an annual income of IDR 400 million and having a family size of 4?
Find the regression equation, lotting the diameler be the predictor (x) variable. Find the best predicted circumference of a marble with a diameter of 1.3 cm. How does the result compare to the achual circumference of 4.1 cm? Use a significance level of 0.05 Baseball Basketball Golf Soccer Tennis Ping Pong Volleyball 7.3 Diameter Circumference 23.5 4.4 21.9 7.1 3.9 21.4 22.9 73.8 13.8 68.8 22.3 12.3 67.2 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y = x (Round to five decimal places as needed.)
please answer in text form and in proper format answer with must explanation , calculation for each part and steps clearly
what % of the variation is ( height, or head circumference) explained by the least-squares regression model. (Round to one decimal place as needed.)
The best predicted circumference for a diameter 1.2 cm is?
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.)
Dex Research Limited conducted a research to investigate consumer characteristics that can be used to predict the amount charged by credit card users. The following multiple regression output is based on a data collected by this research company on annual income, household size and annual credit card charges for a sample if 50 consumers. Regression Statistics Multiple R 0.9086 R Square A Adjusted R Square 0.8181 Standard Error 398.091 Observations B ANOVA df SS MS F Significance F Regression 2 C E G 1.50876E-18 Residual D 7448393 F Total 49 42699149 Coefficients Standard Error t Stat P-Value Intercept 1304.9048 197.6548 6.6019 3.28664E-08 Income ($1000s) 33.1330 3.9679 H 7.68206E-11 Household size 356.2959 33.2009 10.7315 3.12342E-14 a. Complete the missing entries from A to H in this outputb. Estimate the annual credit card charges…
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.

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