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 |
| 1.908 | 0.182 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.683 |
| Model: | OLS | Adj. R-squared: | 0.633 |
| Method: | Least Squares | F-statistic: | 13.63 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.58e-05 |
| Time: | 05:12:33 | Log-Likelihood: | -99.900 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 19 | BIC: | 212.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 94.7942 | 44.902 | 2.111 | 0.048 | 0.814 188.775 |
| C(dose)[T.1] | 89.2867 | 83.522 | 1.069 | 0.298 | -85.527 264.101 |
| expression | -8.4789 | 9.299 | -0.912 | 0.373 | -27.942 10.984 |
| expression:C(dose)[T.1] | -7.6279 | 17.448 | -0.437 | 0.667 | -44.148 28.892 |
| Omnibus: | 1.081 | Durbin-Watson: | 1.856 |
| Prob(Omnibus): | 0.582 | Jarque-Bera (JB): | 0.905 |
| Skew: | 0.449 | Prob(JB): | 0.636 |
| Kurtosis: | 2.626 | Cond. No. | 116. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.680 |
| Model: | OLS | Adj. R-squared: | 0.648 |
| Method: | Least Squares | F-statistic: | 21.21 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.14e-05 |
| Time: | 05:12:33 | Log-Likelihood: | -100.02 |
| No. Observations: | 23 | AIC: | 206.0 |
| Df Residuals: | 20 | BIC: | 209.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 105.1645 | 37.346 | 2.816 | 0.011 | 27.263 183.066 |
| C(dose)[T.1] | 52.9651 | 8.384 | 6.318 | 0.000 | 35.477 70.453 |
| expression | -10.6454 | 7.707 | -1.381 | 0.182 | -26.723 5.432 |
| Omnibus: | 0.792 | Durbin-Watson: | 1.908 |
| Prob(Omnibus): | 0.673 | Jarque-Bera (JB): | 0.668 |
| Skew: | 0.380 | Prob(JB): | 0.716 |
| Kurtosis: | 2.654 | Cond. No. | 44.9 |
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: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 05:12:33 | 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: | 0.040 |
| Model: | OLS | Adj. R-squared: | -0.005 |
| Method: | Least Squares | F-statistic: | 0.8805 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.359 |
| Time: | 05:12:33 | Log-Likelihood: | -112.63 |
| No. Observations: | 23 | AIC: | 229.3 |
| Df Residuals: | 21 | BIC: | 231.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 137.9575 | 62.467 | 2.208 | 0.038 | 8.049 267.866 |
| expression | -12.2097 | 13.012 | -0.938 | 0.359 | -39.269 14.850 |
| Omnibus: | 3.697 | Durbin-Watson: | 2.559 |
| Prob(Omnibus): | 0.157 | Jarque-Bera (JB): | 1.752 |
| Skew: | 0.348 | Prob(JB): | 0.416 |
| Kurtosis: | 1.841 | Cond. No. | 44.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.178 | 0.299 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.527 |
| Model: | OLS | Adj. R-squared: | 0.398 |
| Method: | Least Squares | F-statistic: | 4.086 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0355 |
| Time: | 05:12:33 | Log-Likelihood: | -69.684 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 11 | BIC: | 150.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 104.9802 | 89.567 | 1.172 | 0.266 | -92.155 302.115 |
| C(dose)[T.1] | 152.5194 | 142.170 | 1.073 | 0.306 | -160.395 465.434 |
| expression | -6.9912 | 16.546 | -0.423 | 0.681 | -43.409 29.427 |
| expression:C(dose)[T.1] | -24.1488 | 29.396 | -0.821 | 0.429 | -88.849 40.551 |
| Omnibus: | 2.235 | Durbin-Watson: | 1.312 |
| Prob(Omnibus): | 0.327 | Jarque-Bera (JB): | 1.632 |
| Skew: | -0.766 | Prob(JB): | 0.442 |
| Kurtosis: | 2.486 | Cond. No. | 118. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.498 |
| Model: | OLS | Adj. R-squared: | 0.414 |
| Method: | Least Squares | F-statistic: | 5.953 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0160 |
| Time: | 05:12:34 | Log-Likelihood: | -70.131 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 12 | BIC: | 148.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 146.0752 | 73.282 | 1.993 | 0.069 | -13.592 305.742 |
| C(dose)[T.1] | 36.7894 | 18.874 | 1.949 | 0.075 | -4.334 77.913 |
| expression | -14.6421 | 13.490 | -1.085 | 0.299 | -44.033 14.749 |
| Omnibus: | 1.801 | Durbin-Watson: | 1.187 |
| Prob(Omnibus): | 0.406 | Jarque-Bera (JB): | 1.414 |
| Skew: | -0.669 | Prob(JB): | 0.493 |
| Kurtosis: | 2.314 | Cond. No. | 51.4 |
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: | 05:12:34 | 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: | 0.339 |
| Model: | OLS | Adj. R-squared: | 0.288 |
| Method: | Least Squares | F-statistic: | 6.671 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0227 |
| Time: | 05:12:34 | Log-Likelihood: | -72.194 |
| No. Observations: | 15 | AIC: | 148.4 |
| Df Residuals: | 13 | BIC: | 149.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 244.0292 | 58.798 | 4.150 | 0.001 | 117.003 371.056 |
| expression | -30.5656 | 11.834 | -2.583 | 0.023 | -56.131 -5.000 |
| Omnibus: | 3.395 | Durbin-Watson: | 1.533 |
| Prob(Omnibus): | 0.183 | Jarque-Bera (JB): | 1.350 |
| Skew: | -0.298 | Prob(JB): | 0.509 |
| Kurtosis: | 1.657 | Cond. No. | 36.8 |