3. Refer to your bivariate regression output table, how does income level predict the number of volunteer hours? Is this prediction significant? Explain briefly.

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H E7 PARTNERSTROONE AutoSave Off File Home Insert Draw Page Layout Formulas Data 2. X Undo A 1 SUMMARY OUTPUT 2 10 ANOVA Paste 19 Clipboard 4 Multiple R 5 R Square 6 Adjusted R Squar 7 Standard Error 8 Observations 17 Intercept 18 income > X 12 Regression 13 Residual 14 Total 61F Sunny Mary_Volunteer_profile (1) Calibri BIL U fx Regression Statistics B Ready Accessibility: Investigate 0.794534637 0.63128529 0.62492814 2.845801382 df Coefficients Assignment Problem Font 60 1 58 59 1.046536707 0.894732476 11 A^ A V Y A~A~ SS 804.215374 469.7179594 1273.933333 5 bivariate regression Review View I == = = = B D Alignment MS 804.215374 8.098585506 E Q Search Search Questions 2 and 3 Automate ab F 99.30318984 Help General $ % 95.000 F Standard Error t Stat P-value Lower 95% Upper 95% 1.004848223 1.041487344 0.301971387 -0.96488555 3.057958964 0.089786616 9.965098587 3.5329E-14 0.715005038 1.074459914 + Number Significance F 3.5329E-14 G Conditional Format as Cell Formatting Table Styles Styles H I Lower 95.0% Upper 95.0% -0.96488555 3.057958964 0.715005038 1.074459914 Insert Delete Format Cells J Residuals hours_week monica hourani MH 10 5 0 -5 -10 30 20 EM 10 0 K 0 V AY Sort & Find & Filter Select Editing L Comments B 10 M income O Analyze Data Analysis M income Residual Plot income income Line Fit Plot 20 N 15 4) O Share 30 20 hou Pre hou 2:33 PM 4/10/2023 + 102% & H E7 PARTNERSTROONE AutoSave Off File Home Insert Draw Page Layout Formulas Data 2. X Undo A 1 SUMMARY OUTPUT 2 10 ANOVA Paste 19 Clipboard 4 Multiple R 5 R Square 6 Adjusted R Squar 7 Standard Error 8 Observations 17 Intercept 18 income > X 12 Regression 13 Residual 14 Total 61F Sunny Mary_Volunteer_profile (1) Calibri BIL U fx Regression Statistics B Ready Accessibility: Investigate 0.794534637 0.63128529 0.62492814 2.845801382 df Coefficients Assignment Problem Font 60 1 58 59 1.046536707 0.894732476 11 A^ A V Y A~A~ SS 804.215374 469.7179594 1273.933333 5 bivariate regression Review View I == = = = B D Alignment MS 804.215374 8.098585506 E Q Search Search Questions 2 and 3 Automate ab F 99.30318984 Help General $ % 95.000 F Standard Error t Stat P-value Lower 95% Upper 95% 1.004848223 1.041487344 0.301971387 -0.96488555 3.057958964 0.089786616 9.965098587 3.5329E-14 0.715005038 1.074459914 + Number Significance F 3.5329E-14 G Conditional Format as Cell Formatting Table Styles Styles H I Lower 95.0% Upper 95.0% -0.96488555 3.057958964 0.715005038 1.074459914 Insert Delete Format Cells J Residuals hours_week monica hourani MH 10 5 0 -5 -10 30 20 EM 10 0 K 0 V AY Sort & Find & Filter Select Editing L Comments B 10 M income O Analyze Data Analysis M income Residual Plot income income Line Fit Plot 20 N 15 4) O Share 30 20 hou Pre hou 2:33 PM 4/10/2023 + 102% &