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 |
| 0.604 | 0.446 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.686 |
| Model: | OLS | Adj. R-squared: | 0.637 |
| Method: | Least Squares | F-statistic: | 13.87 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 5.01e-05 |
| Time: | 22:57:33 | Log-Likelihood: | -99.766 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 19 | BIC: | 212.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -21.9186 | 51.309 | -0.427 | 0.674 | -129.309 85.472 |
| C(dose)[T.1] | 136.0315 | 61.999 | 2.194 | 0.041 | 6.266 265.797 |
| expression | 12.5716 | 8.417 | 1.494 | 0.152 | -5.046 30.189 |
| expression:C(dose)[T.1] | -13.8754 | 10.821 | -1.282 | 0.215 | -36.525 8.774 |
| Omnibus: | 0.127 | Durbin-Watson: | 1.246 |
| Prob(Omnibus): | 0.938 | Jarque-Bera (JB): | 0.303 |
| Skew: | -0.137 | Prob(JB): | 0.859 |
| Kurtosis: | 2.509 | Cond. No. | 115. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.625 |
| Method: | Least Squares | F-statistic: | 19.36 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.10e-05 |
| Time: | 22:57:33 | Log-Likelihood: | -100.72 |
| No. Observations: | 23 | AIC: | 207.4 |
| Df Residuals: | 20 | BIC: | 210.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 28.9166 | 33.089 | 0.874 | 0.393 | -40.107 97.940 |
| C(dose)[T.1] | 57.5907 | 10.228 | 5.631 | 0.000 | 36.255 78.926 |
| expression | 4.1767 | 5.375 | 0.777 | 0.446 | -7.034 15.388 |
| Omnibus: | 0.577 | Durbin-Watson: | 1.793 |
| Prob(Omnibus): | 0.749 | Jarque-Bera (JB): | 0.624 |
| Skew: | 0.098 | Prob(JB): | 0.732 |
| Kurtosis: | 2.218 | Cond. No. | 45.5 |
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: | 22:57:34 | 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: | 0.119 |
| Model: | OLS | Adj. R-squared: | 0.077 |
| Method: | Least Squares | F-statistic: | 2.846 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.106 |
| Time: | 22:57:34 | Log-Likelihood: | -111.64 |
| No. Observations: | 23 | AIC: | 227.3 |
| Df Residuals: | 21 | BIC: | 229.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 146.6371 | 40.244 | 3.644 | 0.002 | 62.946 230.328 |
| expression | -12.0177 | 7.124 | -1.687 | 0.106 | -26.833 2.798 |
| Omnibus: | 1.821 | Durbin-Watson: | 2.491 |
| Prob(Omnibus): | 0.402 | Jarque-Bera (JB): | 1.037 |
| Skew: | 0.088 | Prob(JB): | 0.595 |
| Kurtosis: | 1.975 | Cond. No. | 34.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.851 | 0.199 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.531 |
| Model: | OLS | Adj. R-squared: | 0.403 |
| Method: | Least Squares | F-statistic: | 4.152 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0340 |
| Time: | 22:57:34 | Log-Likelihood: | -69.621 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 11 | BIC: | 150.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 141.7606 | 59.852 | 2.369 | 0.037 | 10.027 273.494 |
| C(dose)[T.1] | 11.5910 | 88.036 | 0.132 | 0.898 | -182.175 205.357 |
| expression | -14.7732 | 11.690 | -1.264 | 0.232 | -40.503 10.956 |
| expression:C(dose)[T.1] | 7.6354 | 17.031 | 0.448 | 0.663 | -29.849 45.119 |
| Omnibus: | 2.062 | Durbin-Watson: | 1.242 |
| Prob(Omnibus): | 0.357 | Jarque-Bera (JB): | 1.458 |
| Skew: | -0.730 | Prob(JB): | 0.483 |
| Kurtosis: | 2.555 | Cond. No. | 81.8 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.522 |
| Model: | OLS | Adj. R-squared: | 0.443 |
| Method: | Least Squares | F-statistic: | 6.564 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0119 |
| Time: | 22:57:34 | Log-Likelihood: | -69.757 |
| No. Observations: | 15 | AIC: | 145.5 |
| Df Residuals: | 12 | BIC: | 147.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 123.6596 | 42.688 | 2.897 | 0.013 | 30.651 216.668 |
| C(dose)[T.1] | 50.4681 | 14.680 | 3.438 | 0.005 | 18.484 82.453 |
| expression | -11.1757 | 8.213 | -1.361 | 0.199 | -29.071 6.719 |
| Omnibus: | 2.007 | Durbin-Watson: | 1.106 |
| Prob(Omnibus): | 0.367 | Jarque-Bera (JB): | 1.451 |
| Skew: | -0.722 | Prob(JB): | 0.484 |
| Kurtosis: | 2.514 | Cond. No. | 31.5 |
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: | 22:57:34 | 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: | 0.052 |
| Model: | OLS | Adj. R-squared: | -0.021 |
| Method: | Least Squares | F-statistic: | 0.7145 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.413 |
| Time: | 22:57:34 | Log-Likelihood: | -74.899 |
| No. Observations: | 15 | AIC: | 153.8 |
| Df Residuals: | 13 | BIC: | 155.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 141.4217 | 57.358 | 2.466 | 0.028 | 17.508 265.335 |
| expression | -9.3780 | 11.095 | -0.845 | 0.413 | -33.347 14.591 |
| Omnibus: | 3.199 | Durbin-Watson: | 1.730 |
| Prob(Omnibus): | 0.202 | Jarque-Bera (JB): | 1.304 |
| Skew: | 0.286 | Prob(JB): | 0.521 |
| Kurtosis: | 1.673 | Cond. No. | 31.1 |