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 |
| 4.951 | 0.038 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.750 |
| Model: | OLS | Adj. R-squared: | 0.710 |
| Method: | Least Squares | F-statistic: | 18.99 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.09e-06 |
| Time: | 04:53:13 | Log-Likelihood: | -97.168 |
| No. Observations: | 23 | AIC: | 202.3 |
| Df Residuals: | 19 | BIC: | 206.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -69.4132 | 120.154 | -0.578 | 0.570 | -320.899 182.073 |
| C(dose)[T.1] | -292.3611 | 218.747 | -1.337 | 0.197 | -750.204 165.482 |
| expression | 15.4921 | 15.043 | 1.030 | 0.316 | -15.994 46.978 |
| expression:C(dose)[T.1] | 41.0251 | 26.654 | 1.539 | 0.140 | -14.762 96.812 |
| Omnibus: | 1.937 | Durbin-Watson: | 1.403 |
| Prob(Omnibus): | 0.380 | Jarque-Bera (JB): | 1.681 |
| Skew: | 0.578 | Prob(JB): | 0.432 |
| Kurtosis: | 2.353 | Cond. No. | 574. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.719 |
| Model: | OLS | Adj. R-squared: | 0.691 |
| Method: | Least Squares | F-statistic: | 25.55 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.10e-06 |
| Time: | 04:53:13 | Log-Likelihood: | -98.519 |
| No. Observations: | 23 | AIC: | 203.0 |
| Df Residuals: | 20 | BIC: | 206.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -173.6936 | 102.573 | -1.693 | 0.106 | -387.656 40.269 |
| C(dose)[T.1] | 44.0731 | 8.887 | 4.959 | 0.000 | 25.534 62.612 |
| expression | 28.5604 | 12.836 | 2.225 | 0.038 | 1.784 55.336 |
| Omnibus: | 1.472 | Durbin-Watson: | 1.603 |
| Prob(Omnibus): | 0.479 | Jarque-Bera (JB): | 1.034 |
| Skew: | 0.225 | Prob(JB): | 0.596 |
| Kurtosis: | 2.064 | Cond. No. | 217. |
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: | 04:53:13 | 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: | 0.373 |
| Model: | OLS | Adj. R-squared: | 0.343 |
| Method: | Least Squares | F-statistic: | 12.48 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00197 |
| Time: | 04:53:13 | Log-Likelihood: | -107.74 |
| 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 | -395.2095 | 134.551 | -2.937 | 0.008 | -675.024 -115.395 |
| expression | 58.3823 | 16.525 | 3.533 | 0.002 | 24.016 92.749 |
| Omnibus: | 0.222 | Durbin-Watson: | 2.503 |
| Prob(Omnibus): | 0.895 | Jarque-Bera (JB): | 0.278 |
| Skew: | -0.198 | Prob(JB): | 0.870 |
| Kurtosis: | 2.636 | Cond. No. | 195. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 6.489 | 0.026 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.645 |
| Model: | OLS | Adj. R-squared: | 0.548 |
| Method: | Least Squares | F-statistic: | 6.664 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00792 |
| Time: | 04:53:13 | Log-Likelihood: | -67.531 |
| No. Observations: | 15 | AIC: | 143.1 |
| Df Residuals: | 11 | BIC: | 145.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -415.8436 | 257.861 | -1.613 | 0.135 | -983.392 151.705 |
| C(dose)[T.1] | 141.1479 | 355.301 | 0.397 | 0.699 | -640.864 923.160 |
| expression | 51.8898 | 27.668 | 1.875 | 0.088 | -9.006 112.786 |
| expression:C(dose)[T.1] | -11.1401 | 37.583 | -0.296 | 0.772 | -93.860 71.580 |
| Omnibus: | 0.951 | Durbin-Watson: | 1.295 |
| Prob(Omnibus): | 0.622 | Jarque-Bera (JB): | 0.540 |
| Skew: | -0.447 | Prob(JB): | 0.763 |
| Kurtosis: | 2.745 | Cond. No. | 700. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.642 |
| Model: | OLS | Adj. R-squared: | 0.583 |
| Method: | Least Squares | F-statistic: | 10.77 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00210 |
| Time: | 04:53:13 | Log-Likelihood: | -67.591 |
| No. Observations: | 15 | AIC: | 141.2 |
| Df Residuals: | 12 | BIC: | 143.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -359.6149 | 167.894 | -2.142 | 0.053 | -725.424 6.194 |
| C(dose)[T.1] | 35.9172 | 13.710 | 2.620 | 0.022 | 6.046 65.788 |
| expression | 45.8525 | 18.000 | 2.547 | 0.026 | 6.635 85.070 |
| Omnibus: | 1.291 | Durbin-Watson: | 1.258 |
| Prob(Omnibus): | 0.524 | Jarque-Bera (JB): | 0.556 |
| Skew: | -0.471 | Prob(JB): | 0.757 |
| Kurtosis: | 2.950 | Cond. No. | 255. |
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: | 04:53:13 | 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: | 0.438 |
| Model: | OLS | Adj. R-squared: | 0.394 |
| Method: | Least Squares | F-statistic: | 10.12 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00723 |
| Time: | 04:53:13 | Log-Likelihood: | -70.983 |
| No. Observations: | 15 | AIC: | 146.0 |
| Df Residuals: | 13 | BIC: | 147.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -510.2157 | 190.019 | -2.685 | 0.019 | -920.726 -99.706 |
| expression | 63.7822 | 20.054 | 3.181 | 0.007 | 20.459 107.105 |
| Omnibus: | 0.861 | Durbin-Watson: | 1.661 |
| Prob(Omnibus): | 0.650 | Jarque-Bera (JB): | 0.784 |
| Skew: | 0.451 | Prob(JB): | 0.676 |
| Kurtosis: | 2.335 | Cond. No. | 239. |