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 | 0.643 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.598 |
| Method: | Least Squares | F-statistic: | 11.91 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.000129 |
| Time: | 17:01:47 | Log-Likelihood: | -100.94 |
| No. Observations: | 23 | AIC: | 209.9 |
| Df Residuals: | 19 | BIC: | 214.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 38.8020 | 54.194 | 0.716 | 0.483 | -74.628 152.232 |
| C(dose)[T.1] | 51.7685 | 72.185 | 0.717 | 0.482 | -99.316 202.853 |
| expression | 2.4926 | 8.711 | 0.286 | 0.778 | -15.739 20.724 |
| expression:C(dose)[T.1] | 0.3610 | 11.787 | 0.031 | 0.976 | -24.310 25.032 |
| Omnibus: | 0.269 | Durbin-Watson: | 1.890 |
| Prob(Omnibus): | 0.874 | Jarque-Bera (JB): | 0.451 |
| Skew: | -0.023 | Prob(JB): | 0.798 |
| Kurtosis: | 2.315 | Cond. No. | 134. |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 2.54e-05 |
| Time: | 17:01:47 | 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 | 37.5836 | 35.867 | 1.048 | 0.307 | -37.233 112.401 |
| C(dose)[T.1] | 53.9618 | 8.822 | 6.116 | 0.000 | 35.559 72.365 |
| expression | 2.6897 | 5.720 | 0.470 | 0.643 | -9.242 14.622 |
| Omnibus: | 0.277 | Durbin-Watson: | 1.892 |
| Prob(Omnibus): | 0.871 | Jarque-Bera (JB): | 0.457 |
| Skew: | -0.020 | Prob(JB): | 0.796 |
| Kurtosis: | 2.311 | Cond. No. | 51.9 |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 17:01:47 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.044 |
| Method: | Least Squares | F-statistic: | 0.07605 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.785 |
| Time: | 17:01:47 | Log-Likelihood: | -113.06 |
| 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 | 95.3684 | 57.210 | 1.667 | 0.110 | -23.605 214.342 |
| expression | -2.5785 | 9.350 | -0.276 | 0.785 | -22.023 16.866 |
| Omnibus: | 2.512 | Durbin-Watson: | 2.505 |
| Prob(Omnibus): | 0.285 | Jarque-Bera (JB): | 1.426 |
| Skew: | 0.308 | Prob(JB): | 0.490 |
| Kurtosis: | 1.948 | Cond. No. | 49.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.815 | 0.384 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.610 |
| Model: | OLS | Adj. R-squared: | 0.504 |
| Method: | Least Squares | F-statistic: | 5.737 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0130 |
| Time: | 17:01:47 | Log-Likelihood: | -68.236 |
| No. Observations: | 15 | AIC: | 144.5 |
| Df Residuals: | 11 | BIC: | 147.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -93.2080 | 175.629 | -0.531 | 0.606 | -479.765 293.349 |
| C(dose)[T.1] | 474.5986 | 223.212 | 2.126 | 0.057 | -16.688 965.885 |
| expression | 25.9060 | 28.277 | 0.916 | 0.379 | -36.331 88.143 |
| expression:C(dose)[T.1] | -66.6138 | 35.300 | -1.887 | 0.086 | -144.309 11.081 |
| Omnibus: | 1.738 | Durbin-Watson: | 0.890 |
| Prob(Omnibus): | 0.419 | Jarque-Bera (JB): | 0.873 |
| Skew: | -0.590 | Prob(JB): | 0.646 |
| Kurtosis: | 2.927 | Cond. No. | 300. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.484 |
| Model: | OLS | Adj. R-squared: | 0.398 |
| Method: | Least Squares | F-statistic: | 5.625 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0189 |
| Time: | 17:01:47 | Log-Likelihood: | -70.340 |
| 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 | 171.8369 | 116.152 | 1.479 | 0.165 | -81.236 424.910 |
| C(dose)[T.1] | 54.3031 | 16.247 | 3.342 | 0.006 | 18.905 89.701 |
| expression | -16.8380 | 18.646 | -0.903 | 0.384 | -57.464 23.788 |
| Omnibus: | 1.930 | Durbin-Watson: | 0.770 |
| Prob(Omnibus): | 0.381 | Jarque-Bera (JB): | 1.300 |
| Skew: | -0.697 | Prob(JB): | 0.522 |
| Kurtosis: | 2.631 | Cond. No. | 100. |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 17:01:47 | 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: | 0.04327 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.838 |
| Time: | 17:01:48 | Log-Likelihood: | -75.275 |
| 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 | 62.7787 | 148.828 | 0.422 | 0.680 | -258.745 384.303 |
| expression | 4.8547 | 23.337 | 0.208 | 0.838 | -45.562 55.271 |
| Omnibus: | 0.562 | Durbin-Watson: | 1.598 |
| Prob(Omnibus): | 0.755 | Jarque-Bera (JB): | 0.574 |
| Skew: | 0.108 | Prob(JB): | 0.750 |
| Kurtosis: | 2.066 | Cond. No. | 95.9 |