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 |
| 0.044 | 0.835 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.599 |
| Method: | Least Squares | F-statistic: | 11.97 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000125 |
| Time: | 04:44:26 | Log-Likelihood: | -100.90 |
| No. Observations: | 23 | AIC: | 209.8 |
| Df Residuals: | 19 | BIC: | 214.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 44.4525 | 55.882 | 0.795 | 0.436 | -72.509 161.415 |
| C(dose)[T.1] | 92.0817 | 81.935 | 1.124 | 0.275 | -79.410 263.573 |
| expression | 1.4734 | 8.388 | 0.176 | 0.862 | -16.083 19.029 |
| expression:C(dose)[T.1] | -5.7971 | 12.219 | -0.474 | 0.641 | -31.372 19.778 |
| Omnibus: | 0.960 | Durbin-Watson: | 1.832 |
| Prob(Omnibus): | 0.619 | Jarque-Bera (JB): | 0.809 |
| Skew: | 0.162 | Prob(JB): | 0.667 |
| Kurtosis: | 2.140 | Cond. No. | 161. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.56 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.77e-05 |
| Time: | 04:44:26 | Log-Likelihood: | -101.04 |
| 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 | 62.5403 | 40.057 | 1.561 | 0.134 | -21.017 146.098 |
| C(dose)[T.1] | 53.4418 | 8.774 | 6.091 | 0.000 | 35.139 71.745 |
| expression | -1.2583 | 5.980 | -0.210 | 0.835 | -13.733 11.216 |
| Omnibus: | 0.535 | Durbin-Watson: | 1.856 |
| Prob(Omnibus): | 0.765 | Jarque-Bera (JB): | 0.605 |
| Skew: | 0.102 | Prob(JB): | 0.739 |
| Kurtosis: | 2.232 | Cond. No. | 62.8 |
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: | 04:44:26 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.006721 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.935 |
| Time: | 04:44:26 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 74.3413 | 65.973 | 1.127 | 0.273 | -62.858 211.540 |
| expression | 0.8071 | 9.845 | 0.082 | 0.935 | -19.666 21.281 |
| Omnibus: | 3.337 | Durbin-Watson: | 2.479 |
| Prob(Omnibus): | 0.189 | Jarque-Bera (JB): | 1.575 |
| Skew: | 0.288 | Prob(JB): | 0.455 |
| Kurtosis: | 1.855 | Cond. No. | 62.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 4.119 | 0.065 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.626 |
| Model: | OLS | Adj. R-squared: | 0.524 |
| Method: | Least Squares | F-statistic: | 6.144 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0104 |
| Time: | 04:44:26 | Log-Likelihood: | -67.918 |
| No. Observations: | 15 | AIC: | 143.8 |
| Df Residuals: | 11 | BIC: | 146.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 117.2447 | 61.465 | 1.908 | 0.083 | -18.038 252.528 |
| C(dose)[T.1] | 139.9136 | 90.576 | 1.545 | 0.151 | -59.443 339.270 |
| expression | -7.8213 | 9.524 | -0.821 | 0.429 | -28.785 13.142 |
| expression:C(dose)[T.1] | -14.8012 | 14.255 | -1.038 | 0.321 | -46.175 16.573 |
| Omnibus: | 0.362 | Durbin-Watson: | 1.118 |
| Prob(Omnibus): | 0.834 | Jarque-Bera (JB): | 0.467 |
| Skew: | -0.280 | Prob(JB): | 0.792 |
| Kurtosis: | 2.341 | Cond. No. | 113. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.590 |
| Model: | OLS | Adj. R-squared: | 0.521 |
| Method: | Least Squares | F-statistic: | 8.621 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00478 |
| Time: | 04:44:26 | Log-Likelihood: | -68.620 |
| No. Observations: | 15 | AIC: | 143.2 |
| Df Residuals: | 12 | BIC: | 145.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 159.3330 | 46.355 | 3.437 | 0.005 | 58.334 260.332 |
| C(dose)[T.1] | 46.9280 | 13.626 | 3.444 | 0.005 | 17.239 76.617 |
| expression | -14.4292 | 7.109 | -2.030 | 0.065 | -29.919 1.061 |
| Omnibus: | 0.637 | Durbin-Watson: | 1.137 |
| Prob(Omnibus): | 0.727 | Jarque-Bera (JB): | 0.626 |
| Skew: | -0.190 | Prob(JB): | 0.731 |
| Kurtosis: | 2.075 | Cond. No. | 44.8 |
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: | 04:44:26 | 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: | 0.184 |
| Model: | OLS | Adj. R-squared: | 0.121 |
| Method: | Least Squares | F-statistic: | 2.932 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.111 |
| Time: | 04:44:26 | Log-Likelihood: | -73.775 |
| No. Observations: | 15 | AIC: | 151.5 |
| Df Residuals: | 13 | BIC: | 153.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 196.9841 | 61.030 | 3.228 | 0.007 | 65.138 328.831 |
| expression | -16.4375 | 9.599 | -1.712 | 0.111 | -37.175 4.300 |
| Omnibus: | 3.276 | Durbin-Watson: | 1.865 |
| Prob(Omnibus): | 0.194 | Jarque-Bera (JB): | 1.195 |
| Skew: | 0.128 | Prob(JB): | 0.550 |
| Kurtosis: | 1.641 | Cond. No. | 43.3 |