SUMMARYOUTPUTRegressionStatisticsMultiple R0.503R SquareAdjusted R SquareStandard ErrorObservations50ANOVAdfMSSignificanceFRegression2728.7 5.190.004Residual24161.8525.3Total32348Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept132.623.515.89 091.03185.67Seconds-2.040.6380.002-3.401-0.839Female-64.131.4-2.26-137.33-8.07Seconds*Female0.832.80.0070.67Based on the regression results, when testing to see if the coefficient on FEMALE is different than -120, what isthe test statistic? (please express your answer using 2 decimal places)

Introduction: Denote β2 as the true slope of the independent variable, FEMALE.

Answered: SUMMARY OUTPUT Regression Statistics…

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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Table 4.1 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.99794806 Missing 0.99513164 Standard Error 1.64839211 Observations 20 ANOVA Significance F 2.66E-19 af MS F Regression 10561.07486 Missing 16 43.47514498 19 1295.585 Missing 2.717197 Residual Total 10604.55 Coeficients Standard Error t Stat P-Value Intercept 0.562 1.327 0.424 0.677 X1 0.959 0.038 25.245 0.000 X2 1.117 0.125 8.916 0.000 X3 1.460 0.066 22.185 0.000 Consider the output shown in Table 4.1. What is the value of the regression df? 1 3.
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…
|After running the regression analysis between independent and dependent variable the below output is available. SUMMARY OUTPUT Regression Statistics Multiple R 0.714528 R Square 0.510551 Adjusted R Square 0.44937 Standard Error 49.60803 Observations 10 ANOVA ignificance F 1 20536.44 20536.44 8.344902 0.020238 df SS MS F Regression Residual 8 19687.66 2460.957 Total 40224.1 Coefficientsandard Err t Stat P-value Lower 95%Upper 95%ower 95.0%pper 95.0% Intercept -112.208 186.0395 -0.60314 0.563118 -541.216 316.7995 -541.216 316.7995 X Variable 0.398147 0.137827 2.888754 0.020238 0.080319 0.715976 0.080319 0.715976 a. What is the equation of the line? What can you say about the relationship between the two variables?
what is the estimated regression equation.?
Tutorial 8: Multiple Regression The table below reports the results of the regression analysis on the relationships between salary data for n=14 randomly sampled systems analysts from a large population of such individuals and their years of experience (Exper) and years of postsecondary education (Educ): Variable Coefficients Standard Error I-stat p-value Intercept EXPER 2690 204.6 45511 16.92 0.000 825.7 4.04 0.002 EDUC 1603.7 536.3 2.99 0.012 s= 2156 R-sq = 0.672 R-sq (Adj) = 0.613 ANOVA Source DF SS MS F-stat P 0.002 Regression Residual Total 2 104862168 52431084 11.28 11 51149976 4649998 13 156012144 Prediction Interval 3 Salary 4 5 Predicted value 56467 6. 7 Prediction Interval (95%) 8 Lower limit 51548 9 Upper limit 61386 10 Interval Estimate of Expected Value (95%) Lower limit 11 12 55180 13 Upper limit 57755
Table 4.1 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.99794806 Missing 0.99513164 Standard Error 1.64839211 Observations 20 ANOVA Significance F MS F Regression Missing 10561.07486 16 43.47514498 19 10604.55 fissing 2.717197 1295.585 2.66E-19 Residual Total t Stat Coefficients 0.562 Standard Error P-Value Intercept X1 1.327 0.424 0.677 0.959 0.038 25.245 0.000 X2 1.117 0.125 8.916 0.000 X3 1.460 0.066 22185 0.000 Consider the output shown in Table 4.1. The Mean Square value is missing. What is its actual value? 3520 660 10561 31683
A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 2020 ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181 Total 1919 2078.6512078.651 Step 1 of 2: How many independent variables are included in the regression model
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
Big Time Corporation wanted to determine the relationship between its monthly operating costs and a potential cost driver, machine hours. The output of a regression analysis showed the following information (note: only a portion of the regression analysis results is presented here) SUMMARY OUTPUT Regression Statistics Multiple R 0.998041188 R Square 0.996086212 Adjusted R Square 0.994781616 Standard Error 917.1714273 Observations 5 ANOVA df SS MS F Significance F Regression 1 642276389.7 642276389.7 763.5208906 0.000104039 Residual 3 2523610.281 841203 427 Total 4 644800000 Coefficients Standard Errort Stat P-value Intercept 6679.617454 931.8558049 7.168080532 0.005592948 3714.03639 X Variable 9.796772265 0.354545968 27.63188178 0.000104039 8.66844876 What is variable cost per machine hour (rounded to the nearest cent)? OA. $6,679.62 OB. $917.17 OC. $9.80 OD. $.99 Lower 95% Upper 95.0% 9645.198517 10.92509577
The parallel trends assumption is a crucial assumption for which the following estimators? Multiple regression Differences-in-Differences First differences Instrumental Variable
Match the regression (linear model) assumptions with the corresponding graphical procedure for testing that assumption. ( Choose ] Std. residuals vs fitted values plot Normality v QQ plot Time Series plot (of responses or residuals) Independence Std. residuals vs fitted values Homogeneity of variance Time Series plot (of responses v Model fit Std. residuals vs fitted values v
Use the final model (attached) to predict the probability of cancer for this patient with the biomarker profile below AFP CEA CA125 CA199 CA50 5.05 10.7 8.12 20.62 57.39

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