QUESTION 24Consider the following Stata regression estimates (IDFB2)SourceSSdfMSNumber of obs935%3DF(3, 931)114.07Model1211.30177403.767257Prob > F0.0000%3DResidual3295.517489313.53976099R-squared0.2688%DAdj R-squared0.2664Total4506.819259344.82528828Root MSE1.8814educСoef.Std. Err.tP> |t|[95% Conf. Interval]age.0074829.0198281urban.25461611367932IQ.0750274.0040968_cons5.439236.7981253What is the value of the standard error of the constant?IL || || || ||||

There are 3 independent variables which are age, urban, IQ. The dependent variable is y. We have to…

Answered: Consider the following Stata 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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Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.73360.7336 R Square 0.53810.5381 Adjusted R Square 0.51800.5180 Standard Error 2140.27632140.2763 Observations 49 ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 245,430,999.7671245,430,999.7671 122,715,499.8836122,715,499.8836 26.789226.7892 1.9E-081.9E-08 Residual 4646 210,716,007.0084210,716,007.0084 4,580,782.76114,580,782.7611 Total 4848 456,147,006.7755456,147,006.7755 Coefficients Standard Error t� Stat P-value Lower 95%95% Upper 95%95% Intercept 14276.146814276.1468 2,531.84252,531.8425 5.63865.6386 0.0000010040.000001004 9179.81229179.8122 19,372.481419,372.4814 Education (Years) 2349.95952349.9595 338.5500338.5500 6.94126.9412 0.0000000110.000000011 1668.49371668.4937 3031.42533031.4253 Experience (Years) 833.6183833.6183…
A multiple regression was estimated using the SEK method and the results are given below. Based on these estimates alone, multiple Explain the level of linearity, varying variance and cascading dependency problems. Bi: 4,1234 B2: -0,2189 B3: -1,7862 sh:3,1223 R?: 0,9820 R*: 0,9750 F: 10,89 F tablo:3,07 sh:4,4420 sh:0,3179 D.W.: 0,2558 d1:0,385 dy:1,685
The results of the regression analysis to estimate setup costs using the number of setup hours are as follows: SUMMARY OUTPUT Regression Statistics Multiple R 0.965127 R Square 0.931469 Adjusted R Square 0.921679 Standard Error 285.6803 Observations 9 ANOVA df SS MS F Regression 1 7765004 7765004 95.14395 Residual 7 571292.5 81613.21 Total 8 8336296 Coefficients Std Error t Stat P-value Intercept 2498.644 680.6304 3.671073 0.007953 X Variable 1 2.506915 0.257009 9.754176 2.52E–05 What percentage change (rounded to one decimal place) in setup costs can be explained by changes in setup hours? a.95.1% b.96.5% c.25.7% d.none of these e.93.1%
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.73650.7365 R Square 0.54240.5424 Adjusted R Square 0.52250.5225 Standard Error 2124.60962124.6096 Observations 4949 ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 246,127,958.1791246,127,958.1791 123,063,979.0896123,063,979.0896 27.262927.2629 1.6E-081.6E-08 Residual 4646 207,642,442.8821207,642,442.8821 4,513,966.14964,513,966.1496 Total 4848 453,770,401.0612453,770,401.0612 Coefficients Standard Error t� Stat P-value Lower 95%95% Upper 95%95% Intercept 14256.268814256.2688 2,513.30952,513.3095 5.67235.6723 0.0000008950.000000895 9197.23929197.2392 19,315.298419,315.2984 Education (Years) 2353.85412353.8541 336.0719336.0719 7.00407.0040 0.0000000090.000000009 1677.37651677.3765 3030.33173030.3317 Experience (Years) 832.8371832.8371…
Analysis of Variance Source DF SS MS Regression 1 Residual Error 13 0.2364 Total 14 11.3240 What is the value for MSR (Mean Square for Regression)?
Please help interpret this SPSS output of a multiple regression analysis result? DV- To what extent the product safety incident have changed your perceptions of the involved brand(s) IV'S- Trustworthy , Reliability , I don’t know how good the company’s products will be before I buy it. , Preventability
The following output comes from regression using the actual HW9 scores and the Final test scores from intro stats one semester. Sample size: 50R (correlation coefficient) = 0.3658R-sq = 0.1338Estimate of error standard deviation: 10.36256Parameter estimates: Parameter Estimate Std. Err. DF T-Stat P-Value Intercept 43.558118 11.1716 48 3.899 0.0003 Slope 0.360048 0.132225 48 2.723 0.009 Assume no assumptions are violated. Form a 87% Confidence interval for how much your final is supposed to increase for each problem done on homework 9.Use 5 decimal places
4. A simple linear regression is fit to a dataset. Unfortunately, the corresponding ANOVA table is not complete because some quantities in the table are missing due to unknown digital errors. Calculate the missing values, denoted by "?", in the ANOVA table based on the other available values. Is this regression model significant (α = 0.05)? Source Regression Residual Total df ? ? 21 SS ? ? ? MS = SS/df ? 1.13 F-Ratio 430.65 p-value ?
The following regression output can be used to predict the weight of a Shetland pony based on the animal's age: Regression Statistics Multiple R 0.97224608 R Square 0.94526245 Adjusted R Square 0.9270166 Standard Error 14.0700917 Observations Intercept Age Coefficients Standard Error 55.7 tStat 12.08558531 P-value 4.61142 0.01918 7.19771 0.00553 5.9 0.818914857 True or false? The pony's weight is related to its age at the .01 level of significance. A True B) False
Beachcomber Ltd in a local car dealership that sells used and new vehicles. The manager of the company wants to know how different variables affect the sales of his vehicles. A random sample of yearly data was taken with the view to testing the model: SALES=a+BAGE+YMIL+SENG Where SALES= amount that a vehicle is sold for($000's), AGE = age of the vehicle, MIL= the total mileage of the vehicle at the point of sale and ENG = the size of the engine. The sample of data was processed using MINITAB and the following is an extract of the output obtained:
Given the Analysis of Variance (ANOVA) for the simple linear regression as below: Table Q5: ANOVA Source df MS F Regression 1 252378 E Error A 5482 D Total 24 в (a) Calculate A, B, C, D and E. (b) At a = 0.05 test whether the regression model are fit.

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