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 |
| 1.778 | 0.197 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.690 |
| Model: | OLS | Adj. R-squared: | 0.641 |
| Method: | Least Squares | F-statistic: | 14.07 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 4.56e-05 |
| Time: | 23:02:19 | Log-Likelihood: | -99.649 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 19 | BIC: | 211.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 109.1140 | 100.749 | 1.083 | 0.292 | -101.757 319.985 |
| C(dose)[T.1] | 229.6522 | 186.099 | 1.234 | 0.232 | -159.857 619.162 |
| expression | -7.9879 | 14.633 | -0.546 | 0.591 | -38.614 22.639 |
| expression:C(dose)[T.1] | -20.8462 | 24.377 | -0.855 | 0.403 | -71.869 30.176 |
| Omnibus: | 0.219 | Durbin-Watson: | 1.640 |
| Prob(Omnibus): | 0.896 | Jarque-Bera (JB): | 0.390 |
| Skew: | -0.172 | Prob(JB): | 0.823 |
| Kurtosis: | 2.462 | Cond. No. | 412. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.678 |
| Model: | OLS | Adj. R-squared: | 0.645 |
| Method: | Least Squares | F-statistic: | 21.03 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.21e-05 |
| Time: | 23:02:19 | Log-Likelihood: | -100.08 |
| No. Observations: | 23 | AIC: | 206.2 |
| Df Residuals: | 20 | BIC: | 209.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 160.7417 | 80.113 | 2.006 | 0.059 | -6.371 327.855 |
| C(dose)[T.1] | 71.0894 | 15.745 | 4.515 | 0.000 | 38.246 103.933 |
| expression | -15.4989 | 11.624 | -1.333 | 0.197 | -39.747 8.749 |
| Omnibus: | 0.434 | Durbin-Watson: | 1.856 |
| Prob(Omnibus): | 0.805 | Jarque-Bera (JB): | 0.568 |
| Skew: | -0.195 | Prob(JB): | 0.753 |
| Kurtosis: | 2.337 | Cond. No. | 147. |
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: | 23:02:19 | 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: | 0.349 |
| Model: | OLS | Adj. R-squared: | 0.318 |
| Method: | Least Squares | F-statistic: | 11.27 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00299 |
| Time: | 23:02:20 | Log-Likelihood: | -108.16 |
| No. Observations: | 23 | AIC: | 220.3 |
| Df Residuals: | 21 | BIC: | 222.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -134.6393 | 64.123 | -2.100 | 0.048 | -267.990 -1.288 |
| expression | 28.8835 | 8.605 | 3.357 | 0.003 | 10.989 46.778 |
| Omnibus: | 4.269 | Durbin-Watson: | 2.036 |
| Prob(Omnibus): | 0.118 | Jarque-Bera (JB): | 1.987 |
| Skew: | 0.408 | Prob(JB): | 0.370 |
| Kurtosis: | 1.814 | Cond. No. | 83.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.012 | 0.915 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.506 |
| Model: | OLS | Adj. R-squared: | 0.371 |
| Method: | Least Squares | F-statistic: | 3.750 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0446 |
| Time: | 23:02:20 | Log-Likelihood: | -70.017 |
| No. Observations: | 15 | AIC: | 148.0 |
| Df Residuals: | 11 | BIC: | 150.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 112.8484 | 91.422 | 1.234 | 0.243 | -88.370 314.067 |
| C(dose)[T.1] | -146.1596 | 175.061 | -0.835 | 0.422 | -531.467 239.148 |
| expression | -8.5806 | 17.137 | -0.501 | 0.626 | -46.299 29.138 |
| expression:C(dose)[T.1] | 36.1549 | 32.306 | 1.119 | 0.287 | -34.951 107.260 |
| Omnibus: | 1.370 | Durbin-Watson: | 0.885 |
| Prob(Omnibus): | 0.504 | Jarque-Bera (JB): | 1.100 |
| Skew: | -0.586 | Prob(JB): | 0.577 |
| Kurtosis: | 2.377 | Cond. No. | 153. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.895 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0279 |
| Time: | 23:02:20 | Log-Likelihood: | -70.826 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 58.9970 | 78.547 | 0.751 | 0.467 | -112.143 230.137 |
| C(dose)[T.1] | 48.9667 | 15.874 | 3.085 | 0.009 | 14.381 83.553 |
| expression | 1.5929 | 14.679 | 0.109 | 0.915 | -30.391 33.576 |
| Omnibus: | 2.714 | Durbin-Watson: | 0.804 |
| Prob(Omnibus): | 0.257 | Jarque-Bera (JB): | 1.885 |
| Skew: | -0.845 | Prob(JB): | 0.390 |
| Kurtosis: | 2.602 | Cond. No. | 56.1 |
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: | 23:02:20 | 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: | 0.013 |
| Model: | OLS | Adj. R-squared: | -0.063 |
| Method: | Least Squares | F-statistic: | 0.1662 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.690 |
| Time: | 23:02:20 | Log-Likelihood: | -75.205 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 52.6849 | 101.016 | 0.522 | 0.611 | -165.547 270.917 |
| expression | 7.6313 | 18.716 | 0.408 | 0.690 | -32.803 48.065 |
| Omnibus: | 0.146 | Durbin-Watson: | 1.502 |
| Prob(Omnibus): | 0.929 | Jarque-Bera (JB): | 0.360 |
| Skew: | -0.065 | Prob(JB): | 0.835 |
| Kurtosis: | 2.253 | Cond. No. | 55.8 |