Consider the following Stata regression estimates (IDFB2) Source SS df MS Number of obs 935 F(3, 931) 114.07 Model 1211.30177 403.767257 Prob > F 0.0000 Residual 3295.51748 931 3.53976099 R-squared 0.2688 Adj R-squared 0.2664 %3D Total 4506.81925 934 4.82528828 Root MSE 1.8814 %3D educ Сoef. Std. Err. P>|t| [95% Conf. Interval] age .0074829 .0198281 urban .2546161 .1367932 IQ .0750274 .0040968 _cons 5.439236 .7981253 What is the value of the standard error of the constant?
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![QUESTION 24
Consider the following Stata regression estimates (IDFB2)
Source
SS
df
MS
Number of obs
935
%3D
F(3, 931)
114.07
Model
1211.30177
403.767257
Prob > F
0.0000
%3D
Residual
3295.51748
931
3.53976099
R-squared
0.2688
%D
Adj R-squared
0.2664
Total
4506.81925
934
4.82528828
Root MSE
1.8814
educ
Сoef.
Std. Err.
t
P> |t|
[95% Conf. Interval]
age
.0074829
.0198281
urban
.2546161
1367932
IQ
.0750274
.0040968
_cons
5.439236
.7981253
What is the value of the standard error of the constant?
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- A multiple regression analysis produced the following tables. Predictor Coefficients StandardErrort Statistic p-valueIntercept -139.609 2548.989 -0.05477 0.957154x 24.24619 22.25267 1.089586 0.295682x 32.10171 17.44559 1.840105 0.08869Source df SS MS F p-valueRegression 2 302689 151344.5 1.705942 0.219838Residual 13 1153309 88716.07Total 15 1455998Using = 0.01 to test the null hypothesis H :?1 = ?2 = 0, the critical F value is ____.6.701.964.845.995.70You may need to use the appropriate technology to answer this question. The commercial division of a real estate firm is conducting a regression analysis of the relationship between x, annual gross rents (in thousands of dollars), and y, selling price (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained. Analysis of Variance SOURCE DF Adj SS Regression 1 41587.3 Error 7 Total 8 51984.1 Predictor Coef SE Coef T-Value Constant 20.000 3.2213 6.21 X 7.210 1.3626 5.29 Regression Equation Y = 20.0 + 7.21 X (a) How many apartment buildings were in the sample? (b) Write the estimated regression equation. ŷ = (c) What is the value of sb1? (d) Use the F statistic to test the significance of the relationship at a 0.05 level of significance. State the null and alternative hypotheses. H0: ?1 ≠ 0Ha: ?1 = 0H0: ?0 = 0Ha: ?0 ≠ 0 H0: ?0 ≠…Shown below is a portion of a computer output for regression analysis relating Y (dependent variable)X(independent variable). ANOVA df. Ss Regression 1. 24.011 Residual. 8. 67.989 Coefficients. Standard error Intercept. 11.065. 2.043 X. -0.511. 0.304 What is the sample size?
- A manager at a local bank analyzed the relationship between monthly salary and three independent variables: Length of service (measured in months), Gender (0 = female, 1 = male), and Job type (0 = clerical, 1 = technical). The following ANOVA summarizes the regression results.ANOVA Source of Variation df Sum of Squares Mean Square F Regression 3 1,004,346.771 334,782.257 5.96 Residual 26 1,461,134.596 56,197.48445 Total 29 2,465,481.367 Coefficients Standard Error t-Stat p-value Intercept 784.92 322.25 2.44 0.02 Service 9.19 3.20 2.87 0.01 Gender 222.78 89.00 2.50 0.02 Job −28.21 89.61 −0.31 0.76 The level of significance is 0.05. Based on the hypothesis tests for the individual regression coefficients, ________. Multiple Choice all the regression coefficients are not equal to zero "Job" is the only significant variable in the model only months of service and gender are significantly related to monthly salary "Service"…Please help me to interpret the attached chats. This is the ourput results of Regression (Scatter plots and Histograms) for Austria and United KingdomThe output of a different regression analysis on the effect of radio advertising spending on sales is given below. For this study they only explored radio advertising expenditure. Regression Analysis: Sales versus Spend Analysis of Variance Source DF MS F-Value P-Value Regression dfreg 23991.2 MSR F-val p-val Error dferr 561.7 MSE Total 33 24552.9 How many degrees of freedom does the Error (SSE) have? It is denoted by dferr in the table above.
- 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.A multiple regression analysis produced the following tables. Predictor Coefficients StandardErrort Statistic p-valueIntercept 624.5369 78.49712 7.956176 6.88E-06x 8.569122 1.652255 5.186319 0.000301x 4.736515 0.699194 6.774248 3.06E-05Source df SS MS F p-valueRegression2 1660914 830457.1 58.319561.4E06Residual 11 156637.5 14239.77Total 13 1817552The adjusted R is ____________.0.91380.88910.88510.8981▼ Set background Clear frame 6.2 2) The program used to create this scatterplot found the line-of-best-fit and reported the r-squared value as r^2 = 0.805 for the relationship between arm-span and height for several individuals. What is the correlation coefficient? Is it positive or negative? Explain how you know. 1850- 1800- 1750- 1700- 00 1650- 1600- 1550- arm-span (cm) height (cm) 160.0 R Sq Linear-0805 : =S Open c 2 IN
- What is the degree of the correlation between sales and display area in square feet? The sales of 14 store branches and display area(in square feet) were obtained. The objective is to predict sales in terms of area of dispslay. Data were encoded into MS Excel. Output is shown below. REGRESSION STATISTICS Multiple R 0.954 R Square 0.910 Adjusted R square 0.902 Standard Error 936.85 Observations 14 Coefficients Standard t stat Error Intercept 901.25 513.02 1.76 Size 1.69 0.15 11.00 CHOICES A. 0.15, low positive B. 0.910, high positive C. 0.954, high positive D. 0.902, high positiveA 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: Regression Statistics Multiple R 0.8479 R Square 0.7189 Adjusted R Square 0.7069 Standard Error 17.5571 Observations 50 ANOVA df SS MS F Significance F Regression 370443.3236 18521.662 0.0000 Residual 14487.7627 308.2503 Total 49 51531.0863 Coefficients Standard Error t Stat P-value Intercept -5.5146 7.2273 -0.763 0.4493 Income 0.4262 0.0392 10.8668 0.0000 Size 5.5437 1.6949 3.2708 0.00020 a. What is the population model of this regression problem?b. What is the sample estimates of the regression problem?c. Which of the independent variables in the model are significant at the 5% level?…We have a data set which contains the speed of cars and the distances taken to stop. Note that the data were recorded in the 1920s. We know the following summary statistics of the data: n=50 ∑xi=770, ∑x2i=13228 ∑ni=1yi=2149, ∑y2i=124903 ∑xiyi=38482 1(a). Fill in the blanks in the ANOVA table Source df MS Regression 21159.59 Residual Total 49 1(b). Base on the ANOVA table you obtained above, test the hypothesis that β=0. What is the value of statistics? 1(c). Calculate the value of R2 (coefficient of determination).