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.
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 |
|
|
|