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 |
| 3.873 | 0.063 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.706 |
| Model: | OLS | Adj. R-squared: | 0.660 |
| Method: | Least Squares | F-statistic: | 15.23 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 2.73e-05 |
| Time: | 19:31:00 | Log-Likelihood: | -99.016 |
| No. Observations: | 23 | AIC: | 206.0 |
| Df Residuals: | 19 | BIC: | 210.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -42.1928 | 62.938 | -0.670 | 0.511 | -173.923 89.537 |
| C(dose)[T.1] | 33.1847 | 119.041 | 0.279 | 0.783 | -215.972 282.341 |
| expression | 12.7386 | 8.283 | 1.538 | 0.141 | -4.597 30.074 |
| expression:C(dose)[T.1] | 2.0546 | 15.248 | 0.135 | 0.894 | -29.860 33.969 |
| Omnibus: | 0.964 | Durbin-Watson: | 1.821 |
| Prob(Omnibus): | 0.618 | Jarque-Bera (JB): | 0.780 |
| Skew: | -0.420 | Prob(JB): | 0.677 |
| Kurtosis: | 2.671 | Cond. No. | 272. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.706 |
| Model: | OLS | Adj. R-squared: | 0.677 |
| Method: | Least Squares | F-statistic: | 24.01 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 4.83e-06 |
| Time: | 19:31:00 | Log-Likelihood: | -99.027 |
| No. Observations: | 23 | AIC: | 204.1 |
| Df Residuals: | 20 | BIC: | 207.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -46.7804 | 51.618 | -0.906 | 0.376 | -154.453 60.892 |
| C(dose)[T.1] | 49.1838 | 8.300 | 5.926 | 0.000 | 31.870 66.497 |
| expression | 13.3449 | 6.781 | 1.968 | 0.063 | -0.801 27.490 |
| Omnibus: | 0.967 | Durbin-Watson: | 1.836 |
| Prob(Omnibus): | 0.617 | Jarque-Bera (JB): | 0.840 |
| Skew: | -0.424 | Prob(JB): | 0.657 |
| Kurtosis: | 2.602 | Cond. No. | 102. |
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, 28 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 19:31:00 | 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: | 0.190 |
| Model: | OLS | Adj. R-squared: | 0.151 |
| Method: | Least Squares | F-statistic: | 4.918 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0377 |
| Time: | 19:31:00 | Log-Likelihood: | -110.68 |
| No. Observations: | 23 | AIC: | 225.4 |
| Df Residuals: | 21 | BIC: | 227.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -102.1086 | 82.244 | -1.242 | 0.228 | -273.143 68.926 |
| expression | 23.5634 | 10.625 | 2.218 | 0.038 | 1.468 45.659 |
| Omnibus: | 0.943 | Durbin-Watson: | 2.453 |
| Prob(Omnibus): | 0.624 | Jarque-Bera (JB): | 0.792 |
| Skew: | 0.141 | Prob(JB): | 0.673 |
| Kurtosis: | 2.136 | Cond. No. | 99.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.504 | 0.086 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.683 |
| Model: | OLS | Adj. R-squared: | 0.596 |
| Method: | Least Squares | F-statistic: | 7.890 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00437 |
| Time: | 19:31:00 | Log-Likelihood: | -66.690 |
| No. Observations: | 15 | AIC: | 141.4 |
| Df Residuals: | 11 | BIC: | 144.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 49.9537 | 52.577 | 0.950 | 0.362 | -65.768 165.675 |
| C(dose)[T.1] | -121.9561 | 85.356 | -1.429 | 0.181 | -309.823 65.911 |
| expression | 2.7218 | 8.065 | 0.337 | 0.742 | -15.030 20.473 |
| expression:C(dose)[T.1] | 24.4052 | 12.532 | 1.947 | 0.077 | -3.178 51.988 |
| Omnibus: | 1.666 | Durbin-Watson: | 0.549 |
| Prob(Omnibus): | 0.435 | Jarque-Bera (JB): | 1.263 |
| Skew: | -0.654 | Prob(JB): | 0.532 |
| Kurtosis: | 2.445 | Cond. No. | 122. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.573 |
| Model: | OLS | Adj. R-squared: | 0.502 |
| Method: | Least Squares | F-statistic: | 8.063 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00603 |
| Time: | 19:31:00 | Log-Likelihood: | -68.912 |
| No. Observations: | 15 | AIC: | 143.8 |
| Df Residuals: | 12 | BIC: | 145.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -14.9435 | 45.151 | -0.331 | 0.746 | -113.320 83.433 |
| C(dose)[T.1] | 42.3572 | 14.321 | 2.958 | 0.012 | 11.154 73.560 |
| expression | 12.8297 | 6.854 | 1.872 | 0.086 | -2.103 27.763 |
| Omnibus: | 6.341 | Durbin-Watson: | 0.715 |
| Prob(Omnibus): | 0.042 | Jarque-Bera (JB): | 1.569 |
| Skew: | -0.137 | Prob(JB): | 0.456 |
| Kurtosis: | 1.439 | Cond. No. | 45.5 |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 19:31:00 | 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: | 0.262 |
| Model: | OLS | Adj. R-squared: | 0.206 |
| Method: | Least Squares | F-statistic: | 4.623 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0509 |
| Time: | 19:31:00 | Log-Likelihood: | -73.018 |
| No. Observations: | 15 | AIC: | 150.0 |
| Df Residuals: | 13 | BIC: | 151.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -27.0272 | 56.807 | -0.476 | 0.642 | -149.750 95.696 |
| expression | 18.0013 | 8.372 | 2.150 | 0.051 | -0.085 36.088 |
| Omnibus: | 2.637 | Durbin-Watson: | 1.631 |
| Prob(Omnibus): | 0.267 | Jarque-Bera (JB): | 1.149 |
| Skew: | -0.221 | Prob(JB): | 0.563 |
| Kurtosis: | 1.718 | Cond. No. | 45.1 |