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 |
| 0.763 | 0.393 | 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: | 12.85 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 8.10e-05 |
| Time: | 22:51:21 | 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 | 34.2013 | 49.942 | 0.685 | 0.502 | -70.328 138.730 |
| C(dose)[T.1] | -28.5948 | 112.162 | -0.255 | 0.802 | -263.352 206.162 |
| expression | 3.1793 | 7.878 | 0.404 | 0.691 | -13.309 19.668 |
| expression:C(dose)[T.1] | 10.5242 | 15.608 | 0.674 | 0.508 | -22.144 43.192 |
| Omnibus: | 1.396 | Durbin-Watson: | 1.965 |
| Prob(Omnibus): | 0.498 | Jarque-Bera (JB): | 0.937 |
| Skew: | 0.126 | Prob(JB): | 0.626 |
| Kurtosis: | 2.044 | Cond. No. | 217. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 19.58 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.95e-05 |
| Time: | 22:51:21 | Log-Likelihood: | -100.63 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 17.3288 | 42.627 | 0.407 | 0.689 | -71.591 106.248 |
| C(dose)[T.1] | 46.6216 | 11.539 | 4.040 | 0.001 | 22.551 70.692 |
| expression | 5.8604 | 6.707 | 0.874 | 0.393 | -8.131 19.852 |
| Omnibus: | 1.427 | Durbin-Watson: | 1.979 |
| Prob(Omnibus): | 0.490 | Jarque-Bera (JB): | 0.927 |
| Skew: | 0.083 | Prob(JB): | 0.629 |
| Kurtosis: | 2.031 | Cond. No. | 71.0 |
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: | 22:51:21 | 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: | 0.386 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 13.21 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00155 |
| Time: | 22:51:22 | Log-Likelihood: | -107.49 |
| No. Observations: | 23 | AIC: | 219.0 |
| Df Residuals: | 21 | BIC: | 221.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -83.8556 | 45.366 | -1.848 | 0.079 | -178.200 10.489 |
| expression | 23.9105 | 6.580 | 3.634 | 0.002 | 10.227 37.594 |
| Omnibus: | 0.543 | Durbin-Watson: | 2.578 |
| Prob(Omnibus): | 0.762 | Jarque-Bera (JB): | 0.339 |
| Skew: | -0.282 | Prob(JB): | 0.844 |
| Kurtosis: | 2.813 | Cond. No. | 56.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.040 | 0.107 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.568 |
| Model: | OLS | Adj. R-squared: | 0.450 |
| Method: | Least Squares | F-statistic: | 4.811 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0223 |
| Time: | 22:51:22 | Log-Likelihood: | -69.014 |
| No. Observations: | 15 | AIC: | 146.0 |
| Df Residuals: | 11 | BIC: | 148.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -104.7876 | 172.011 | -0.609 | 0.555 | -483.381 273.805 |
| C(dose)[T.1] | -79.4459 | 273.204 | -0.291 | 0.777 | -680.764 521.872 |
| expression | 17.7458 | 17.691 | 1.003 | 0.337 | -21.191 56.683 |
| expression:C(dose)[T.1] | 11.7790 | 27.311 | 0.431 | 0.675 | -48.332 71.890 |
| Omnibus: | 0.874 | Durbin-Watson: | 0.692 |
| Prob(Omnibus): | 0.646 | Jarque-Bera (JB): | 0.814 |
| Skew: | -0.414 | Prob(JB): | 0.666 |
| Kurtosis: | 2.214 | Cond. No. | 487. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.560 |
| Model: | OLS | Adj. R-squared: | 0.487 |
| Method: | Least Squares | F-statistic: | 7.642 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00724 |
| Time: | 22:51:22 | Log-Likelihood: | -69.139 |
| No. Observations: | 15 | AIC: | 144.3 |
| Df Residuals: | 12 | BIC: | 146.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -152.7502 | 126.699 | -1.206 | 0.251 | -428.803 123.303 |
| C(dose)[T.1] | 38.1834 | 15.413 | 2.477 | 0.029 | 4.601 71.765 |
| expression | 22.6880 | 13.013 | 1.744 | 0.107 | -5.664 51.040 |
| Omnibus: | 1.362 | Durbin-Watson: | 0.688 |
| Prob(Omnibus): | 0.506 | Jarque-Bera (JB): | 1.127 |
| Skew: | -0.533 | Prob(JB): | 0.569 |
| Kurtosis: | 2.182 | Cond. No. | 183. |
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: | 22:51:22 | 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: | 0.335 |
| Model: | OLS | Adj. R-squared: | 0.284 |
| Method: | Least Squares | F-statistic: | 6.556 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0237 |
| Time: | 22:51:22 | Log-Likelihood: | -72.237 |
| No. Observations: | 15 | AIC: | 148.5 |
| Df Residuals: | 13 | BIC: | 149.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -264.0140 | 139.935 | -1.887 | 0.082 | -566.326 38.298 |
| expression | 35.8990 | 14.020 | 2.561 | 0.024 | 5.610 66.188 |
| Omnibus: | 0.988 | Durbin-Watson: | 1.572 |
| Prob(Omnibus): | 0.610 | Jarque-Bera (JB): | 0.838 |
| Skew: | 0.342 | Prob(JB): | 0.658 |
| Kurtosis: | 2.067 | Cond. No. | 170. |