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 |
| 0.026 | 0.875 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.670 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 12.86 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 8.08e-05 |
| Time: | 21:57:51 | Log-Likelihood: | -100.36 |
| No. Observations: | 23 | AIC: | 208.7 |
| Df Residuals: | 19 | BIC: | 213.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 3.6736 | 168.261 | 0.022 | 0.983 | -348.502 355.849 |
| C(dose)[T.1] | 523.6792 | 432.088 | 1.212 | 0.240 | -380.690 1428.049 |
| expression | 4.7720 | 15.879 | 0.301 | 0.767 | -28.462 38.006 |
| expression:C(dose)[T.1] | -41.8384 | 38.556 | -1.085 | 0.291 | -122.537 38.861 |
| Omnibus: | 0.315 | Durbin-Watson: | 1.841 |
| Prob(Omnibus): | 0.854 | Jarque-Bera (JB): | 0.484 |
| Skew: | 0.099 | Prob(JB): | 0.785 |
| Kurtosis: | 2.318 | Cond. No. | 1.28e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.53 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 2.80e-05 |
| Time: | 21:57:51 | Log-Likelihood: | -101.05 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 78.8193 | 154.029 | 0.512 | 0.614 | -242.480 400.118 |
| C(dose)[T.1] | 55.0474 | 13.828 | 3.981 | 0.001 | 26.204 83.891 |
| expression | -2.3240 | 14.534 | -0.160 | 0.875 | -32.641 27.993 |
| Omnibus: | 0.367 | Durbin-Watson: | 1.920 |
| Prob(Omnibus): | 0.833 | Jarque-Bera (JB): | 0.512 |
| Skew: | 0.074 | Prob(JB): | 0.774 |
| Kurtosis: | 2.284 | Cond. No. | 390. |
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: | 21:57:51 | 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: | 0.372 |
| Model: | OLS | Adj. R-squared: | 0.342 |
| Method: | Least Squares | F-statistic: | 12.43 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00201 |
| Time: | 21:57:51 | Log-Likelihood: | -107.76 |
| No. Observations: | 23 | AIC: | 219.5 |
| Df Residuals: | 21 | BIC: | 221.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -384.5236 | 131.816 | -2.917 | 0.008 | -658.649 -110.398 |
| expression | 42.4279 | 12.036 | 3.525 | 0.002 | 17.399 67.457 |
| Omnibus: | 2.150 | Durbin-Watson: | 1.764 |
| Prob(Omnibus): | 0.341 | Jarque-Bera (JB): | 1.104 |
| Skew: | -0.042 | Prob(JB): | 0.576 |
| Kurtosis: | 1.930 | Cond. No. | 254. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.000 | 0.995 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.472 |
| Model: | OLS | Adj. R-squared: | 0.328 |
| Method: | Least Squares | F-statistic: | 3.275 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0626 |
| Time: | 21:57:51 | Log-Likelihood: | -70.513 |
| No. Observations: | 15 | AIC: | 149.0 |
| Df Residuals: | 11 | BIC: | 151.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 117.7341 | 144.057 | 0.817 | 0.431 | -199.333 434.801 |
| C(dose)[T.1] | -152.7794 | 292.214 | -0.523 | 0.611 | -795.937 490.379 |
| expression | -6.0261 | 17.199 | -0.350 | 0.733 | -43.881 31.829 |
| expression:C(dose)[T.1] | 23.9330 | 34.570 | 0.692 | 0.503 | -52.156 100.022 |
| Omnibus: | 1.871 | Durbin-Watson: | 0.904 |
| Prob(Omnibus): | 0.392 | Jarque-Bera (JB): | 1.445 |
| Skew: | -0.691 | Prob(JB): | 0.486 |
| Kurtosis: | 2.366 | Cond. No. | 375. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.885 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0281 |
| Time: | 21:57:51 | Log-Likelihood: | -70.833 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 68.2824 | 122.355 | 0.558 | 0.587 | -198.307 334.872 |
| C(dose)[T.1] | 49.2089 | 15.840 | 3.107 | 0.009 | 14.696 83.721 |
| expression | -0.1023 | 14.592 | -0.007 | 0.995 | -31.896 31.691 |
| Omnibus: | 2.723 | Durbin-Watson: | 0.807 |
| Prob(Omnibus): | 0.256 | Jarque-Bera (JB): | 1.874 |
| Skew: | -0.845 | Prob(JB): | 0.392 |
| Kurtosis: | 2.621 | Cond. No. | 133. |
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: | 21:57:51 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.071 |
| Method: | Least Squares | F-statistic: | 0.07126 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.794 |
| Time: | 21:57:51 | Log-Likelihood: | -75.259 |
| 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 | 51.6429 | 157.752 | 0.327 | 0.749 | -289.159 392.445 |
| expression | 4.9951 | 18.712 | 0.267 | 0.794 | -35.430 45.420 |
| Omnibus: | 0.421 | Durbin-Watson: | 1.615 |
| Prob(Omnibus): | 0.810 | Jarque-Bera (JB): | 0.507 |
| Skew: | 0.026 | Prob(JB): | 0.776 |
| Kurtosis: | 2.100 | Cond. No. | 133. |