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 |
| 0.221 | 0.644 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.713 |
| Model: | OLS | Adj. R-squared: | 0.667 |
| Method: | Least Squares | F-statistic: | 15.71 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.22e-05 |
| Time: | 03:47:52 | Log-Likelihood: | -98.762 |
| No. Observations: | 23 | AIC: | 205.5 |
| Df Residuals: | 19 | BIC: | 210.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 133.4795 | 76.956 | 1.734 | 0.099 | -27.592 294.551 |
| C(dose)[T.1] | -165.2965 | 110.182 | -1.500 | 0.150 | -395.911 65.318 |
| expression | -12.0564 | 11.673 | -1.033 | 0.315 | -36.488 12.375 |
| expression:C(dose)[T.1] | 33.1586 | 16.674 | 1.989 | 0.061 | -1.741 68.058 |
| Omnibus: | 1.278 | Durbin-Watson: | 1.813 |
| Prob(Omnibus): | 0.528 | Jarque-Bera (JB): | 1.177 |
| Skew: | 0.454 | Prob(JB): | 0.555 |
| Kurtosis: | 2.365 | Cond. No. | 236. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 18.81 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.54e-05 |
| Time: | 03:47:52 | Log-Likelihood: | -100.94 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 26.6308 | 59.024 | 0.451 | 0.657 | -96.490 149.752 |
| C(dose)[T.1] | 53.2150 | 8.726 | 6.099 | 0.000 | 35.013 71.417 |
| expression | 4.1943 | 8.930 | 0.470 | 0.644 | -14.433 22.822 |
| Omnibus: | 0.608 | Durbin-Watson: | 1.934 |
| Prob(Omnibus): | 0.738 | Jarque-Bera (JB): | 0.643 |
| Skew: | 0.118 | Prob(JB): | 0.725 |
| Kurtosis: | 2.215 | Cond. No. | 91.8 |
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: | 03:47:52 | 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: | 0.007 |
| Model: | OLS | Adj. R-squared: | -0.040 |
| Method: | Least Squares | F-statistic: | 0.1560 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.697 |
| Time: | 03:47:53 | Log-Likelihood: | -113.02 |
| No. Observations: | 23 | AIC: | 230.0 |
| Df Residuals: | 21 | BIC: | 232.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 41.3854 | 97.325 | 0.425 | 0.675 | -161.012 243.783 |
| expression | 5.8176 | 14.731 | 0.395 | 0.697 | -24.816 36.451 |
| Omnibus: | 3.161 | Durbin-Watson: | 2.568 |
| Prob(Omnibus): | 0.206 | Jarque-Bera (JB): | 1.428 |
| Skew: | 0.206 | Prob(JB): | 0.490 |
| Kurtosis: | 1.851 | Cond. No. | 91.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.101 | 0.756 | 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: | 0.335 |
| Method: | Least Squares | F-statistic: | 3.351 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0592 |
| Time: | 03:47:53 | Log-Likelihood: | -70.432 |
| 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 | 1.0779 | 107.224 | 0.010 | 0.992 | -234.920 237.076 |
| C(dose)[T.1] | 199.9777 | 210.904 | 0.948 | 0.363 | -264.219 664.175 |
| expression | 11.1669 | 17.938 | 0.623 | 0.546 | -28.315 50.649 |
| expression:C(dose)[T.1] | -25.8523 | 36.277 | -0.713 | 0.491 | -105.698 53.993 |
| Omnibus: | 2.063 | Durbin-Watson: | 0.985 |
| Prob(Omnibus): | 0.356 | Jarque-Bera (JB): | 1.176 |
| Skew: | -0.682 | Prob(JB): | 0.555 |
| Kurtosis: | 2.848 | Cond. No. | 191. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.453 |
| Model: | OLS | Adj. R-squared: | 0.362 |
| Method: | Least Squares | F-statistic: | 4.976 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0267 |
| Time: | 03:47:53 | Log-Likelihood: | -70.770 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 38.6366 | 91.442 | 0.423 | 0.680 | -160.599 237.872 |
| C(dose)[T.1] | 50.1291 | 15.947 | 3.143 | 0.008 | 15.383 84.875 |
| expression | 4.8457 | 15.269 | 0.317 | 0.756 | -28.422 38.113 |
| Omnibus: | 2.570 | Durbin-Watson: | 0.811 |
| Prob(Omnibus): | 0.277 | Jarque-Bera (JB): | 1.842 |
| Skew: | -0.825 | Prob(JB): | 0.398 |
| Kurtosis: | 2.527 | Cond. No. | 70.8 |
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: | 03:47:53 | 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: | 0.003 |
| Model: | OLS | Adj. R-squared: | -0.073 |
| Method: | Least Squares | F-statistic: | 0.04219 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.840 |
| Time: | 03:47:53 | Log-Likelihood: | -75.276 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 117.0193 | 114.139 | 1.025 | 0.324 | -129.562 363.601 |
| expression | -3.9994 | 19.470 | -0.205 | 0.840 | -46.062 38.063 |
| Omnibus: | 0.290 | Durbin-Watson: | 1.632 |
| Prob(Omnibus): | 0.865 | Jarque-Bera (JB): | 0.446 |
| Skew: | -0.008 | Prob(JB): | 0.800 |
| Kurtosis: | 2.156 | Cond. No. | 67.9 |