Question

#3-ANOVA with solutions Regression Analysis R2 0.642 20 R 0.801 Std. Error 3.219 ANOVA table MS 157.9645 10.3605 df p-value Source Regression Residual Total 315.9291 176.1284 492.0575 15.25 0002 17 19 Regression output Variables Intercept Months Gender Coefficients 15.7625 0.4415 3.8598 std. error 3.0782 0.0839 1.4724 5.121 5.263 2.621 p-value 0001 0001 0179

How do we come out with the std.error.

0 0
Add a comment Improve this question Transcribed image text
Answer #1

How, SSE= Σ(X-Y)- 176-1284 N-20 NN-2

Add a comment
Know the answer?
Add Answer to:
How do we come out with the std.error. #3-ANOVA with solutions Regression Analysis R2 0.642 20...
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for? Ask your own homework help question. Our experts will answer your question WITHIN MINUTES for Free.
Similar Homework Help Questions
  • J. Thie uala set is 1or b4 banks. R2 Std. Error 6.977 0.519 64 ANOVA table Source df MS F p-value 1 3,260.0981 66.97 1.90E-11 62 3,260.0981 3,018.3339 Regression Residual 48.6828 Total 6,278.4320...

    J. Thie uala set is 1or b4 banks. R2 Std. Error 6.977 0.519 64 ANOVA table Source df MS F p-value 1 3,260.0981 66.97 1.90E-11 62 3,260.0981 3,018.3339 Regression Residual 48.6828 Total 6,278.4320 63 Regression output Confidence Interval Lower 95% Upper 95% variables Coefficients Std. Error tStt p-value Intercept 65763 1.9254 3.416 0011 2.727510.4252 X1 00452 0.0055 8.183 1.90E-11 0.0342 0.0563 Calculate the R2 a. b. In words what does the R? say about total revenue for a bank? c....

  • 1st regression analysis 2nd regression analysis 1. Analyze the two regression analysis's above ...

    1st regression analysis 2nd regression analysis 1. Analyze the two regression analysis's above and make a recommendation on if the organization should increase, decrease, or retain their pricing and why? 2. What happens to the dependent variable Y if the price X1 decreases in the second regression analysis? SUMMARY OUTPUT Y=UNITS SOLD X=PRICE Regression Statistics Multiple R R Square Adiusted R S Standard Error Observations 0.874493978 0.764739718 0.756026374 159.2178137 29 quare ANOVA df MS Significance F 1 2224908.261 2224908.26187.76650338 5.64792E-10...

  • 1.Based on the table above, how to intepret this regression analysis? 2. When we need to...

    1.Based on the table above, how to intepret this regression analysis? 2. When we need to look at the adjusted r2 and why? 3. How to conduct the hypothesis test? 0 Regression Statistics 1 Multiple R 2 R Square 3 Adjusted RS 0.853658537 0,97530483 0.951219512 4 Standard Err 0.191273014 5 Observation 6 7 ANOVA Significance F 0.220863052 df SS MS 0.713414634 0.356707 9 Regression 0 Residual 1 Total 2. 9.75 1 0.036585366 0.036585 0.75 2 Lower 95 % 3 Coefficients...

  • ANOVA DF SS MS Regression 1 0.0994 0.0985 Residual 62 0.1413 0.0025 Total 61 0.2407 Coefficients...

    ANOVA DF SS MS Regression 1 0.0994 0.0985 Residual 62 0.1413 0.0025 Total 61 0.2407 Coefficients Standard Error Intercept -0.013 0.0053 S&P 500 Returns 1,2139 0.1878 Looking both at the specification of the model and at the estimated coefficient, how can you interpret the coefficient of S&P 500 Returns

  • 7,10,11 Based on the following regression output, what is the equation of the regression line? Regression...

    7,10,11 Based on the following regression output, what is the equation of the regression line? Regression Statistics Multiple R 0.917214 R Square 0.841282 Adjusted R Square 0.821442 Standard Error 9.385572 Observations 10 ANOVA df SS MS Significance F 1 Regression 3735.3060 3735.30600 42.40379 0.000186 8 Residual 704.7117 88.08896 9 Total 4440.0170 Coefficients Standard Error t Stat P-value Lower 95% Intercept 31.623780 10.442970 3.028236 0.016353 7.542233 X Variable 1.131661 0.173786 6.511819 0.000186 0.730910 o a. 9; = 7.542233+0.7309 Xli o b....

  • Consider the simple linear regression model: HARD1 = β0 + β1*SCORE + є, where є ~...

    Consider the simple linear regression model: HARD1 = β0 + β1*SCORE + є, where є ~ N(0, σ). Note: HARD1 is the Rockwell hardness of 1% copper alloys and SCORE is the abrasion loss score. Assume all regression model assumptions hold. The following incomplete output was obtained from Excel. Consider also that the mean of x is 81.467 and SXX is 81.733. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.450969 Standard Error Observations 15 ANOVA df...

  • Based on the below data what will be the value of multiple R? Regression Statistics Multiple...

    Based on the below data what will be the value of multiple R? Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 8 ANOVA df SS MS F Regression 1 29 29 7 Residual 6 26 4 Total 7 Coefficients Standard Error t Stat P-value Intercept 1 31.274666 3.984284 0.007248 Advertising (thousands of S) 42 6.19330674 1.610802 0.158349 Submit Answer format: Number Round to: 2 decimal places.

  • Following a regression analysis output : SUMMARY OUTPUT Regression Statistics Multiple R 0.719422 R Square Adjusted...

    Following a regression analysis output : SUMMARY OUTPUT Regression Statistics Multiple R 0.719422 R Square Adjusted R Square 0.477366 Standard Error Observations 14 ANOVA df SS MS F Regression 1 3.028885709 Residual 12 2.823257148 Total 13 5.852142857 Coefficients Standard Error t Stat P-value Intercept 1.157091 0.566482479 0.063699302 Satisfaction with Speed of Execution 0.636798 0.177478218 0.003726861 Group of answer choices R Square is 0.517 Standard error is 0.386 Residuals are 2.823 F-test is 11.87 R Square is 0.517 Standard error is...

  • SUMMARY OUTPUT Regression Statistics Multiple R     0.9448 R2     0.8927 Adj. R2     0.8853 SY.X...

    SUMMARY OUTPUT Regression Statistics Multiple R     0.9448 R2     0.8927 Adj. R2     0.8853 SY.X 133.14 N 32 ANOVA df SS MS F P-value Regression 2 4277160 2138580 120.6511       0.0000 Residual 29 514034.5 17725.33 Total 31 4791194 Coeff. Std. Err. t Stat P-value Lower 95% Upper 95% Intercept -1336.72 173.3561 -7.71084     0.0000 -1691.2753 -982.16877 X1 12.7362 0.90238 14.114     0.0000 10.890623 14.5817752 X2 85.81513 8.705757 9.857286     0.0000 68.009851 103.620414 With respect to the null hypothesis for...

  • You were asked by your manager to evaluate the regression tables below to decide which cost driver would be best to use...

    You were asked by your manager to evaluate the regression tables below to decide which cost driver would be best to use for the production department. Since your manager is new and does not understand the regression analysis tables, you will need to explain why one set of statistics is better than the other and why you have chosen the better driver.   Manufacturing Direct Labor Hours Regression Statistics Multiple R 0.799304258 R Square 0.638887297 Adjusted R Square 0.602776026 Standard Error...

ADVERTISEMENT
Free Homework Help App
Download From Google Play
Scan Your Homework
to Get Instant Free Answers
Need Online Homework Help?
Ask a Question
Get Answers For Free
Most questions answered within 3 hours.
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT