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Regression Statistics: Multiple R 0.79 R Square 0.63 Adjusted R Square 0.60 Standard Error 233.08

The document appears to show regression statistics and analysis for two separate multiple linear regression models. The first model predicts credit card debt from years of experience and income. The second model predicts number of item sales from number of salespeople, commercial space size, and number of items sold. Both models show high R-squared values indicating the regressions fit the data well in predicting the target variables from the features.

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Matei Pavelescu
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80 views4 pages

Regression Statistics: Multiple R 0.79 R Square 0.63 Adjusted R Square 0.60 Standard Error 233.08

The document appears to show regression statistics and analysis for two separate multiple linear regression models. The first model predicts credit card debt from years of experience and income. The second model predicts number of item sales from number of salespeople, commercial space size, and number of items sold. Both models show high R-squared values indicating the regressions fit the data well in predicting the target variables from the features.

Uploaded by

Matei Pavelescu
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as XLSX, PDF, TXT or read online on Scribd
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Vechime (ani) X1 Venit (lei) X2 Datorie Credit-card (lei) Y

17 29920 1931.20
10 5270 231.20
15 9350 146.20
15 20400 452.20
2 4760 304.30
5 4250 66.30
20 11390 651.10
12 6460 22.10
3 3230 231.20
0 4250 472.60
0 2720 30.60
4 3910 42.50
24 10880 668.10
6 4930 292.40
22 17000 629.00
9 8330 139.40
13 6970 496.40
23 12240 200.60
6 10370 95.20
0 4420 17.00
22 8840 195.50
17 7310 100.30
3 4420 73.10
8 4590 68.00
1 2720 40.80
0 5440 363.80
9 11730 120.70
25 10880 161.50
12 9860 523.60
2 6290 34.00

SUMMARY OUTPUT

Regression Statistics
Multiple R 0.79
R Square 0.63
Adjusted R Square 0.60
Standard Error 233.08
Observations 30

ANOVA
df SS MS F
Regression 2 2512302.23959341 1256151 23.12
Residual 27 1466803.99007325 54326.074
Total 29 3979106.22966666

Coefficients Standard Error t Stat P-value


Intercept -103.55 78.21 -1.32 0.19659
Vechime (ani) X1 -8.03 6.69 -1.20 0.2407
Venit (lei) X2 0.06 0.01 6.02 2E-06
Significance F
1.4090241E-06

Lower 95% Upper 95% Lower 99.0% Upper 99.0%


-264.02 56.92 -320.24 113.14
-21.75 5.70 -26.56 10.51
0.04 0.08 0.03 0.08
Număr Suprafaţa
Vânzări
vânzători comercial
(bucăţi)
(persoane) ă (mp)
7 98 22
5 90 20
8 110 23
9 130 26
12 140 30
15 145 32
22 156 45
25 160 50
32 164 52
40 175 60

SUMMARY OUTPUT

Regression Statistics
Multiple R 0.98943
R Square 0.978973
Adjusted R 0.972965
Standard E 2.377678
Observatio 10

ANOVA
df SS MS F Significance F
Regression 2 1842.427 921.2133 162.9499 1.348E-06
Residual 7 39.57347 5.653352
Total 9 1882

Coefficients
Standard Error t Stat P-value Lower 95%Upper 95%Lower 99.0%
Upper 99.0%
Intercept 4.702903 6.186763 0.760156 0.471983 -9.926466 19.33227 -16.94757 26.35338
Număr vânz0.974544 0.151387 6.437448 0.000354 0.616571 1.332516 0.444769 1.504319
Suprafaţa 0.104112 0.061428 1.694874 0.133923 -0.041141 0.249366 -0.110853 0.319078

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