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 |
| 2.298 | 0.145 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.698 |
| Model: | OLS | Adj. R-squared: | 0.651 |
| Method: | Least Squares | F-statistic: | 14.66 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 3.50e-05 |
| Time: | 22:51:23 | Log-Likelihood: | -99.321 |
| No. Observations: | 23 | AIC: | 206.6 |
| Df Residuals: | 19 | BIC: | 211.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 55.4771 | 117.453 | 0.472 | 0.642 | -190.355 301.310 |
| C(dose)[T.1] | 173.6371 | 136.341 | 1.274 | 0.218 | -111.727 459.002 |
| expression | -0.2125 | 19.652 | -0.011 | 0.991 | -41.344 40.919 |
| expression:C(dose)[T.1] | -20.9671 | 23.035 | -0.910 | 0.374 | -69.179 27.245 |
| Omnibus: | 0.794 | Durbin-Watson: | 1.970 |
| Prob(Omnibus): | 0.672 | Jarque-Bera (JB): | 0.725 |
| Skew: | 0.122 | Prob(JB): | 0.696 |
| Kurtosis: | 2.165 | Cond. No. | 283. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.685 |
| Model: | OLS | Adj. R-squared: | 0.654 |
| Method: | Least Squares | F-statistic: | 21.77 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 9.55e-06 |
| Time: | 22:51:23 | Log-Likelihood: | -99.812 |
| No. Observations: | 23 | AIC: | 205.6 |
| Df Residuals: | 20 | BIC: | 209.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 146.5783 | 61.205 | 2.395 | 0.027 | 18.907 274.249 |
| C(dose)[T.1] | 49.7845 | 8.630 | 5.769 | 0.000 | 31.783 67.786 |
| expression | -15.4737 | 10.208 | -1.516 | 0.145 | -36.767 5.819 |
| Omnibus: | 1.129 | Durbin-Watson: | 1.924 |
| Prob(Omnibus): | 0.569 | Jarque-Bera (JB): | 0.904 |
| Skew: | 0.211 | Prob(JB): | 0.636 |
| Kurtosis: | 2.125 | Cond. No. | 89.5 |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:51:23 | 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: | 0.161 |
| Model: | OLS | Adj. R-squared: | 0.122 |
| Method: | Least Squares | F-statistic: | 4.043 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0574 |
| Time: | 22:51:23 | Log-Likelihood: | -111.08 |
| No. Observations: | 23 | AIC: | 226.2 |
| Df Residuals: | 21 | BIC: | 228.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 264.0920 | 91.931 | 2.873 | 0.009 | 72.912 455.272 |
| expression | -31.4651 | 15.648 | -2.011 | 0.057 | -64.007 1.077 |
| Omnibus: | 3.514 | Durbin-Watson: | 2.325 |
| Prob(Omnibus): | 0.173 | Jarque-Bera (JB): | 1.419 |
| Skew: | 0.125 | Prob(JB): | 0.492 |
| Kurtosis: | 1.809 | Cond. No. | 84.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.024 | 0.879 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.497 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 3.622 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0488 |
| Time: | 22:51:23 | Log-Likelihood: | -70.147 |
| No. Observations: | 15 | AIC: | 148.3 |
| Df Residuals: | 11 | BIC: | 151.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 168.5774 | 140.697 | 1.198 | 0.256 | -141.095 478.250 |
| C(dose)[T.1] | -180.7713 | 225.916 | -0.800 | 0.441 | -678.010 316.467 |
| expression | -16.6016 | 23.016 | -0.721 | 0.486 | -67.259 34.056 |
| expression:C(dose)[T.1] | 39.7773 | 39.208 | 1.015 | 0.332 | -46.518 126.073 |
| Omnibus: | 1.248 | Durbin-Watson: | 0.711 |
| Prob(Omnibus): | 0.536 | Jarque-Bera (JB): | 1.031 |
| Skew: | -0.553 | Prob(JB): | 0.597 |
| Kurtosis: | 2.348 | Cond. No. | 215. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.450 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.907 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0277 |
| Time: | 22:51:23 | Log-Likelihood: | -70.818 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 85.0631 | 114.242 | 0.745 | 0.471 | -163.849 333.975 |
| C(dose)[T.1] | 47.6499 | 18.617 | 2.559 | 0.025 | 7.086 88.214 |
| expression | -2.8944 | 18.656 | -0.155 | 0.879 | -43.542 37.753 |
| Omnibus: | 2.553 | Durbin-Watson: | 0.771 |
| Prob(Omnibus): | 0.279 | Jarque-Bera (JB): | 1.762 |
| Skew: | -0.816 | Prob(JB): | 0.414 |
| Kurtosis: | 2.607 | Cond. No. | 88.0 |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:51:23 | 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: | 0.150 |
| Model: | OLS | Adj. R-squared: | 0.084 |
| Method: | Least Squares | F-statistic: | 2.286 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.154 |
| Time: | 22:51:23 | Log-Likelihood: | -74.085 |
| No. Observations: | 15 | AIC: | 152.2 |
| Df Residuals: | 13 | BIC: | 153.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 258.9549 | 109.712 | 2.360 | 0.035 | 21.937 495.972 |
| expression | -28.4601 | 18.822 | -1.512 | 0.154 | -69.122 12.202 |
| Omnibus: | 2.138 | Durbin-Watson: | 0.917 |
| Prob(Omnibus): | 0.343 | Jarque-Bera (JB): | 1.019 |
| Skew: | 0.168 | Prob(JB): | 0.601 |
| Kurtosis: | 1.768 | Cond. No. | 70.2 |