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 |
| 0.364 | 0.553 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.704 |
| Model: | OLS | Adj. R-squared: | 0.657 |
| Method: | Least Squares | F-statistic: | 15.07 |
| Date: | Mon, 03 Nov 2025 | Prob (F-statistic): | 2.92e-05 |
| Time: | 20:58:02 | Log-Likelihood: | -99.100 |
| No. Observations: | 23 | AIC: | 206.2 |
| Df Residuals: | 19 | BIC: | 210.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 108.1584 | 53.747 | 2.012 | 0.059 | -4.336 220.653 |
| C(dose)[T.1] | -63.5688 | 67.074 | -0.948 | 0.355 | -203.956 76.818 |
| expression | -10.5919 | 10.492 | -1.009 | 0.325 | -32.553 11.369 |
| expression:C(dose)[T.1] | 23.5033 | 13.278 | 1.770 | 0.093 | -4.289 51.295 |
| Omnibus: | 0.343 | Durbin-Watson: | 1.755 |
| Prob(Omnibus): | 0.842 | Jarque-Bera (JB): | 0.503 |
| Skew: | -0.180 | Prob(JB): | 0.778 |
| Kurtosis: | 2.372 | Cond. No. | 116. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.655 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 19.01 |
| Date: | Mon, 03 Nov 2025 | Prob (F-statistic): | 2.37e-05 |
| Time: | 20:58:02 | Log-Likelihood: | -100.86 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 33.4098 | 34.976 | 0.955 | 0.351 | -39.549 106.369 |
| C(dose)[T.1] | 54.2254 | 8.815 | 6.152 | 0.000 | 35.838 72.613 |
| expression | 4.0833 | 6.765 | 0.604 | 0.553 | -10.028 18.194 |
| Omnibus: | 0.410 | Durbin-Watson: | 1.763 |
| Prob(Omnibus): | 0.815 | Jarque-Bera (JB): | 0.551 |
| Skew: | 0.199 | Prob(JB): | 0.759 |
| Kurtosis: | 2.355 | Cond. No. | 42.3 |
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: | Mon, 03 Nov 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 20:58:02 | 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: | -0.044 |
| Method: | Least Squares | F-statistic: | 0.06693 |
| Date: | Mon, 03 Nov 2025 | Prob (F-statistic): | 0.798 |
| Time: | 20:58:02 | 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 | 94.0063 | 55.699 | 1.688 | 0.106 | -21.825 209.838 |
| expression | -2.8638 | 11.069 | -0.259 | 0.798 | -25.884 20.156 |
| Omnibus: | 2.728 | Durbin-Watson: | 2.440 |
| Prob(Omnibus): | 0.256 | Jarque-Bera (JB): | 1.519 |
| Skew: | 0.334 | Prob(JB): | 0.468 |
| Kurtosis: | 1.932 | Cond. No. | 40.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.938 | 0.352 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.503 |
| Model: | OLS | Adj. R-squared: | 0.368 |
| Method: | Least Squares | F-statistic: | 3.716 |
| Date: | Mon, 03 Nov 2025 | Prob (F-statistic): | 0.0457 |
| Time: | 20:58:02 | Log-Likelihood: | -70.052 |
| No. Observations: | 15 | AIC: | 148.1 |
| Df Residuals: | 11 | BIC: | 150.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 168.5604 | 95.212 | 1.770 | 0.104 | -41.000 378.121 |
| C(dose)[T.1] | -27.9805 | 135.154 | -0.207 | 0.840 | -325.453 269.492 |
| expression | -19.1590 | 17.908 | -1.070 | 0.308 | -58.574 20.256 |
| expression:C(dose)[T.1] | 14.5541 | 25.623 | 0.568 | 0.581 | -41.841 70.949 |
| Omnibus: | 2.249 | Durbin-Watson: | 0.980 |
| Prob(Omnibus): | 0.325 | Jarque-Bera (JB): | 1.586 |
| Skew: | -0.765 | Prob(JB): | 0.452 |
| Kurtosis: | 2.559 | Cond. No. | 125. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.489 |
| Model: | OLS | Adj. R-squared: | 0.404 |
| Method: | Least Squares | F-statistic: | 5.736 |
| Date: | Mon, 03 Nov 2025 | Prob (F-statistic): | 0.0179 |
| Time: | 20:58:02 | Log-Likelihood: | -70.268 |
| No. Observations: | 15 | AIC: | 146.5 |
| Df Residuals: | 12 | BIC: | 148.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 131.0334 | 66.598 | 1.968 | 0.073 | -14.070 276.137 |
| C(dose)[T.1] | 48.2742 | 15.188 | 3.178 | 0.008 | 15.182 81.366 |
| expression | -12.0497 | 12.441 | -0.969 | 0.352 | -39.157 15.057 |
| Omnibus: | 1.857 | Durbin-Watson: | 0.876 |
| Prob(Omnibus): | 0.395 | Jarque-Bera (JB): | 1.420 |
| Skew: | -0.691 | Prob(JB): | 0.492 |
| Kurtosis: | 2.399 | Cond. No. | 48.3 |
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: | Mon, 03 Nov 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 20:58:02 | 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: | 0.058 |
| Model: | OLS | Adj. R-squared: | -0.014 |
| Method: | Least Squares | F-statistic: | 0.8053 |
| Date: | Mon, 03 Nov 2025 | Prob (F-statistic): | 0.386 |
| Time: | 20:58:02 | Log-Likelihood: | -74.849 |
| No. Observations: | 15 | AIC: | 153.7 |
| Df Residuals: | 13 | BIC: | 155.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 169.7642 | 85.371 | 1.989 | 0.068 | -14.669 354.197 |
| expression | -14.5287 | 16.190 | -0.897 | 0.386 | -49.505 20.448 |
| Omnibus: | 1.521 | Durbin-Watson: | 1.696 |
| Prob(Omnibus): | 0.467 | Jarque-Bera (JB): | 0.977 |
| Skew: | 0.303 | Prob(JB): | 0.614 |
| Kurtosis: | 1.906 | Cond. No. | 47.3 |