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 |
| 0.652 | 0.429 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.664 |
| Model: | OLS | Adj. R-squared: | 0.611 |
| Method: | Least Squares | F-statistic: | 12.53 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.47e-05 |
| Time: | 04:33:38 | Log-Likelihood: | -100.55 |
| No. Observations: | 23 | AIC: | 209.1 |
| Df Residuals: | 19 | BIC: | 213.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -16.1683 | 125.446 | -0.129 | 0.899 | -278.730 246.393 |
| C(dose)[T.1] | -133.5390 | 370.669 | -0.360 | 0.723 | -909.358 642.280 |
| expression | 7.2941 | 12.986 | 0.562 | 0.581 | -19.887 34.475 |
| expression:C(dose)[T.1] | 17.1664 | 35.612 | 0.482 | 0.635 | -57.369 91.702 |
| Omnibus: | 0.810 | Durbin-Watson: | 1.995 |
| Prob(Omnibus): | 0.667 | Jarque-Bera (JB): | 0.643 |
| Skew: | 0.381 | Prob(JB): | 0.725 |
| Kurtosis: | 2.697 | Cond. No. | 998. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.660 |
| Model: | OLS | Adj. R-squared: | 0.626 |
| Method: | Least Squares | F-statistic: | 19.42 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.06e-05 |
| Time: | 04:33:38 | Log-Likelihood: | -100.69 |
| No. Observations: | 23 | AIC: | 207.4 |
| Df Residuals: | 20 | BIC: | 210.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -38.1941 | 114.565 | -0.333 | 0.742 | -277.172 200.784 |
| C(dose)[T.1] | 45.0185 | 13.437 | 3.350 | 0.003 | 16.988 73.049 |
| expression | 9.5769 | 11.858 | 0.808 | 0.429 | -15.158 34.312 |
| Omnibus: | 0.436 | Durbin-Watson: | 1.943 |
| Prob(Omnibus): | 0.804 | Jarque-Bera (JB): | 0.421 |
| Skew: | 0.280 | Prob(JB): | 0.810 |
| Kurtosis: | 2.644 | Cond. No. | 272. |
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: | 04:33:38 | 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: | 0.469 |
| Model: | OLS | Adj. R-squared: | 0.444 |
| Method: | Least Squares | F-statistic: | 18.58 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000309 |
| Time: | 04:33:38 | Log-Likelihood: | -105.82 |
| No. Observations: | 23 | AIC: | 215.6 |
| Df Residuals: | 21 | BIC: | 217.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -323.1074 | 93.604 | -3.452 | 0.002 | -517.767 -128.447 |
| expression | 40.0268 | 9.286 | 4.310 | 0.000 | 20.715 59.339 |
| Omnibus: | 1.811 | Durbin-Watson: | 2.237 |
| Prob(Omnibus): | 0.404 | Jarque-Bera (JB): | 1.283 |
| Skew: | 0.571 | Prob(JB): | 0.526 |
| Kurtosis: | 2.813 | Cond. No. | 181. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.100 | 0.757 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.613 |
| Model: | OLS | Adj. R-squared: | 0.507 |
| Method: | Least Squares | F-statistic: | 5.798 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0126 |
| Time: | 04:33:38 | Log-Likelihood: | -68.188 |
| No. Observations: | 15 | AIC: | 144.4 |
| Df Residuals: | 11 | BIC: | 147.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 299.0140 | 184.106 | 1.624 | 0.133 | -106.200 704.228 |
| C(dose)[T.1] | -517.5355 | 265.763 | -1.947 | 0.077 | -1102.476 67.405 |
| expression | -28.3373 | 22.494 | -1.260 | 0.234 | -77.846 21.171 |
| expression:C(dose)[T.1] | 67.2704 | 31.631 | 2.127 | 0.057 | -2.349 136.890 |
| Omnibus: | 1.128 | Durbin-Watson: | 1.296 |
| Prob(Omnibus): | 0.569 | Jarque-Bera (JB): | 0.767 |
| Skew: | -0.521 | Prob(JB): | 0.681 |
| Kurtosis: | 2.623 | Cond. No. | 440. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.453 |
| Model: | OLS | Adj. R-squared: | 0.362 |
| Method: | Least Squares | F-statistic: | 4.975 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0267 |
| Time: | 04:33:38 | Log-Likelihood: | -70.771 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 20.9889 | 147.440 | 0.142 | 0.889 | -300.255 342.233 |
| C(dose)[T.1] | 46.7199 | 17.525 | 2.666 | 0.021 | 8.535 84.905 |
| expression | 5.6825 | 17.987 | 0.316 | 0.757 | -33.507 44.872 |
| Omnibus: | 2.111 | Durbin-Watson: | 0.882 |
| Prob(Omnibus): | 0.348 | Jarque-Bera (JB): | 1.597 |
| Skew: | -0.740 | Prob(JB): | 0.450 |
| Kurtosis: | 2.397 | Cond. No. | 162. |
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: | 04:33:38 | 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: | 0.130 |
| Model: | OLS | Adj. R-squared: | 0.063 |
| Method: | Least Squares | F-statistic: | 1.935 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.188 |
| Time: | 04:33:38 | Log-Likelihood: | -74.259 |
| No. Observations: | 15 | AIC: | 152.5 |
| Df Residuals: | 13 | BIC: | 153.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -134.3541 | 164.193 | -0.818 | 0.428 | -489.072 220.364 |
| expression | 27.1295 | 19.503 | 1.391 | 0.188 | -15.004 69.263 |
| Omnibus: | 0.820 | Durbin-Watson: | 1.488 |
| Prob(Omnibus): | 0.664 | Jarque-Bera (JB): | 0.458 |
| Skew: | -0.408 | Prob(JB): | 0.795 |
| Kurtosis: | 2.743 | Cond. No. | 148. |