How to write a multiple regression equations for this data SUMMARY OUTPUT Regres

Question

How to write a multiple regression equations for this data

SUMMARY OUTPUT Regression Statistics Multiple R 0.943951039 R Square 0.891043565 Adjusted R Square 0.834196729 Standard Error 2256.508434 Observations 36 ANOVA df SS MS F Significance F Regression 12 957740403.6 79811700.3 15.67446192 2.61E-08 Residual 23 117112097.2 5091830.312 Total 35 1074852501 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 34309.83333 1595.592415 21.50288069 9.909E-17 31009.09894 37610.56773 31009.09894 37610.56773 Year 4540.25 460.6078552 9.857083305 1.00233E-09 3587.410055 5493.089945 3587.410055 5493.089945 Feb 3406.333333 1842.431421 1.848825033 0.077379276 -405.0264474 7217.693114 -405.0264474 7217.693114 Mar 10802.66667 1842.431421 5.863266629 5.63526E-06 6991.306886 14614.02645 6991.306886 14614.02645 Apr 7134 1842.431421 3.872057282 0.000772546 3322.640219 10945.35978 3322.640219 10945.35978 May 10693 1842.431421 5.803743834 6.50584E-06 6881.640219 14504.35978 6881.640219 14504.35978 Jun 11866 1842.431421 6.440402538 1.42739E-06 8054.640219 15677.35978 8054.640219 15677.35978 Jul 8610.666667 1842.431421 4.673534422 0.000105019 4799.306886 12422.02645 4799.306886 12422.02645 Aug 10155 1842.431421 5.511738393 1.32281E-05 6343.640219 13966.35978 6343.640219 13966.35978 Sep 7092.666667 1842.431421 3.849623159 0.000816643 3281.306886 10904.02645 3281.306886 10904.02645 Oct 6689.666667 1842.431421 3.630890458 0.001399916 2878.306886 10501.02645 2878.306886 10501.02645 Nov 3098.666667 1842.431421 1.681835552 0.106130689 -712.693114 6910.026447 -712.693114 6910.026447 Dec 3416.333333 1842.431421 1.854252644 0.076570637 -395.0264474 7227.693114 -395.0264474 7227.693114 RESIDUAL OUTPUT Observation Predicted Units Residuals Standard Residuals 1 38850.08333 959.9166667 0.524767192 2 42256.41667 -2175.416667 -1.189256667 3 49652.75 -2212.75 -1.209666051 4 45984.08333 1312.916667 0.717745213 5 49543.08333 -332.0833333 -0.181543299 6 50716.08333 762.9166667 0.417071242 7 47460.75 -994.75 -0.543809877 8 49005.08333 -3797.083333 -2.075789313 9 45942.75 -1142.75 -0.624718509 10 45539.75 1449.25 0.792275912 11 41948.75 212.25 0.116032819 12 42266.41667 1919.583333 1.049397714 13 43390.33333 -1163.333333 -0.635971004 14 46796.66667 -1374.666667 -0.7515027 15 54193 -118 -0.064508234 16 50524.33333 401.6666667 0.219583112 17 54083.33333 -511.3333333 -0.279535679 18 55256.33333 -336.3333333 -0.183866689 19 52001 2448 1.338272509 20 53545.33333 2533.666667 1.385104757 21 50483 1694 0.926075829 22 50080 7 0.00382676 23 46489 2024 1.106480211 24 46806.66667 2471.333333 1.351028374 25 47930.58333 203.4166667 0.111203812 26 51336.91667 3550.083333 1.940759366 27 58733.25 2330.75 1.274174285 28 55064.58333 -1714.583333 -0.937328325 29 58623.58333 843.4166667 0.461078978 30 59796.58333 -426.5833333 -0.233204554 31 56541.25 -1453.25 -0.794462632 32 58085.58333 1263.416667 0.690684556 33 55023.25 -551.25 -0.30135732 34 54620.25 -1456.25 -0.796102672 35 51029.25 -2236.25 -1.22251303 36 51346.91667 -4390.916667 -2.400426088

Explanation / Answer

To write the regression equation we need to only get the co-efficien information and intercept.

as , regression equation is like,

Y=intercept+(co-efficient1*independent variable1)+(co-efficient2*independent variable2)+..........

So, from this information we need to take only intercept and co-efficient value.

Independent data value we can get the from the raw data.

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