{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Module 6 test2

Module 6 test2 - Regression Analysis Step 1 2 ANOVA table...

This preview shows pages 1–18. Sign up to view the full content.

Regression Analysis Step 1 2 ANOVA table Source SS df MS F p-value Regression 48.0000 1 48.0000 Residual 54.0000 9 6.0000 Total 102.0000 10 3 4 5 6 7 8 9

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
SS Total = SS Regression + SS Residual SS Total = 48.0000 54.0000 SS Total = 102.0000 SS Regression = SS Total - SS Residual SS Regression = 102.0000 54.0000 SS Regression = 48.0000 SS Regression / SS Total 48.0000 102.0000 0.471 Delta Check 0.0000 n = 11 Formula for Standard Error S = Square Root of SS Residual / n - 2 S= SS Residual n - 2 S= 54.0000 9 S= 2.449 df Regression = df Total - df Residual 10 9 F-Test Formula F = MS Regression / MS Residual F = (SS Regression / df Regression) / SS Residual / df Residual 48.0000 1 54.0000 9 F = MS Regression / MS Residual 48.000 6.0000 F = 8.00 Find the value of r r = 0.686 Is r Positive or Negative ( + / - )? It is positive (+) because the slope of the line goes upward and right on the plot. r 2 = r 2 = r 2 = r = square root of r 2

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Regression Analysis r² 0.366 n 24 r 0.605 k 1 Std. Error 109.162 Dep. Var. Sales ANOVA table Source SS df MS F p-value Regression 151,642.2653 1 151,642.2653 12.73 .0017 Residual 262,159.4365 22 11,916.3380 Total 413,801.7018 23 Regression output confidence interval variables coefficients std. error t (df=22) p-value 95% lower 95% upper Intercept 203.8348 43.5917 4.676 .0001 113.4312 294.2384 Advertising 30.0232 8.4162 3.567 .0017 12.5690 47.4774 Predicted values for: Sales 95% Confidence Intervals 95% Prediction Intervals Advertising Predicted lower upper lower upper Leverage 2 263.881 200.900 326.862 28.896 498.867 0.077 7 413.997 349.857 478.136 178.698 649.296 0.080 Regression Equation: 203.8348 tells us how much the natural gas is estimeated to be consumed when 30.0232 For each additional Advertising 30.0232 Expect an x 0 should be substituted with the independent variable. 233.858 x Find x To find the x-intercept assumes x = 0 The Simple Coefficient of Determination Explained Variation / Total Variation the simple linear regression model, which includes the independent variable explains  36.6% of the variability in  Sales r = r = the simple coefficient of r = 0.605  relationship between  0 AND 0 Y^ = b 0 + b 1 x b 0 b 1 Y^ = b 0 + b 1 r 2  =  r 2  =  r 2  =  r 2  =  Our interpretation is that there is

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
for each additional independent variable we can expect an increase/decrease of the dependent vari Dependent Sales Independen Advertising IS 30.0232 < OR > THAN 1? Increase, Increase or Decrease? n the temperature = 0 Increase, of the Sales 203.8348 Advertising expenditures 0 s a fairly strong  positive  or  negative  linear

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
iable.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Regression Analysis r² 0.366 n 24 r 0.605 k 1 Std. Error 109.162 Dep. Var. Sales ANOVA table Source SS df MS F p-value Regression 151,642.2653 1 151,642.2653 12.73 .0017 Residual 262,159.4365 22 11,916.3380 Total 413,801.7018 23 Regression output confidence variables coefficients std. error t (df=22) p-value 95% lower Intercept 203.8348 43.5917 4.676 1.16E-04 113.4312 Advertising 30.0232 8.4162 3.567 .0017 12.5690 Predicted values for: Sales 95% Confidence Intervals 95% Prediction Intervals Advertising Predicted lower upper lower upper 2 263.881 200.900 326.862 28.896 498.867 7 413.997 349.857 478.136 178.698 649.296 Regression Equation: 203.8348 tells us how much the natural gas is estimeated to be consumed w 30.0232 For each additional Advertising 30.0232 x 0 should be substituted with the independent variabl 233.858 x Find x To find the x-intercept assumes x = 0 Y^ = b 0 + b 1 x b 0 b 1 Y^ = b 0 + b 1

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Dependent Sales

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern