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 |
| 0.205 | 0.655 | 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: | 12.91 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.88e-05 |
| Time: | 05:22:24 | Log-Likelihood: | -100.33 |
| No. Observations: | 23 | AIC: | 208.7 |
| Df Residuals: | 19 | BIC: | 213.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 139.2268 | 236.452 | 0.589 | 0.563 | -355.672 634.126 |
| C(dose)[T.1] | -307.2041 | 351.183 | -0.875 | 0.393 | -1042.239 427.830 |
| expression | -9.2277 | 25.656 | -0.360 | 0.723 | -62.925 44.470 |
| expression:C(dose)[T.1] | 38.8463 | 37.906 | 1.025 | 0.318 | -40.492 118.185 |
| Omnibus: | 0.227 | Durbin-Watson: | 1.750 |
| Prob(Omnibus): | 0.893 | Jarque-Bera (JB): | 0.161 |
| Skew: | -0.169 | Prob(JB): | 0.923 |
| Kurtosis: | 2.768 | Cond. No. | 962. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 18.79 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.56e-05 |
| Time: | 05:22:24 | Log-Likelihood: | -100.95 |
| 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 | -24.7248 | 174.330 | -0.142 | 0.889 | -388.371 338.921 |
| C(dose)[T.1] | 52.5750 | 8.886 | 5.917 | 0.000 | 34.039 71.111 |
| expression | 8.5672 | 18.910 | 0.453 | 0.655 | -30.878 48.013 |
| Omnibus: | 0.052 | Durbin-Watson: | 1.970 |
| Prob(Omnibus): | 0.975 | Jarque-Bera (JB): | 0.273 |
| Skew: | -0.011 | Prob(JB): | 0.872 |
| Kurtosis: | 2.467 | Cond. No. | 375. |
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: | 05:22:24 | 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: | 0.045 |
| Model: | OLS | Adj. R-squared: | -0.001 |
| Method: | Least Squares | F-statistic: | 0.9799 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.333 |
| Time: | 05:22:24 | Log-Likelihood: | -112.58 |
| No. Observations: | 23 | AIC: | 229.2 |
| Df Residuals: | 21 | BIC: | 231.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -195.6253 | 278.246 | -0.703 | 0.490 | -774.269 383.018 |
| expression | 29.7476 | 30.052 | 0.990 | 0.333 | -32.748 92.243 |
| Omnibus: | 3.141 | Durbin-Watson: | 2.618 |
| Prob(Omnibus): | 0.208 | Jarque-Bera (JB): | 1.329 |
| Skew: | 0.089 | Prob(JB): | 0.515 |
| Kurtosis: | 1.836 | Cond. No. | 369. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 4.622 | 0.053 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.603 |
| Model: | OLS | Adj. R-squared: | 0.495 |
| Method: | Least Squares | F-statistic: | 5.571 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0143 |
| Time: | 05:22:24 | Log-Likelihood: | -68.370 |
| No. Observations: | 15 | AIC: | 144.7 |
| Df Residuals: | 11 | BIC: | 147.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -384.0431 | 327.315 | -1.173 | 0.265 | -1104.458 336.372 |
| C(dose)[T.1] | 113.5339 | 412.789 | 0.275 | 0.788 | -795.008 1022.076 |
| expression | 47.0537 | 34.097 | 1.380 | 0.195 | -27.994 122.101 |
| expression:C(dose)[T.1] | -7.2756 | 42.773 | -0.170 | 0.868 | -101.418 86.867 |
| Omnibus: | 2.093 | Durbin-Watson: | 0.582 |
| Prob(Omnibus): | 0.351 | Jarque-Bera (JB): | 1.626 |
| Skew: | -0.709 | Prob(JB): | 0.444 |
| Kurtosis: | 2.232 | Cond. No. | 819. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.602 |
| Model: | OLS | Adj. R-squared: | 0.536 |
| Method: | Least Squares | F-statistic: | 9.077 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00397 |
| Time: | 05:22:24 | Log-Likelihood: | -68.389 |
| No. Observations: | 15 | AIC: | 142.8 |
| Df Residuals: | 12 | BIC: | 144.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -339.6815 | 189.615 | -1.791 | 0.098 | -752.816 73.453 |
| C(dose)[T.1] | 43.3607 | 13.646 | 3.178 | 0.008 | 13.628 73.093 |
| expression | 42.4302 | 19.736 | 2.150 | 0.053 | -0.571 85.431 |
| Omnibus: | 2.063 | Durbin-Watson: | 0.581 |
| Prob(Omnibus): | 0.356 | Jarque-Bera (JB): | 1.600 |
| Skew: | -0.719 | Prob(JB): | 0.449 |
| Kurtosis: | 2.297 | Cond. No. | 278. |
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: | 05:22:24 | 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: | 0.267 |
| Model: | OLS | Adj. R-squared: | 0.211 |
| Method: | Least Squares | F-statistic: | 4.741 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0485 |
| Time: | 05:22:24 | Log-Likelihood: | -72.968 |
| No. Observations: | 15 | AIC: | 149.9 |
| Df Residuals: | 13 | BIC: | 151.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -437.1581 | 243.951 | -1.792 | 0.096 | -964.183 89.867 |
| expression | 54.9043 | 25.216 | 2.177 | 0.048 | 0.428 109.381 |
| Omnibus: | 2.050 | Durbin-Watson: | 1.737 |
| Prob(Omnibus): | 0.359 | Jarque-Bera (JB): | 1.073 |
| Skew: | 0.268 | Prob(JB): | 0.585 |
| Kurtosis: | 1.804 | Cond. No. | 274. |