Research and application of constructing football training linear programming based on multiple linear regression equation
Publié en ligne: 13 déc. 2021
Pages: 143 - 154
Reçu: 17 juin 2021
Accepté: 24 sept. 2021
© 2021 YinZhuang Bai et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Fig. 1
Division of football match fields.The correlation between the outcome of the game and the indicators
Index |
Sample size |
Correlation coefficient |
Significance |
Index |
Sample size |
Correlation coefficient |
Significance |
|
X1 |
400 |
0.475 |
0.047 |
X27 |
400 |
−0.683 |
0.002 |
X2 |
400 |
0.976 |
0 |
X28 |
400 |
−0.183 |
0.469 |
X3 |
400 |
0.535 |
0.022 |
X29 |
400 |
0.304 |
0.22 |
X4 |
400 |
0.683 |
0.002 |
X30 |
400 |
0.122 |
0.63 |
X5 |
400 |
0.545 |
0.019 |
X31 |
400 |
0.011 |
0.913 |
X6 |
400 |
0.416 |
0.086 |
X32 |
400 |
0.34 |
0.167 |
X7 |
400 |
0.131 |
0.604 |
X33 |
400 |
0.452 |
0.06 |
X8 |
400 |
0.098 |
0.7 |
X34 |
400 |
0.657 |
0.003 |
X9 |
400 |
0.024 |
0.924 |
X35 |
400 |
0.354 |
0.15 |
X10 |
400 |
0.085 |
0.737 |
X36 |
400 |
−0.219 |
0.383 |
X11 |
400 |
0.438 |
0.069 |
X37 |
400 |
−0.280 |
0.261 |
X12 |
400 |
0.523 |
0.026 |
X38 |
400 |
−0.173 |
0.493 |
X13 |
400 |
0.401 |
0.099 |
X39 |
400 |
0.427 |
0.077 |
X14 |
400 |
0.617 |
0.006 |
X40 |
400 |
−0.527 |
0.025 |
X15 |
400 |
0.377 |
0.123 |
X41 |
400 |
−0.401 |
0.099 |
X16 |
400 |
0.295 |
0.235 |
X42 |
400 |
−0.450 |
0.061 |
X17 |
400 |
0.413 |
0.088 |
X43 |
400 |
0.225 |
0.369 |
X18 |
400 |
0.402 |
0.098 |
X44 |
400 |
0.228 |
0.363 |
X19 |
400 |
0.061 |
0.809 |
X45 |
400 |
−0.085 |
0.737 |
X20 |
400 |
0.511 |
0.03 |
X46 |
400 |
0.182 |
0.469 |
X21 |
400 |
0.547 |
0.019 |
X47 |
400 |
0.267 |
0.284 |
X22 |
400 |
0.206 |
0.411 |
X48 |
400 |
0.383 |
0.117 |
X23 |
400 |
0.802 |
0 |
X49 |
400 |
0.389 |
0.111 |
X24 |
400 |
−0.012 |
0.962 |
X50 |
400 |
0.073 |
0.774 |
X25 |
400 |
−0.976 |
0 |
X51 |
400 |
0.152 |
0.548 |
X26 |
400 |
−0.559 |
0.016 |
X52 |
400 |
−0.152 |
0.548 |
Regression coefficient table
Variable |
Parameter estimate |
Standard error |
Type II SS |
F value |
Significance (Pr > F) |
|
Intercept |
0.07407 |
0.01719 |
0.00142 |
18.57 |
<0.0001 |
X6 |
0.27741 |
0.02728 |
0.00790 |
103.40 |
<0.0001 |
X13 |
0.09219 |
0.01663 |
0.00235 |
30.72 |
<0.0001 |
X2 |
0.13100 |
0.02305 |
0.00247 |
32.30 |
<0.0001 |
X20 |
0.07443 |
0.01342 |
0.00235 |
30.74 |
<.0001 |
X11 |
0.12904 |
0.02254 |
0.00250 |
32.77 |
<0.0001 |
X21 |
0.04669 |
0.01905 |
0.00046 |
6.01 |
0.0184 |
X23 |
0.06296 |
0.02080 |
0.00070 |
9.17 |
0.0042 |
X19 |
0.06853 |
0.02419 |
0.00061 |
8.03 |
0.0070 |
Regression analysis variance of game winning factors and game performance
Model |
Sum of variance |
Mean difference |
F |
Sig. |
|
1 |
Regression |
129.495 |
2.878 |
3.8880.000 |
|
Residual |
37.005 |
0.74 |
|
|
Total |
160.5 |
|
|
Stepwise regression process
Step |
Introduced variables |
Number of variables |
Coefficient of determination R2 |
Model R2 |
C (P) |
F |
Significance(Pr > F) |
|
1 |
X6 |
1 |
0.7855 |
0.7855 |
354.351 |
183.10 |
<0.0001 |
2 |
X13 |
2 |
0.0971 |
0.8826 |
174.172 |
40.54 |
<0.0001 |
3 |
X2 |
3 |
0.0342 |
0.9168 |
111.994 |
19.75 |
<0.0001 |
4 |
X20 |
4 |
0.0269 |
0.9437 |
63.5307 |
22.47 |
<0.0001 |
5 |
X11 |
5 |
0.0174 |
0.9611 |
32.8756 |
20.61 |
<0.0001 |
6 |
X21 |
6 |
0.0072 |
0.9684 |
21.3579 |
10.25 |
0.0025 |
7 |
X23 |
7 |
0.0044 |
0.9728 |
15.0255 |
7.19 |
0.0103 |
8 |
X19 |
8 |
0.0043 |
0.9771 |
9.0000 |
8.03 |
0.0070 |
Regression statistics results
|
DF |
SS |
MS |
F |
Significance (Pr > F) |
|
Regression analysis |
8 |
0.14009 |
0.01751 |
229.10 |
<0.0001 |
Residual |
43 |
0.00329 |
0.00007644 |
|
|
Total |
51 |
0.14338 |
|
|
|