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 |
| 4.821 | 0.040 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.719 |
| Model: | OLS | Adj. R-squared: | 0.675 |
| Method: | Least Squares | F-statistic: | 16.24 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.78e-05 |
| Time: | 04:48:22 | Log-Likelihood: | -98.491 |
| No. Observations: | 23 | AIC: | 205.0 |
| Df Residuals: | 19 | BIC: | 209.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 222.6863 | 84.655 | 2.631 | 0.016 | 45.502 399.871 |
| C(dose)[T.1] | 2.9743 | 158.176 | 0.019 | 0.985 | -328.091 334.040 |
| expression | -20.6669 | 10.362 | -1.994 | 0.061 | -42.355 1.021 |
| expression:C(dose)[T.1] | 7.0686 | 18.535 | 0.381 | 0.707 | -31.726 45.863 |
| Omnibus: | 0.647 | Durbin-Watson: | 1.812 |
| Prob(Omnibus): | 0.724 | Jarque-Bera (JB): | 0.623 |
| Skew: | -0.344 | Prob(JB): | 0.732 |
| Kurtosis: | 2.579 | Cond. No. | 405. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.717 |
| Model: | OLS | Adj. R-squared: | 0.689 |
| Method: | Least Squares | F-statistic: | 25.36 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.27e-06 |
| Time: | 04:48:22 | Log-Likelihood: | -98.579 |
| No. Observations: | 23 | AIC: | 203.2 |
| Df Residuals: | 20 | BIC: | 206.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 204.6771 | 68.742 | 2.977 | 0.007 | 61.284 348.070 |
| C(dose)[T.1] | 63.1929 | 9.062 | 6.974 | 0.000 | 44.290 82.096 |
| expression | -18.4577 | 8.406 | -2.196 | 0.040 | -35.992 -0.923 |
| Omnibus: | 0.715 | Durbin-Watson: | 1.804 |
| Prob(Omnibus): | 0.699 | Jarque-Bera (JB): | 0.669 |
| Skew: | -0.362 | Prob(JB): | 0.716 |
| Kurtosis: | 2.585 | Cond. No. | 150. |
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: | 04:48:22 | 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: | 0.030 |
| Model: | OLS | Adj. R-squared: | -0.017 |
| Method: | Least Squares | F-statistic: | 0.6420 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.432 |
| Time: | 04:48:22 | Log-Likelihood: | -112.76 |
| No. Observations: | 23 | AIC: | 229.5 |
| Df Residuals: | 21 | BIC: | 231.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -9.2115 | 111.215 | -0.083 | 0.935 | -240.496 222.073 |
| expression | 10.5774 | 13.201 | 0.801 | 0.432 | -16.876 38.031 |
| Omnibus: | 3.847 | Durbin-Watson: | 2.367 |
| Prob(Omnibus): | 0.146 | Jarque-Bera (JB): | 1.632 |
| Skew: | 0.264 | Prob(JB): | 0.442 |
| Kurtosis: | 1.806 | Cond. No. | 134. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.163 | 0.693 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.486 |
| Model: | OLS | Adj. R-squared: | 0.346 |
| Method: | Least Squares | F-statistic: | 3.469 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0544 |
| Time: | 04:48:22 | Log-Likelihood: | -70.306 |
| No. Observations: | 15 | AIC: | 148.6 |
| Df Residuals: | 11 | BIC: | 151.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -159.3038 | 255.127 | -0.624 | 0.545 | -720.834 402.227 |
| C(dose)[T.1] | 291.7185 | 302.668 | 0.964 | 0.356 | -374.450 957.887 |
| expression | 30.0371 | 33.764 | 0.890 | 0.393 | -44.277 104.351 |
| expression:C(dose)[T.1] | -32.1379 | 40.092 | -0.802 | 0.440 | -120.379 56.103 |
| Omnibus: | 2.546 | Durbin-Watson: | 0.742 |
| Prob(Omnibus): | 0.280 | Jarque-Bera (JB): | 1.660 |
| Skew: | -0.802 | Prob(JB): | 0.436 |
| Kurtosis: | 2.709 | Cond. No. | 428. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.456 |
| Model: | OLS | Adj. R-squared: | 0.366 |
| Method: | Least Squares | F-statistic: | 5.033 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0259 |
| Time: | 04:48:22 | Log-Likelihood: | -70.732 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 12.7521 | 135.845 | 0.094 | 0.927 | -283.229 308.733 |
| C(dose)[T.1] | 49.4305 | 15.644 | 3.160 | 0.008 | 15.344 83.517 |
| expression | 7.2435 | 17.933 | 0.404 | 0.693 | -31.829 46.316 |
| Omnibus: | 2.288 | Durbin-Watson: | 0.755 |
| Prob(Omnibus): | 0.318 | Jarque-Bera (JB): | 1.733 |
| Skew: | -0.772 | Prob(JB): | 0.420 |
| Kurtosis: | 2.377 | Cond. No. | 134. |
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: | 04:48:22 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.073 |
| Method: | Least Squares | F-statistic: | 0.04874 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.829 |
| Time: | 04:48:22 | Log-Likelihood: | -75.272 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 54.9192 | 175.797 | 0.312 | 0.760 | -324.867 434.706 |
| expression | 5.1449 | 23.304 | 0.221 | 0.829 | -45.200 55.490 |
| Omnibus: | 0.819 | Durbin-Watson: | 1.589 |
| Prob(Omnibus): | 0.664 | Jarque-Bera (JB): | 0.665 |
| Skew: | 0.105 | Prob(JB): | 0.717 |
| Kurtosis: | 1.990 | Cond. No. | 133. |