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 |
| 0.017 | 0.898 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.671 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 12.93 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 7.78e-05 |
| Time: | 11:47:40 | Log-Likelihood: | -100.31 |
| No. Observations: | 23 | AIC: | 208.6 |
| Df Residuals: | 19 | BIC: | 213.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -11.5791 | 76.227 | -0.152 | 0.881 | -171.125 147.967 |
| C(dose)[T.1] | 157.5449 | 92.811 | 1.697 | 0.106 | -36.712 351.802 |
| expression | 9.5801 | 11.066 | 0.866 | 0.397 | -13.581 32.741 |
| expression:C(dose)[T.1] | -14.9478 | 13.281 | -1.125 | 0.274 | -42.746 12.850 |
| Omnibus: | 0.757 | Durbin-Watson: | 1.581 |
| Prob(Omnibus): | 0.685 | Jarque-Bera (JB): | 0.694 |
| Skew: | -0.067 | Prob(JB): | 0.707 |
| Kurtosis: | 2.160 | Cond. No. | 217. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.52 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 2.81e-05 |
| Time: | 11:47:40 | Log-Likelihood: | -101.05 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 59.6766 | 42.735 | 1.396 | 0.178 | -29.466 148.820 |
| C(dose)[T.1] | 53.5685 | 8.947 | 5.987 | 0.000 | 34.905 72.232 |
| expression | -0.7963 | 6.160 | -0.129 | 0.898 | -13.646 12.054 |
| Omnibus: | 0.391 | Durbin-Watson: | 1.893 |
| Prob(Omnibus): | 0.823 | Jarque-Bera (JB): | 0.523 |
| Skew: | 0.050 | Prob(JB): | 0.770 |
| Kurtosis: | 2.268 | Cond. No. | 70.3 |
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, 03 Dec 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 11:47:40 | 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: | 0.021 |
| Model: | OLS | Adj. R-squared: | -0.026 |
| Method: | Least Squares | F-statistic: | 0.4472 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.511 |
| Time: | 11:47:40 | Log-Likelihood: | -112.86 |
| No. Observations: | 23 | AIC: | 229.7 |
| Df Residuals: | 21 | BIC: | 232.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 33.6042 | 69.328 | 0.485 | 0.633 | -110.570 177.779 |
| expression | 6.5819 | 9.843 | 0.669 | 0.511 | -13.887 27.051 |
| Omnibus: | 1.593 | Durbin-Watson: | 2.459 |
| Prob(Omnibus): | 0.451 | Jarque-Bera (JB): | 1.353 |
| Skew: | 0.448 | Prob(JB): | 0.508 |
| Kurtosis: | 2.220 | Cond. No. | 69.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.056 | 0.817 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.477 |
| Model: | OLS | Adj. R-squared: | 0.334 |
| Method: | Least Squares | F-statistic: | 3.339 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0597 |
| Time: | 11:47:40 | Log-Likelihood: | -70.444 |
| No. Observations: | 15 | AIC: | 148.9 |
| Df Residuals: | 11 | BIC: | 151.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 4.3333 | 141.252 | 0.031 | 0.976 | -306.560 315.227 |
| C(dose)[T.1] | 174.1140 | 173.262 | 1.005 | 0.337 | -207.234 555.462 |
| expression | 9.5302 | 21.262 | 0.448 | 0.663 | -37.267 56.327 |
| expression:C(dose)[T.1] | -19.2707 | 26.439 | -0.729 | 0.481 | -77.463 38.921 |
| Omnibus: | 2.348 | Durbin-Watson: | 0.878 |
| Prob(Omnibus): | 0.309 | Jarque-Bera (JB): | 1.801 |
| Skew: | -0.772 | Prob(JB): | 0.406 |
| Kurtosis: | 2.293 | Cond. No. | 205. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 4.936 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0273 |
| Time: | 11:47:40 | Log-Likelihood: | -70.798 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 86.8440 | 82.816 | 1.049 | 0.315 | -93.596 267.284 |
| C(dose)[T.1] | 48.3938 | 16.065 | 3.012 | 0.011 | 13.391 83.396 |
| expression | -2.9326 | 12.388 | -0.237 | 0.817 | -29.924 24.059 |
| Omnibus: | 2.221 | Durbin-Watson: | 0.846 |
| Prob(Omnibus): | 0.329 | Jarque-Bera (JB): | 1.665 |
| Skew: | -0.762 | Prob(JB): | 0.435 |
| Kurtosis: | 2.416 | Cond. No. | 70.7 |
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, 03 Dec 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 11:47:40 | 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: | 0.036 |
| Model: | OLS | Adj. R-squared: | -0.038 |
| Method: | Least Squares | F-statistic: | 0.4915 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.496 |
| Time: | 11:47:40 | Log-Likelihood: | -75.022 |
| No. Observations: | 15 | AIC: | 154.0 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 163.6489 | 100.322 | 1.631 | 0.127 | -53.083 380.381 |
| expression | -10.8087 | 15.418 | -0.701 | 0.496 | -44.117 22.500 |
| Omnibus: | 0.680 | Durbin-Watson: | 1.549 |
| Prob(Omnibus): | 0.712 | Jarque-Bera (JB): | 0.607 |
| Skew: | 0.025 | Prob(JB): | 0.738 |
| Kurtosis: | 2.016 | Cond. No. | 67.0 |