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 |
| 4.941 | 0.038 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.721 |
| Model: | OLS | Adj. R-squared: | 0.677 |
| Method: | Least Squares | F-statistic: | 16.35 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.70e-05 |
| Time: | 22:55:11 | Log-Likelihood: | -98.431 |
| No. Observations: | 23 | AIC: | 204.9 |
| Df Residuals: | 19 | BIC: | 209.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 8.1547 | 56.364 | 0.145 | 0.886 | -109.817 126.127 |
| C(dose)[T.1] | 25.1429 | 67.227 | 0.374 | 0.713 | -115.565 165.850 |
| expression | 7.2015 | 8.771 | 0.821 | 0.422 | -11.156 25.560 |
| expression:C(dose)[T.1] | 4.0542 | 10.345 | 0.392 | 0.699 | -17.597 25.706 |
| Omnibus: | 1.206 | Durbin-Watson: | 1.844 |
| Prob(Omnibus): | 0.547 | Jarque-Bera (JB): | 1.116 |
| Skew: | 0.414 | Prob(JB): | 0.572 |
| Kurtosis: | 2.308 | Cond. No. | 161. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.719 |
| Model: | OLS | Adj. R-squared: | 0.690 |
| Method: | Least Squares | F-statistic: | 25.53 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 3.12e-06 |
| Time: | 22:55:11 | Log-Likelihood: | -98.524 |
| No. Observations: | 23 | AIC: | 203.0 |
| Df Residuals: | 20 | BIC: | 206.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -10.4837 | 29.605 | -0.354 | 0.727 | -72.238 51.271 |
| C(dose)[T.1] | 51.2987 | 7.907 | 6.488 | 0.000 | 34.806 67.792 |
| expression | 10.1161 | 4.551 | 2.223 | 0.038 | 0.623 19.609 |
| Omnibus: | 1.435 | Durbin-Watson: | 1.808 |
| Prob(Omnibus): | 0.488 | Jarque-Bera (JB): | 1.178 |
| Skew: | 0.365 | Prob(JB): | 0.555 |
| Kurtosis: | 2.165 | Cond. No. | 50.7 |
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: | 22:55:11 | 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: | 0.126 |
| Model: | OLS | Adj. R-squared: | 0.085 |
| Method: | Least Squares | F-statistic: | 3.035 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0961 |
| Time: | 22:55:11 | Log-Likelihood: | -111.55 |
| No. Observations: | 23 | AIC: | 227.1 |
| Df Residuals: | 21 | BIC: | 229.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -8.1787 | 50.904 | -0.161 | 0.874 | -114.039 97.682 |
| expression | 13.5405 | 7.773 | 1.742 | 0.096 | -2.624 29.705 |
| Omnibus: | 1.275 | Durbin-Watson: | 2.612 |
| Prob(Omnibus): | 0.529 | Jarque-Bera (JB): | 0.968 |
| Skew: | 0.225 | Prob(JB): | 0.616 |
| Kurtosis: | 2.101 | Cond. No. | 50.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.400 | 0.539 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.490 |
| Model: | OLS | Adj. R-squared: | 0.350 |
| Method: | Least Squares | F-statistic: | 3.518 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0525 |
| Time: | 22:55:11 | Log-Likelihood: | -70.255 |
| No. Observations: | 15 | AIC: | 148.5 |
| Df Residuals: | 11 | BIC: | 151.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 15.2965 | 57.011 | 0.268 | 0.793 | -110.185 140.778 |
| C(dose)[T.1] | 106.6502 | 79.184 | 1.347 | 0.205 | -67.634 280.934 |
| expression | 8.3792 | 8.973 | 0.934 | 0.370 | -11.371 28.130 |
| expression:C(dose)[T.1] | -9.3400 | 13.234 | -0.706 | 0.495 | -38.469 19.789 |
| Omnibus: | 3.064 | Durbin-Watson: | 0.946 |
| Prob(Omnibus): | 0.216 | Jarque-Bera (JB): | 2.013 |
| Skew: | -0.887 | Prob(JB): | 0.365 |
| Kurtosis: | 2.734 | Cond. No. | 80.5 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.467 |
| Model: | OLS | Adj. R-squared: | 0.378 |
| Method: | Least Squares | F-statistic: | 5.248 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0230 |
| Time: | 22:55:11 | Log-Likelihood: | -70.587 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 42.0113 | 41.730 | 1.007 | 0.334 | -48.910 132.932 |
| C(dose)[T.1] | 51.9854 | 16.099 | 3.229 | 0.007 | 16.910 87.061 |
| expression | 4.0853 | 6.456 | 0.633 | 0.539 | -9.982 18.152 |
| Omnibus: | 1.935 | Durbin-Watson: | 0.980 |
| Prob(Omnibus): | 0.380 | Jarque-Bera (JB): | 1.257 |
| Skew: | -0.691 | Prob(JB): | 0.533 |
| Kurtosis: | 2.680 | Cond. No. | 33.8 |
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: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:55:11 | 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: | 0.003 |
| Model: | OLS | Adj. R-squared: | -0.074 |
| Method: | Least Squares | F-statistic: | 0.03959 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.845 |
| Time: | 22:55:11 | Log-Likelihood: | -75.277 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 103.1723 | 48.840 | 2.112 | 0.055 | -2.340 208.684 |
| expression | -1.6228 | 8.156 | -0.199 | 0.845 | -19.243 15.997 |
| Omnibus: | 0.610 | Durbin-Watson: | 1.554 |
| Prob(Omnibus): | 0.737 | Jarque-Bera (JB): | 0.582 |
| Skew: | -0.021 | Prob(JB): | 0.748 |
| Kurtosis: | 2.036 | Cond. No. | 29.6 |