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 |
| 0.290 | 0.596 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.686 |
| Model: | OLS | Adj. R-squared: | 0.637 |
| Method: | Least Squares | F-statistic: | 13.86 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 5.03e-05 |
| Time: | 22:48:42 | Log-Likelihood: | -99.770 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 19 | BIC: | 212.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 36.4755 | 32.973 | 1.106 | 0.282 | -32.538 105.489 |
| C(dose)[T.1] | 117.4508 | 47.136 | 2.492 | 0.022 | 18.794 216.108 |
| expression | 4.1815 | 7.651 | 0.547 | 0.591 | -11.831 20.195 |
| expression:C(dose)[T.1] | -15.7444 | 11.256 | -1.399 | 0.178 | -39.304 7.815 |
| Omnibus: | 1.397 | Durbin-Watson: | 1.651 |
| Prob(Omnibus): | 0.497 | Jarque-Bera (JB): | 0.949 |
| Skew: | 0.146 | Prob(JB): | 0.622 |
| Kurtosis: | 2.049 | Cond. No. | 62.7 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 18.91 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.45e-05 |
| Time: | 22:48:42 | Log-Likelihood: | -100.90 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 67.3198 | 25.094 | 2.683 | 0.014 | 14.975 119.664 |
| C(dose)[T.1] | 52.6274 | 8.806 | 5.976 | 0.000 | 34.258 70.997 |
| expression | -3.0918 | 5.744 | -0.538 | 0.596 | -15.074 8.891 |
| Omnibus: | 0.269 | Durbin-Watson: | 1.901 |
| Prob(Omnibus): | 0.874 | Jarque-Bera (JB): | 0.452 |
| Skew: | 0.049 | Prob(JB): | 0.798 |
| Kurtosis: | 2.320 | Cond. No. | 25.8 |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:48:43 | 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: | 0.036 |
| Model: | OLS | Adj. R-squared: | -0.010 |
| Method: | Least Squares | F-statistic: | 0.7918 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.384 |
| Time: | 22:48:43 | Log-Likelihood: | -112.68 |
| No. Observations: | 23 | AIC: | 229.4 |
| Df Residuals: | 21 | BIC: | 231.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 113.7234 | 38.867 | 2.926 | 0.008 | 32.895 194.552 |
| expression | -8.2320 | 9.251 | -0.890 | 0.384 | -27.471 11.007 |
| Omnibus: | 1.842 | Durbin-Watson: | 2.467 |
| Prob(Omnibus): | 0.398 | Jarque-Bera (JB): | 1.054 |
| Skew: | 0.111 | Prob(JB): | 0.590 |
| Kurtosis: | 1.975 | Cond. No. | 24.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.841 | 0.377 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.531 |
| Model: | OLS | Adj. R-squared: | 0.404 |
| Method: | Least Squares | F-statistic: | 4.159 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0339 |
| Time: | 22:48:43 | Log-Likelihood: | -69.614 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 11 | BIC: | 150.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -249.0278 | 232.140 | -1.073 | 0.306 | -759.964 261.909 |
| C(dose)[T.1] | 328.4641 | 267.720 | 1.227 | 0.245 | -260.784 917.712 |
| expression | 44.6695 | 32.731 | 1.365 | 0.200 | -27.370 116.709 |
| expression:C(dose)[T.1] | -39.4315 | 37.709 | -1.046 | 0.318 | -122.429 43.566 |
| Omnibus: | 2.027 | Durbin-Watson: | 1.275 |
| Prob(Omnibus): | 0.363 | Jarque-Bera (JB): | 1.446 |
| Skew: | -0.725 | Prob(JB): | 0.485 |
| Kurtosis: | 2.540 | Cond. No. | 382. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.485 |
| Model: | OLS | Adj. R-squared: | 0.399 |
| Method: | Least Squares | F-statistic: | 5.648 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0187 |
| Time: | 22:48:43 | Log-Likelihood: | -70.325 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 12 | BIC: | 148.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -38.5745 | 116.134 | -0.332 | 0.746 | -291.608 214.459 |
| C(dose)[T.1] | 48.9656 | 15.218 | 3.218 | 0.007 | 15.809 82.122 |
| expression | 14.9629 | 16.318 | 0.917 | 0.377 | -20.590 50.516 |
| Omnibus: | 2.021 | Durbin-Watson: | 0.877 |
| Prob(Omnibus): | 0.364 | Jarque-Bera (JB): | 1.524 |
| Skew: | -0.633 | Prob(JB): | 0.467 |
| Kurtosis: | 2.086 | Cond. No. | 111. |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:48:43 | 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: | 0.040 |
| Model: | OLS | Adj. R-squared: | -0.033 |
| Method: | Least Squares | F-statistic: | 0.5476 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.472 |
| Time: | 22:48:43 | Log-Likelihood: | -74.991 |
| No. Observations: | 15 | AIC: | 154.0 |
| Df Residuals: | 13 | BIC: | 155.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -18.6192 | 152.068 | -0.122 | 0.904 | -347.143 309.904 |
| expression | 15.8314 | 21.394 | 0.740 | 0.472 | -30.388 62.051 |
| Omnibus: | 1.381 | Durbin-Watson: | 1.619 |
| Prob(Omnibus): | 0.501 | Jarque-Bera (JB): | 0.933 |
| Skew: | 0.296 | Prob(JB): | 0.627 |
| Kurtosis: | 1.932 | Cond. No. | 111. |