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 |
| 46.839 | 0.000 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.896 |
| Model: | OLS | Adj. R-squared: | 0.880 |
| Method: | Least Squares | F-statistic: | 54.71 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 1.54e-09 |
| Time: | 09:57:46 | Log-Likelihood: | -87.049 |
| No. Observations: | 23 | AIC: | 182.1 |
| Df Residuals: | 19 | BIC: | 186.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 262.3138 | 46.234 | 5.674 | 0.000 | 165.546 359.082 |
| C(dose)[T.1] | 91.8772 | 67.775 | 1.356 | 0.191 | -49.978 233.732 |
| expression | -77.8911 | 17.258 | -4.513 | 0.000 | -114.013 -41.769 |
| expression:C(dose)[T.1] | -11.9666 | 24.943 | -0.480 | 0.637 | -64.173 40.240 |
| Omnibus: | 0.319 | Durbin-Watson: | 2.641 |
| Prob(Omnibus): | 0.853 | Jarque-Bera (JB): | 0.487 |
| Skew: | 0.119 | Prob(JB): | 0.784 |
| Kurtosis: | 2.328 | Cond. No. | 110. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.895 |
| Model: | OLS | Adj. R-squared: | 0.884 |
| Method: | Least Squares | F-statistic: | 85.23 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 1.63e-10 |
| Time: | 09:57:46 | Log-Likelihood: | -87.187 |
| No. Observations: | 23 | AIC: | 180.4 |
| Df Residuals: | 20 | BIC: | 183.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 277.6196 | 32.812 | 8.461 | 0.000 | 209.175 346.064 |
| C(dose)[T.1] | 59.4494 | 4.880 | 12.183 | 0.000 | 49.271 69.628 |
| expression | -83.6199 | 12.218 | -6.844 | 0.000 | -109.106 -58.133 |
| Omnibus: | 0.071 | Durbin-Watson: | 2.556 |
| Prob(Omnibus): | 0.965 | Jarque-Bera (JB): | 0.295 |
| Skew: | 0.018 | Prob(JB): | 0.863 |
| Kurtosis: | 2.446 | Cond. No. | 42.8 |
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: | Tue, 04 Nov 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 09:57:46 | 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: | 0.116 |
| Model: | OLS | Adj. R-squared: | 0.074 |
| Method: | Least Squares | F-statistic: | 2.747 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.112 |
| Time: | 09:57:46 | Log-Likelihood: | -111.69 |
| No. Observations: | 23 | AIC: | 227.4 |
| Df Residuals: | 21 | BIC: | 229.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 232.3106 | 92.325 | 2.516 | 0.020 | 40.309 424.312 |
| expression | -56.3759 | 34.018 | -1.657 | 0.112 | -127.119 14.367 |
| Omnibus: | 9.404 | Durbin-Watson: | 2.736 |
| Prob(Omnibus): | 0.009 | Jarque-Bera (JB): | 2.357 |
| Skew: | 0.284 | Prob(JB): | 0.308 |
| Kurtosis: | 1.538 | Cond. No. | 41.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.451 | 0.088 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.592 |
| Model: | OLS | Adj. R-squared: | 0.480 |
| Method: | Least Squares | F-statistic: | 5.313 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0165 |
| Time: | 09:57:46 | Log-Likelihood: | -68.583 |
| No. Observations: | 15 | AIC: | 145.2 |
| Df Residuals: | 11 | BIC: | 148.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -23.8526 | 164.124 | -0.145 | 0.887 | -385.087 337.382 |
| C(dose)[T.1] | -97.3925 | 207.412 | -0.470 | 0.648 | -553.904 359.119 |
| expression | 33.7898 | 60.634 | 0.557 | 0.588 | -99.664 167.244 |
| expression:C(dose)[T.1] | 56.4154 | 77.304 | 0.730 | 0.481 | -113.730 226.561 |
| Omnibus: | 0.348 | Durbin-Watson: | 0.524 |
| Prob(Omnibus): | 0.840 | Jarque-Bera (JB): | 0.475 |
| Skew: | -0.042 | Prob(JB): | 0.789 |
| Kurtosis: | 2.132 | Cond. No. | 125. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.572 |
| Model: | OLS | Adj. R-squared: | 0.501 |
| Method: | Least Squares | F-statistic: | 8.015 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.00616 |
| Time: | 09:57:46 | Log-Likelihood: | -68.937 |
| No. Observations: | 15 | AIC: | 143.9 |
| Df Residuals: | 12 | BIC: | 146.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -117.6123 | 100.122 | -1.175 | 0.263 | -335.759 100.534 |
| C(dose)[T.1] | 53.6113 | 14.073 | 3.809 | 0.002 | 22.949 84.274 |
| expression | 68.4971 | 36.872 | 1.858 | 0.088 | -11.840 148.835 |
| Omnibus: | 1.928 | Durbin-Watson: | 0.730 |
| Prob(Omnibus): | 0.381 | Jarque-Bera (JB): | 0.987 |
| Skew: | -0.187 | Prob(JB): | 0.611 |
| Kurtosis: | 1.800 | Cond. No. | 44.8 |
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: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 09:57:46 | 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: | 0.054 |
| Model: | OLS | Adj. R-squared: | -0.019 |
| Method: | Least Squares | F-statistic: | 0.7443 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.404 |
| Time: | 09:57:46 | Log-Likelihood: | -74.882 |
| 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 | -25.7561 | 138.773 | -0.186 | 0.856 | -325.557 274.045 |
| expression | 44.7769 | 51.900 | 0.863 | 0.404 | -67.346 156.900 |
| Omnibus: | 1.237 | Durbin-Watson: | 1.632 |
| Prob(Omnibus): | 0.539 | Jarque-Bera (JB): | 0.786 |
| Skew: | 0.103 | Prob(JB): | 0.675 |
| Kurtosis: | 1.897 | Cond. No. | 42.8 |