SUMMARY OUTPUT Regression Statistics Multiple R 0.503 R Square Adjusted R Square Standard Error Observations 50 ΑNOVA df MS Significance F Regression 2728.7 5.19 0.004 Residual 24161.8 525.3 Total 32348 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 132.6 23.51 5.89 91.03 185.67 Seconds -2.04 0.638 10.002 -3.401 -0.839 Female -64.1 31.4 -2.26 1-137.33 -8.07 Seconds*Female 2 0.83 2.8 0.007 0.67 Based on the regression results, when testing to see if the coefficient on FEMALE is different than -120, what is the test statistic? (please express vour answer using 2 decimal places)

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

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

Are the heights of individuals affected by the heights of their parents. Regression Statistics Multiple R R Square Adjusted R Squar 0.631071992 0.398251859 0.365724932 Standard Error 2.914527039 Observations 40 ANOVA df MS F Significance F Regression Residual 2 208.0084392 104.0042 12.24376 8.30181E-05 37 314.2953108 8.494468 Total 39 522.30375 Coefficients Standard Error t Stat P-value Intercept Mother's Height Father's Height 9.804326378 12.39987353 0.79068 0.43417 0.657952815 0.147476295 4.461414 7.34E-05 0.200358437 0.138223638 1.449524 0.155615 1. Write the regression equation that represents the above equation. 2. Is this a good predictor equation? Why or why not (use appropriate statistics/hypothesis test to prove your point)? 3. Use the equation to predict the height of someone whose mother is 52 inches tall and whose father is 70 inches tall.

Item9     eBook Item 9 The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars.   Predictor Coefficient SE Coefficient t p-value Constant   9.601     3.153     3.045     0.010   x1   0.221     0.110     2.009     0.000   x2 − 1.168     0.577   − 2.024     0.028   x3 − 0.106     0.073   − 1.452     0.114   x4   0.631     0.361     1.748     0.001   x5 − 0.043     0.029   − 1.483     0.112       Analysis of Variance Source DF SS MS F p-value Regression 5   1,844.31   368.9   9.37   0.000   Residual Error 56   2,205.62   39.39           Total 61   4,049.93                   x1 is the number of architects employed by the company. x2 is the number of engineers employed by the company. x3 is the number of years involved with health care projects. x4 is the number of states in which the firm operates. x5 is the percent of the firm’s work that is health…