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 |
| 0.098 | 0.758 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.596 |
| Method: | Least Squares | F-statistic: | 11.82 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.000135 |
| Time: | 22:20:53 | Log-Likelihood: | -101.00 |
| No. Observations: | 23 | AIC: | 210.0 |
| Df Residuals: | 19 | BIC: | 214.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 78.4747 | 84.826 | 0.925 | 0.367 | -99.067 256.017 |
| C(dose)[T.1] | 38.3292 | 101.190 | 0.379 | 0.709 | -173.465 250.123 |
| expression | -3.9879 | 13.903 | -0.287 | 0.777 | -33.087 25.111 |
| expression:C(dose)[T.1] | 2.2982 | 17.125 | 0.134 | 0.895 | -33.546 38.142 |
| Omnibus: | 0.407 | Durbin-Watson: | 1.867 |
| Prob(Omnibus): | 0.816 | Jarque-Bera (JB): | 0.548 |
| Skew: | 0.169 | Prob(JB): | 0.760 |
| Kurtosis: | 2.323 | Cond. No. | 188. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.63 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 2.70e-05 |
| Time: | 22:20:53 | Log-Likelihood: | -101.01 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 69.2582 | 48.548 | 1.427 | 0.169 | -32.011 170.528 |
| C(dose)[T.1] | 51.8392 | 9.976 | 5.196 | 0.000 | 31.029 72.649 |
| expression | -2.4733 | 7.916 | -0.312 | 0.758 | -18.986 14.039 |
| Omnibus: | 0.582 | Durbin-Watson: | 1.873 |
| Prob(Omnibus): | 0.748 | Jarque-Bera (JB): | 0.648 |
| Skew: | 0.171 | Prob(JB): | 0.723 |
| Kurtosis: | 2.253 | Cond. No. | 67.2 |
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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:20:53 | 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: | 0.179 |
| Model: | OLS | Adj. R-squared: | 0.140 |
| Method: | Least Squares | F-statistic: | 4.586 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0441 |
| Time: | 22:20:53 | Log-Likelihood: | -110.83 |
| No. Observations: | 23 | AIC: | 225.7 |
| Df Residuals: | 21 | BIC: | 227.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 208.6179 | 60.543 | 3.446 | 0.002 | 82.713 334.523 |
| expression | -22.2420 | 10.386 | -2.142 | 0.044 | -43.840 -0.644 |
| Omnibus: | 2.393 | Durbin-Watson: | 2.223 |
| Prob(Omnibus): | 0.302 | Jarque-Bera (JB): | 1.964 |
| Skew: | 0.599 | Prob(JB): | 0.375 |
| Kurtosis: | 2.216 | Cond. No. | 55.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.370 | 0.555 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.555 |
| Model: | OLS | Adj. R-squared: | 0.433 |
| Method: | Least Squares | F-statistic: | 4.569 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0260 |
| Time: | 22:20:53 | Log-Likelihood: | -69.231 |
| No. Observations: | 15 | AIC: | 146.5 |
| Df Residuals: | 11 | BIC: | 149.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 95.1886 | 64.313 | 1.480 | 0.167 | -46.364 236.741 |
| C(dose)[T.1] | -109.3031 | 106.211 | -1.029 | 0.326 | -343.072 124.465 |
| expression | -7.3680 | 16.828 | -0.438 | 0.670 | -44.406 29.670 |
| expression:C(dose)[T.1] | 39.8386 | 26.788 | 1.487 | 0.165 | -19.121 98.798 |
| Omnibus: | 1.015 | Durbin-Watson: | 0.878 |
| Prob(Omnibus): | 0.602 | Jarque-Bera (JB): | 0.797 |
| Skew: | -0.505 | Prob(JB): | 0.671 |
| Kurtosis: | 2.493 | Cond. No. | 76.9 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.465 |
| Model: | OLS | Adj. R-squared: | 0.376 |
| Method: | Least Squares | F-statistic: | 5.220 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0234 |
| Time: | 22:20:53 | Log-Likelihood: | -70.605 |
| 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 | 35.9566 | 52.985 | 0.679 | 0.510 | -79.487 151.400 |
| C(dose)[T.1] | 47.0351 | 15.905 | 2.957 | 0.012 | 12.381 81.689 |
| expression | 8.3532 | 13.738 | 0.608 | 0.555 | -21.580 38.286 |
| Omnibus: | 2.512 | Durbin-Watson: | 0.817 |
| Prob(Omnibus): | 0.285 | Jarque-Bera (JB): | 1.868 |
| Skew: | -0.816 | Prob(JB): | 0.393 |
| Kurtosis: | 2.428 | Cond. No. | 29.0 |
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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:20:53 | 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: | 0.076 |
| Model: | OLS | Adj. R-squared: | 0.004 |
| Method: | Least Squares | F-statistic: | 1.062 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.322 |
| Time: | 22:20:54 | Log-Likelihood: | -74.711 |
| No. Observations: | 15 | AIC: | 153.4 |
| Df Residuals: | 13 | BIC: | 154.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 25.5779 | 66.786 | 0.383 | 0.708 | -118.704 169.860 |
| expression | 17.4334 | 16.916 | 1.031 | 0.322 | -19.111 53.978 |
| Omnibus: | 0.280 | Durbin-Watson: | 1.608 |
| Prob(Omnibus): | 0.870 | Jarque-Bera (JB): | 0.390 |
| Skew: | -0.255 | Prob(JB): | 0.823 |
| Kurtosis: | 2.397 | Cond. No. | 28.7 |