Education
Unemployment Rate Employee
GDP Spend
(% of Labor Force) Compensation
256,376 14,185 7.00 128,564
264,335 15,004 6.60 135,710
273,256 15,821 7.50 141,985
281,200 16,566 8.20 144,669
296,820 16,709 8.40 148,851
310,038 17,646 8.50 153,985
325,152 18,295 8.30 161,393
343,619 18,962 7.50 170,106
351,743 20,133 7.00 179,628
346,473 21,071 7.90 180,906
363,140 21,936 8.30 184,711
375,968 23,356 7.20 193,171
386,175 24,158 7.60 199,806
392,880 25,045 8.40 203,606
403,003 25,436 8.50 206,201
416,701 26,282 8.50 208,128
430,085 26,675 7.80 211,813
444,991 27,853 7.10 219,187
460,419 28,618 6.00 226,300
Educati on Spend
35,000
30,000
25,000
20,000
15,000
10,000
5,000
-
200,000 250,000 300,000 350,000 400,000 450,000 500,000
the graph depicts education spend and gdp has strong and positive correlation
Unemployment Rate
(% of Labor Force)
9.00
8.00
7.00
6.00
5.00
4.00
3.00
2.00
1.00
-
200,000 250,000 300,000 350,000 400,000 450,000 500,000
The above graph depicts the relation between unemployement rate and GDP is
Employee Compensati on
250,000
200,000
150,000
100,000
50,000
-
200,000 250,000 300,000 350,000 400,000 450,000 500,000
d positive correlation the graph depicts employee compensation and gdp has strong and positive corr
ment rate and GDP is negative and weak
s strong and positive correlation
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.993733244881333
R Square 0.987505761982384
Adjusted R Square 0.985006914378861
Standard Error 7659.40145523017
Observations 19
ANOVA
df SS
Regression 3 69552186642.9385
Residual 15 879996459.78573
Total 18 70432183102.7242
Coefficients Standard Error
Intercept 29500.428768059 31570.1521977402
Education Spend 3.98338185999361 3.6518808588426
Unemployment Rate
(% of Labor Force) -2189.4311558659 2466.11600534355
Employee Compensation 1.43403820397665 0.556454441129743
RESIDUAL OUTPUT
Predicted
Observation GDP Residuals
1 255042.203049035 1334.19695096457
2 269428.798936706 -5093.89893670566
3 279710.813737263 -6454.91373726341
4 284997.689851173 -3797.48985117307
5 291125.523384272 5694.17661572824
6 302001.966699272 8035.63330072793
7 315647.737626813 9503.76237318665
8 332549.316024076 11069.5839759241
9 351964.742299435 -221.642299435451
10 355561.455234852 -9088.65523485228
11 363591.583768301 -451.483768301143
12 383786.443326202 -7818.64332620218
13 395621.525017334 -9446.8250173342
14 402850.306478996 -9970.3064789963
15 407912.361778012 -4909.0617780122
16 414045.264239169 2656.13576083141
17 422425.806083251 7659.49391674862
18 439226.911261753 5764.1887382475
19 454883.651204083 5535.74879591714
MS F Significance F
23184062214.3128 395.1845 1.712764E-14
58666430.652382
t Stat P-value Lower 95% Upper 95%Lower 95.0%
Upper 95.0%
0.934440498838352 0.364877 -37789.75779 96790.62 -37789.76 96790.62
1.09077541518046 0.292584 -3.800417937 11.76718 -3.800418 11.76718
-0.887805419989108 0.38866 -7445.832995 3066.971 -7445.833 3066.971
2.57709903629341 0.021036 0.2479836383 2.620093 0.247984 2.620093
Standard Residuals
0.190816415739355
-0.728527775856458
-0.923179671757499
-0.543115768390528
0.814379295061787
1.14925367168031
1.35922501605101
1.58316831445653
-0.0316992098684721
-1.29985652759245
-0.0645710622928932
-1.11821983581339
-1.35108185386076
-1.42595000293586
-0.702092425294582
0.379879676109703
1.09545833882675
0.824392406145402
0.791720999592068
REGRESSION EQUATION
GDP = 29500.42 + 3.983 X1 + (-2189.43)X2 + 1.434 X2
Since The R square value is greater than 0.7, this model is good fit.This model has R sqaure value
5.a 0.987. The coefficient of determination(R square) > 0.7 indicates the model is a good fit.
5.b GDP = 29500.42 + 3.983 X1 + (-2189.43)X2 + 1.434 X2
GDP= 29500.42 + 3.983 *38618 + (-2189.43)*6 + 1.434 *326300
638093.534
PREDICTED GDP FOR 2024 USING GIVEN DATA IS 6,38,093.534
