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 |
| 0.495 | 0.490 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.606 |
| Method: | Least Squares | F-statistic: | 12.26 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000108 |
| Time: | 04:50:31 | Log-Likelihood: | -100.72 |
| No. Observations: | 23 | AIC: | 209.4 |
| Df Residuals: | 19 | BIC: | 214.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 0.0576 | 77.385 | 0.001 | 0.999 | -161.910 162.026 |
| C(dose)[T.1] | 87.7294 | 102.873 | 0.853 | 0.404 | -127.586 303.045 |
| expression | 9.3607 | 13.335 | 0.702 | 0.491 | -18.550 37.271 |
| expression:C(dose)[T.1] | -5.8357 | 17.965 | -0.325 | 0.749 | -43.437 31.766 |
| Omnibus: | 0.762 | Durbin-Watson: | 1.737 |
| Prob(Omnibus): | 0.683 | Jarque-Bera (JB): | 0.692 |
| Skew: | 0.043 | Prob(JB): | 0.708 |
| Kurtosis: | 2.155 | Cond. No. | 182. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 19.20 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.22e-05 |
| Time: | 04:50:31 | Log-Likelihood: | -100.78 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 18.6574 | 50.878 | 0.367 | 0.718 | -87.472 124.786 |
| C(dose)[T.1] | 54.4412 | 8.804 | 6.184 | 0.000 | 36.076 72.806 |
| expression | 6.1454 | 8.734 | 0.704 | 0.490 | -12.073 24.364 |
| Omnibus: | 0.928 | Durbin-Watson: | 1.839 |
| Prob(Omnibus): | 0.629 | Jarque-Bera (JB): | 0.753 |
| Skew: | 0.034 | Prob(JB): | 0.686 |
| Kurtosis: | 2.116 | Cond. No. | 69.6 |
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: | 04:50:31 | 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: | 0.003 |
| Model: | OLS | Adj. R-squared: | -0.045 |
| Method: | Least Squares | F-statistic: | 0.05910 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.810 |
| Time: | 04:50:31 | Log-Likelihood: | -113.07 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 99.5456 | 81.877 | 1.216 | 0.238 | -70.727 269.819 |
| expression | -3.4793 | 14.311 | -0.243 | 0.810 | -33.241 26.282 |
| Omnibus: | 2.967 | Durbin-Watson: | 2.514 |
| Prob(Omnibus): | 0.227 | Jarque-Bera (JB): | 1.482 |
| Skew: | 0.278 | Prob(JB): | 0.477 |
| Kurtosis: | 1.887 | Cond. No. | 67.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.340 | 0.270 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.552 |
| Model: | OLS | Adj. R-squared: | 0.430 |
| Method: | Least Squares | F-statistic: | 4.518 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0268 |
| Time: | 04:50:31 | Log-Likelihood: | -69.278 |
| No. Observations: | 15 | AIC: | 146.6 |
| Df Residuals: | 11 | BIC: | 149.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 71.0932 | 228.207 | 0.312 | 0.761 | -431.187 573.373 |
| C(dose)[T.1] | -268.8960 | 301.971 | -0.890 | 0.392 | -933.530 395.738 |
| expression | -0.5549 | 34.519 | -0.016 | 0.987 | -76.531 75.421 |
| expression:C(dose)[T.1] | 50.6155 | 46.694 | 1.084 | 0.302 | -52.157 153.388 |
| Omnibus: | 2.368 | Durbin-Watson: | 1.009 |
| Prob(Omnibus): | 0.306 | Jarque-Bera (JB): | 1.042 |
| Skew: | -0.640 | Prob(JB): | 0.594 |
| Kurtosis: | 3.173 | Cond. No. | 365. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.504 |
| Model: | OLS | Adj. R-squared: | 0.422 |
| Method: | Least Squares | F-statistic: | 6.101 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0149 |
| Time: | 04:50:31 | Log-Likelihood: | -70.039 |
| No. Observations: | 15 | AIC: | 146.1 |
| Df Residuals: | 12 | BIC: | 148.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -111.5769 | 155.003 | -0.720 | 0.485 | -449.300 226.146 |
| C(dose)[T.1] | 57.9420 | 16.731 | 3.463 | 0.005 | 21.489 94.395 |
| expression | 27.1074 | 23.415 | 1.158 | 0.270 | -23.908 78.123 |
| Omnibus: | 3.380 | Durbin-Watson: | 1.043 |
| Prob(Omnibus): | 0.184 | Jarque-Bera (JB): | 1.941 |
| Skew: | -0.881 | Prob(JB): | 0.379 |
| Kurtosis: | 3.008 | Cond. No. | 138. |
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: | 04:50:31 | 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: | 0.009 |
| Model: | OLS | Adj. R-squared: | -0.068 |
| Method: | Least Squares | F-statistic: | 0.1122 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.743 |
| Time: | 04:50:31 | Log-Likelihood: | -75.236 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 154.8065 | 182.825 | 0.847 | 0.412 | -240.164 549.777 |
| expression | -9.5063 | 28.383 | -0.335 | 0.743 | -70.824 51.811 |
| Omnibus: | 1.012 | Durbin-Watson: | 1.577 |
| Prob(Omnibus): | 0.603 | Jarque-Bera (JB): | 0.723 |
| Skew: | 0.098 | Prob(JB): | 0.697 |
| Kurtosis: | 1.943 | Cond. No. | 119. |