AIM Score vs. Gene Expression
Full X range:
Auto X range:
Group Comparisons: Boxplots
CP73
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
F-statistic | p-value | df difference |
0.894 | 0.356 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.665 |
Model: | OLS | Adj. R-squared: | 0.612 |
Method: | Least Squares | F-statistic: | 12.58 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.22e-05 |
Time: | 04:41:57 | Log-Likelihood: | -100.52 |
No. Observations: | 23 | AIC: | 209.0 |
Df Residuals: | 19 | BIC: | 213.6 |
Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -108.3460 | 244.148 | -0.444 | 0.662 | -619.354 402.662 |
C(dose)[T.1] | -74.7425 | 487.676 | -0.153 | 0.880 | -1095.461 945.976 |
expression | 18.1374 | 27.233 | 0.666 | 0.513 | -38.862 75.136 |
expression:C(dose)[T.1] | 13.6783 | 53.637 | 0.255 | 0.801 | -98.584 125.941 |
Omnibus: | 0.197 | Durbin-Watson: | 2.060 |
Prob(Omnibus): | 0.906 | Jarque-Bera (JB): | 0.194 |
Skew: | 0.172 | Prob(JB): | 0.908 |
Kurtosis: | 2.711 | Cond. No. | 1.20e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.664 |
Model: | OLS | Adj. R-squared: | 0.630 |
Method: | Least Squares | F-statistic: | 19.77 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.83e-05 |
Time: | 04:41:57 | Log-Likelihood: | -100.56 |
No. Observations: | 23 | AIC: | 207.1 |
Df Residuals: | 20 | BIC: | 210.5 |
Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -139.9487 | 205.384 | -0.681 | 0.503 | -568.373 288.475 |
C(dose)[T.1] | 49.5997 | 9.446 | 5.251 | 0.000 | 29.895 69.305 |
expression | 21.6635 | 22.907 | 0.946 | 0.356 | -26.119 69.446 |
Omnibus: | 0.161 | Durbin-Watson: | 2.043 |
Prob(Omnibus): | 0.923 | Jarque-Bera (JB): | 0.245 |
Skew: | 0.169 | Prob(JB): | 0.885 |
Kurtosis: | 2.624 | Cond. No. | 440. |
Model:
AIM ~ C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.649 |
Model: | OLS | Adj. R-squared: | 0.632 |
Method: | Least Squares | F-statistic: | 38.84 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
Time: | 04:41:57 | Log-Likelihood: | -101.06 |
No. Observations: | 23 | AIC: | 206.1 |
Df Residuals: | 21 | BIC: | 208.4 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 54.2083 | 5.919 | 9.159 | 0.000 | 41.900 66.517 |
C(dose)[T.1] | 53.3371 | 8.558 | 6.232 | 0.000 | 35.539 71.135 |
Omnibus: | 0.322 | Durbin-Watson: | 1.888 |
Prob(Omnibus): | 0.851 | Jarque-Bera (JB): | 0.485 |
Skew: | 0.060 | Prob(JB): | 0.785 |
Kurtosis: | 2.299 | Cond. No. | 2.57 |
Model:
AIM ~ expression
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.201 |
Model: | OLS | Adj. R-squared: | 0.163 |
Method: | Least Squares | F-statistic: | 5.284 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0319 |
Time: | 04:41:57 | Log-Likelihood: | -110.52 |
No. Observations: | 23 | AIC: | 225.0 |
Df Residuals: | 21 | BIC: | 227.3 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -571.3319 | 283.305 | -2.017 | 0.057 | -1160.497 17.833 |
expression | 71.9796 | 31.314 | 2.299 | 0.032 | 6.859 137.100 |
Omnibus: | 0.699 | Durbin-Watson: | 2.585 |
Prob(Omnibus): | 0.705 | Jarque-Bera (JB): | 0.710 |
Skew: | 0.182 | Prob(JB): | 0.701 |
Kurtosis: | 2.220 | Cond. No. | 402. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
F-statistic | p-value | df difference |
0.201 | 0.662 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.477 |
Model: | OLS | Adj. R-squared: | 0.334 |
Method: | Least Squares | F-statistic: | 3.339 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0597 |
Time: | 04:41:57 | Log-Likelihood: | -70.444 |
No. Observations: | 15 | AIC: | 148.9 |
Df Residuals: | 11 | BIC: | 151.7 |
Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 127.8273 | 303.261 | 0.422 | 0.682 | -539.646 795.301 |
C(dose)[T.1] | -188.2063 | 386.678 | -0.487 | 0.636 | -1039.279 662.866 |
expression | -6.6734 | 33.482 | -0.199 | 0.846 | -80.367 67.021 |
expression:C(dose)[T.1] | 27.4735 | 43.751 | 0.628 | 0.543 | -68.822 123.769 |
Omnibus: | 2.293 | Durbin-Watson: | 1.041 |
Prob(Omnibus): | 0.318 | Jarque-Bera (JB): | 1.325 |
Skew: | -0.725 | Prob(JB): | 0.516 |
Kurtosis: | 2.857 | Cond. No. | 594. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.458 |
Model: | OLS | Adj. R-squared: | 0.367 |
Method: | Least Squares | F-statistic: | 5.067 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0254 |
Time: | 04:41:57 | Log-Likelihood: | -70.708 |
No. Observations: | 15 | AIC: | 147.4 |
Df Residuals: | 12 | BIC: | 149.5 |
Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -17.8009 | 190.415 | -0.093 | 0.927 | -432.679 397.077 |
C(dose)[T.1] | 54.2898 | 19.305 | 2.812 | 0.016 | 12.228 96.351 |
expression | 9.4170 | 21.001 | 0.448 | 0.662 | -36.341 55.174 |
Omnibus: | 2.162 | Durbin-Watson: | 1.002 |
Prob(Omnibus): | 0.339 | Jarque-Bera (JB): | 1.411 |
Skew: | -0.735 | Prob(JB): | 0.494 |
Kurtosis: | 2.686 | Cond. No. | 218. |
Model:
AIM ~ C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.449 |
Model: | OLS | Adj. R-squared: | 0.406 |
Method: | Least Squares | F-statistic: | 10.58 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
Time: | 04:41:57 | Log-Likelihood: | -70.833 |
No. Observations: | 15 | AIC: | 145.7 |
Df Residuals: | 13 | BIC: | 147.1 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 67.4286 | 11.044 | 6.106 | 0.000 | 43.570 91.287 |
C(dose)[T.1] | 49.1964 | 15.122 | 3.253 | 0.006 | 16.527 81.866 |
Omnibus: | 2.713 | Durbin-Watson: | 0.810 |
Prob(Omnibus): | 0.258 | Jarque-Bera (JB): | 1.868 |
Skew: | -0.843 | Prob(JB): | 0.393 |
Kurtosis: | 2.619 | Cond. No. | 2.70 |
Model:
AIM ~ expression
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.101 |
Model: | OLS | Adj. R-squared: | 0.031 |
Method: | Least Squares | F-statistic: | 1.453 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.249 |
Time: | 04:41:57 | Log-Likelihood: | -74.505 |
No. Observations: | 15 | AIC: | 153.0 |
Df Residuals: | 13 | BIC: | 154.4 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 315.6434 | 184.382 | 1.712 | 0.111 | -82.689 713.976 |
expression | -25.3335 | 21.014 | -1.206 | 0.249 | -70.732 20.065 |
Omnibus: | 1.165 | Durbin-Watson: | 1.165 |
Prob(Omnibus): | 0.559 | Jarque-Bera (JB): | 0.764 |
Skew: | -0.090 | Prob(JB): | 0.683 |
Kurtosis: | 1.909 | Cond. No. | 170. |