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 |
| 1.834 | 0.191 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.679 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 13.39 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.26e-05 |
| Time: | 04:58:03 | Log-Likelihood: | -100.04 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 19 | BIC: | 212.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 108.2508 | 62.796 | 1.724 | 0.101 | -23.182 239.684 |
| C(dose)[T.1] | 63.5084 | 89.600 | 0.709 | 0.487 | -124.027 251.044 |
| expression | -10.4035 | 12.034 | -0.864 | 0.398 | -35.591 14.784 |
| expression:C(dose)[T.1] | -2.6030 | 17.630 | -0.148 | 0.884 | -39.503 34.297 |
| Omnibus: | 0.734 | Durbin-Watson: | 2.352 |
| Prob(Omnibus): | 0.693 | Jarque-Bera (JB): | 0.754 |
| Skew: | 0.239 | Prob(JB): | 0.686 |
| Kurtosis: | 2.253 | Cond. No. | 141. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.679 |
| Model: | OLS | Adj. R-squared: | 0.646 |
| Method: | Least Squares | F-statistic: | 21.11 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.18e-05 |
| Time: | 04:58:03 | Log-Likelihood: | -100.05 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 20 | BIC: | 209.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 114.5508 | 44.930 | 2.550 | 0.019 | 20.829 208.273 |
| C(dose)[T.1] | 50.3450 | 8.679 | 5.801 | 0.000 | 32.240 68.450 |
| expression | -11.6162 | 8.577 | -1.354 | 0.191 | -29.507 6.275 |
| Omnibus: | 0.639 | Durbin-Watson: | 2.381 |
| Prob(Omnibus): | 0.727 | Jarque-Bera (JB): | 0.698 |
| Skew: | 0.226 | Prob(JB): | 0.705 |
| Kurtosis: | 2.276 | Cond. No. | 57.0 |
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: | 04:58:03 | 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: | 0.138 |
| Model: | OLS | Adj. R-squared: | 0.097 |
| Method: | Least Squares | F-statistic: | 3.354 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0813 |
| Time: | 04:58:03 | Log-Likelihood: | -111.40 |
| No. Observations: | 23 | AIC: | 226.8 |
| Df Residuals: | 21 | BIC: | 229.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 202.8506 | 67.565 | 3.002 | 0.007 | 62.341 343.360 |
| expression | -24.2795 | 13.257 | -1.831 | 0.081 | -51.849 3.290 |
| Omnibus: | 1.983 | Durbin-Watson: | 2.861 |
| Prob(Omnibus): | 0.371 | Jarque-Bera (JB): | 1.329 |
| Skew: | 0.338 | Prob(JB): | 0.515 |
| Kurtosis: | 2.035 | Cond. No. | 53.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.136 | 0.102 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.586 |
| Model: | OLS | Adj. R-squared: | 0.474 |
| Method: | Least Squares | F-statistic: | 5.200 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0177 |
| Time: | 04:58:03 | Log-Likelihood: | -68.677 |
| No. Observations: | 15 | AIC: | 145.4 |
| Df Residuals: | 11 | BIC: | 148.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 136.1724 | 70.602 | 1.929 | 0.080 | -19.221 291.566 |
| C(dose)[T.1] | 133.5640 | 117.399 | 1.138 | 0.279 | -124.829 391.957 |
| expression | -13.2602 | 13.470 | -0.984 | 0.346 | -42.907 16.387 |
| expression:C(dose)[T.1] | -18.7284 | 23.692 | -0.790 | 0.446 | -70.875 33.418 |
| Omnibus: | 1.017 | Durbin-Watson: | 0.966 |
| Prob(Omnibus): | 0.601 | Jarque-Bera (JB): | 0.463 |
| Skew: | -0.425 | Prob(JB): | 0.793 |
| Kurtosis: | 2.867 | Cond. No. | 106. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.563 |
| Model: | OLS | Adj. R-squared: | 0.490 |
| Method: | Least Squares | F-statistic: | 7.729 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00697 |
| Time: | 04:58:03 | Log-Likelihood: | -69.092 |
| No. Observations: | 15 | AIC: | 144.2 |
| Df Residuals: | 12 | BIC: | 146.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 167.5561 | 57.461 | 2.916 | 0.013 | 42.358 292.754 |
| C(dose)[T.1] | 41.5134 | 14.671 | 2.830 | 0.015 | 9.548 73.478 |
| expression | -19.3138 | 10.907 | -1.771 | 0.102 | -43.077 4.450 |
| Omnibus: | 1.203 | Durbin-Watson: | 0.995 |
| Prob(Omnibus): | 0.548 | Jarque-Bera (JB): | 0.721 |
| Skew: | -0.519 | Prob(JB): | 0.697 |
| Kurtosis: | 2.727 | Cond. No. | 43.2 |
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: | 04:58:03 | 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: | 0.271 |
| Model: | OLS | Adj. R-squared: | 0.215 |
| Method: | Least Squares | F-statistic: | 4.842 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0465 |
| Time: | 04:58:03 | Log-Likelihood: | -72.926 |
| No. Observations: | 15 | AIC: | 149.9 |
| Df Residuals: | 13 | BIC: | 151.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 235.0762 | 64.847 | 3.625 | 0.003 | 94.982 375.171 |
| expression | -28.4407 | 12.925 | -2.200 | 0.046 | -56.364 -0.518 |
| Omnibus: | 1.886 | Durbin-Watson: | 2.343 |
| Prob(Omnibus): | 0.389 | Jarque-Bera (JB): | 1.003 |
| Skew: | 0.225 | Prob(JB): | 0.605 |
| Kurtosis: | 1.816 | Cond. No. | 39.0 |