hello I just want to know if this is correct. I am using regression and am supposed to predict a 30 year old woman but I

[answerhappygod]

hello I just want to know if this is correct. I am using
regression and am supposed to predict a 30 year old woman but I do
the regression and I do not know what to do next. It is very
important to get a good feedback please.
in the table, 1 is for women and 0 for men.

Hello I Just Want To Know If This Is Correct I Am Using Regression And Am Supposed To Predict A 30 Year Old Woman But I 1 (220.13 KiB) Viewed 87 times

Hello I Just Want To Know If This Is Correct I Am Using Regression And Am Supposed To Predict A 30 Year Old Woman But I 2 (57.41 KiB) Viewed 87 times

Someone you know is a 30-year old internet user, with a college education and working for the State Government. As a single woman living alone, how much do you think she is making? SUMMARY OUTPUT Regression Statistics Multiple R 0.99755 R R Square 0.99511 Adjusted F 0.99508 Standard E 2318.29 Observatic 1000 ANOVA Regression Residual df SS MS F gnificance F 6 1.1E+12 1.8E+11 33708.6 0 993 5.3E+09 5374465 + 999 1.1E+12 Total 85,000 Characteristics of Randomly selected Internet Users No. in Marital User Gender Age Education Annual Status Household Employment Income 1 1 36 0 1 2 1 $ 29,000 2 0 0 52 1 0 2 1 $ 93,000 3 0 0 40 0 0 1 0 $ 12,500 4 0 25 0 1 2 1 $ 12.500 5 0 39 1 1 2 1 1 S 63,500 6 0 29 1 1 3 0 0 $ S 21,000 7 1 24 1 0 1 1 $ 48,500 8 1 54 1 1 1 1 $ 83.500 9 0 741 1 1 3 1 1 $ 118,500 10 1 34 1 0 3 3 1 $ 68,500 11 1 16 1 1 1 1 1 $ 26,500 12 1 17 0 1 3 1 $ 3,000 13 0 51 1 0 3 1 $ 94,000 14 0 17 1 0 1 1 0 $ 8,000 15 0 23 1 1 1 1 $ 37,000 16 0 20 1 0 4 1 $ 50,000 17 1 36 1 1 0 3 0 0 $ 41,500 181 0 45 1 0 4 1 $ 87,500 19 0 47 0 1 4 1 $ 50,500 20 0 68 1 0 1 1 $ 114,500 . 21 0 47 1 0 2 1 $ 85,500 22 0 28 0 0 4 1 S 32,000 23 1 37 0 200 1 1 1 $ 28,000 240 67 0 0 2 1 $ 85,500 25 1 1 63 1 1 1 1 3 1 S $ 102,000 26 0 62 1 1 1 1 0 0 IS 65,500 27 0 35 1 1 3 3 0 IS 30,000 28 1 65 1 0 3. 0 IS $ 29 0 0 44 0 1 1 3 1 1 S 43,500 30 0 48 1 0 1 o 0 IS $ 54,500 31 1 54 0 0 4 1 1 $ 71,000 32 0 1 1 1 2 1 1 $ 53,000 33 0 0 53 0 0 3 1 S 67,000 34 1 39 0 1 2 1 $ 33,500 35 0 37 1 1 4 1 $ 65,500 36 0 16 1 1 4 1 $ 34,000 37 1 46 0 0 3 1 $ 56,500 38 1 37 1 0 4 1 $ 75,500 39 0 0 67 0 0 4 4 1 $ 90 500 0 40 1 1 2 1 $ 65,000 41 1 59 0 0 1 1 0 0 $ 41,000 42 1 15 1 1 3 1 S 30,000 43 1 48 1 0 1 1 $ 84,500 44 1 40 1 1 1 0 $ 32,500 45 0 29 1 0 1 1 $ 56,000 46 1 34 1 0 4 1 1 $ 71,000 47 1 30 1 1 4 4 1 $ 55,000 48 0 43 1 1 4 1 $ 74,500 49 0 60 0 0 1 1 $ 72 500 50 1 54 0 0 3 0 $ 38,500 51 O 19 1 1 2. 1 1 $ 33,500 52 1 56 1 0 2 0 $ 69,000 53 0 60 1 1 1 0 $ 62.500 54 0 38 1 0 1 1 $ 69,500 55 1 43 1 1 4 4 1 $ S 74,500 56 1 48 1 1 2 2 1 1 $ 77,000 57 0 22 0 1 1 2 0 0 $ 5811 57 1 1 0 1 $ 105,500 59 0 35 0 0 1 2 1 1 $ $ 27,500 60 0 75 1 1 1 2 1 $ 117,500 Coefficientsandard Em t Stat P-value Lower 95%Upper 95%wer 95.0 Spper 95.0% Intercept -46458 332.183 -139.86 0 O 47110 -45806 -47110 45806 Gender 464.65 150.311 3.0913 0.00205 -759.6 -169.69 -759.62 -169.69 Age 1476.77 4.20795 350.948 0 1468.52 1485.03 1468.52 1485.03 Education 28966.7 160.591 180.376 0 28651.6 29281.8 28651.6 29281.8 Marital Sta -9826.8 147.491 -66.626 0 -10116 -9537.3 -10116 -9537.3 No. in Hoi 2327.11 65.2386 35.6708 5E-180 2199.09 2455.13 2199.09 2455.13 Employme 28744.7 168.357 170.736 0 28414.3 29075.1 28414.3 29075.1 32 40
SUMMARY OUTPUT Regression Statistics Multiple R 0.99753 R Square 0.99507 Adjusted R Square 0.99504 Standard Error 2328.25 Observations 1000 ANOVA Regression Residual Total df 5 994 SS MS F gnificance F 1.1E+12 2.2E+11 40103.238 0 5.4E+09 5420727 1.1E+12 999 Coefficientsandard Em t Stat Intercept -46732 321.495 -145.36 Age 1477.13 4.22447 349.66 Education 28998.8 160.944 180.18 Marital Status -9822.2 148.117 -66.313 No. in Household 2337.96 65.4239 35.7356 Employment 28771.2 168.861 170.384 P-value Lower 95%Upper 95% wer 99.0%pper 99.0% 0 -47363 -46101 -47562 -45902 0 1468.84 1485.42 1466.22 1488.03 0 28683 29314.6 28583.4 29414.1 0 -10113 -9531.5 -10204 -9439.9 1.53E-180 2209.58 2466.35 2169.12 2506.81 028439.8 29102.6 28335.4 29207 Based on the all the P values obtained, it is concluded that we can reject the null hypothesis. Based on our results, it is concluded that a positive relationship exist between most variables except "Marital Status" which in some way restate the non marital status of the subject.
SUMMARY OUTPUT Regression Statistics Multiple R 0.98653 R Square 0.97324 Marital Status consisted of 3% for our R Adjusted R Square 0.97314 square value. Standard Error 5419.65 Observations 1000 ANOVA Regression Residual Total df 4 995 999 SS 1.1E+12 2.9E+10 1.1E+12 MS F gnificance F 2.7E+11 9048.4625 0 2.9E+07 Intercept 30 Age 1 Education 1 No. in Household Employment Coefficientsandard Em t Stat -53432 710.45 -75.209 1502.99 9.79165 153.497 28963 374.64 77.309 2577.88 152.059 16.9531 28864 393.059 73.4344 P-value Lower 95% Upper 95% wer 99.0 pper 99.0% 0 -54827 -52038 -55266 -51599 0 1483.77 1522.2 1477.72 1528.26 0 28227.9 29698.2 27996.2 29929.9 7.945E-57 2279.49 2876.28 2185.45 2970.31 0 28092.7 29635.4 27849.6 29878.4 c Predicted wage. $ 23,198.11 Based on calculation performed through regression, it is concluded that at 30 years old the predicted income of a woman working for the government and single is $23,198.11
fx =AB80*AD80+AB81*AD81+AB82*AD82+AD79
1 0 0 1 20000 37000 31000 101500 50500 Regression Residual Total df 4 995 999 SS 1.1E+12 2.9E+10 1.1E+12 MS F gnificance F 2.7E+11 9048.4625 0 2.9E+07 1 0 4500 30 Age 0 1 1 1 51500 9400C 64000 85000 fefficientandard Ern t Stat ntercept -534321 710.45 -75.209 1502.991 9.79165 153.497 Fiducation 289620 374.64 77.309 No. in Household. 2577.891 152.059 16.9531 Employment 28864 393.059 73.4344 P-value Lower 95% UE 0 -54827 0 1483.77 0 28227.9 7.945E-57 2279.49 0 28092.7 1 44500 0 1 1 1 1 1 1 1 1 0 10500 68500 71000 80000 41000 87500 40500 55000 89000 69000 56000 75000 104500 "hrough =AB80*AD80+AB81 *AD81+AB82*AD8 2+AD79 30 years old Le preuille II ILUMIE UI d Wullid working for the government and single is $23,198.11. 1 0 1