In: Statistics and Probability
exper | score | salary |
4 | 78 | 24 |
7 | 100 | 43 |
1 | 86 | 23.7 |
5 | 82 | 34.3 |
8 | 86 | 35.8 |
10 | 84 | 38 |
0 | 75 | 22.2 |
1 | 80 | 23.1 |
6 | 83 | 30 |
6 | 91 | 33 |
9 | 88 | 38 |
2 | 73 | 26.6 |
10 | 75 | 36.2 |
1-R2=
2-F test statistic=
3.b2=
4-P-value for F test=
Output using excel:
Data > Data analysis > Regression
SUMMARY OUTPUT | ||||||||
Regression Statistics | ||||||||
Multiple R | 0.935318 | |||||||
R Square | 0.874819 | |||||||
Adjusted R Square | 0.849783 | |||||||
Standard Error | 2.665625 | |||||||
Observations | 13 | |||||||
ANOVA | ||||||||
df | SS | MS | F | Significance F | ||||
Regression | 2 | 496.5675 | 248.2837 | 34.94219 | 3.07E-05 | |||
Residual | 10 | 71.05558 | 7.105558 | |||||
Total | 12 | 567.6231 | ||||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
Intercept | -4.15742 | 8.895252 | -0.46737 | 0.65025 | -23.9773 | 15.66244 | -23.9773 | 15.66244 |
exper | 1.46519 | 0.235336 | 6.22596 | 9.81E-05 | 0.94083 | 1.989551 | 0.94083 | 1.989551 |
score | 0.33381 | 0.111003 | 3.007215 | 0.01318 | 0.08648 | 0.58114 | 0.08648 | 0.58114 |
1. R2 = R Square = 0.8748
2. F test statistic = 34.9422
3. b2 = slope for score = 0.3338
4. P-value for F test = 0.000031
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