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 |
| 0.240 | 0.630 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.605 |
| Method: | Least Squares | F-statistic: | 12.24 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000109 |
| Time: | 04:58:56 | Log-Likelihood: | -100.73 |
| No. Observations: | 23 | AIC: | 209.5 |
| Df Residuals: | 19 | BIC: | 214.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 97.9258 | 59.878 | 1.635 | 0.118 | -27.401 223.252 |
| C(dose)[T.1] | 13.8533 | 70.403 | 0.197 | 0.846 | -133.502 161.208 |
| expression | -12.3474 | 16.823 | -0.734 | 0.472 | -47.558 22.863 |
| expression:C(dose)[T.1] | 11.1576 | 19.699 | 0.566 | 0.578 | -30.073 52.388 |
| Omnibus: | 0.126 | Durbin-Watson: | 1.715 |
| Prob(Omnibus): | 0.939 | Jarque-Bera (JB): | 0.345 |
| Skew: | -0.054 | Prob(JB): | 0.841 |
| Kurtosis: | 2.409 | Cond. No. | 88.1 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 18.84 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.52e-05 |
| Time: | 04:58:56 | Log-Likelihood: | -100.93 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 69.1153 | 31.051 | 2.226 | 0.038 | 4.345 133.886 |
| C(dose)[T.1] | 53.4117 | 8.719 | 6.126 | 0.000 | 35.224 71.600 |
| expression | -4.2103 | 8.603 | -0.489 | 0.630 | -22.156 13.735 |
| Omnibus: | 0.022 | Durbin-Watson: | 1.841 |
| Prob(Omnibus): | 0.989 | Jarque-Bera (JB): | 0.165 |
| Skew: | -0.063 | Prob(JB): | 0.921 |
| Kurtosis: | 2.604 | Cond. No. | 27.7 |
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, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 04:58:56 | 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: | 0.003 |
| Model: | OLS | Adj. R-squared: | -0.045 |
| Method: | Least Squares | F-statistic: | 0.05338 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.820 |
| Time: | 04:58:56 | Log-Likelihood: | -113.08 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 91.3909 | 51.038 | 1.791 | 0.088 | -14.748 197.530 |
| expression | -3.2891 | 14.236 | -0.231 | 0.820 | -32.895 26.317 |
| Omnibus: | 3.581 | Durbin-Watson: | 2.466 |
| Prob(Omnibus): | 0.167 | Jarque-Bera (JB): | 1.613 |
| Skew: | 0.283 | Prob(JB): | 0.446 |
| Kurtosis: | 1.832 | Cond. No. | 27.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.365 | 0.557 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.474 |
| Model: | OLS | Adj. R-squared: | 0.330 |
| Method: | Least Squares | F-statistic: | 3.303 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0613 |
| Time: | 04:58:56 | Log-Likelihood: | -70.483 |
| No. Observations: | 15 | AIC: | 149.0 |
| Df Residuals: | 11 | BIC: | 151.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 67.0521 | 86.030 | 0.779 | 0.452 | -122.298 256.403 |
| C(dose)[T.1] | 0.6690 | 109.883 | 0.006 | 0.995 | -241.181 242.520 |
| expression | 0.0926 | 20.976 | 0.004 | 0.997 | -46.074 46.260 |
| expression:C(dose)[T.1] | 11.2740 | 26.190 | 0.430 | 0.675 | -46.370 68.918 |
| Omnibus: | 1.729 | Durbin-Watson: | 0.986 |
| Prob(Omnibus): | 0.421 | Jarque-Bera (JB): | 1.374 |
| Skew: | -0.644 | Prob(JB): | 0.503 |
| Kurtosis: | 2.266 | Cond. No. | 87.0 |
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: | 5.216 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0234 |
| Time: | 04:58:56 | Log-Likelihood: | -70.608 |
| 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 | 37.6695 | 50.556 | 0.745 | 0.471 | -72.483 147.822 |
| C(dose)[T.1] | 47.4442 | 15.775 | 3.008 | 0.011 | 13.074 81.815 |
| expression | 7.3242 | 12.126 | 0.604 | 0.557 | -19.097 33.745 |
| Omnibus: | 2.080 | Durbin-Watson: | 0.869 |
| Prob(Omnibus): | 0.353 | Jarque-Bera (JB): | 1.600 |
| Skew: | -0.729 | Prob(JB): | 0.449 |
| Kurtosis: | 2.341 | Cond. No. | 29.5 |
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: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 04:58:56 | 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: | 0.062 |
| Model: | OLS | Adj. R-squared: | -0.010 |
| Method: | Least Squares | F-statistic: | 0.8560 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.372 |
| Time: | 04:58:56 | Log-Likelihood: | -74.822 |
| No. Observations: | 15 | AIC: | 153.6 |
| Df Residuals: | 13 | BIC: | 155.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 34.8639 | 64.314 | 0.542 | 0.597 | -104.079 173.807 |
| expression | 14.0316 | 15.166 | 0.925 | 0.372 | -18.733 46.796 |
| Omnibus: | 0.173 | Durbin-Watson: | 1.610 |
| Prob(Omnibus): | 0.917 | Jarque-Bera (JB): | 0.378 |
| Skew: | 0.069 | Prob(JB): | 0.828 |
| Kurtosis: | 2.234 | Cond. No. | 29.2 |