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 |
| 0.146 | 0.706 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.687 |
| Model: | OLS | Adj. R-squared: | 0.637 |
| Method: | Least Squares | F-statistic: | 13.89 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.95e-05 |
| Time: | 03:44:23 | Log-Likelihood: | -99.750 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 19 | BIC: | 212.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -82.6405 | 115.414 | -0.716 | 0.483 | -324.204 158.923 |
| C(dose)[T.1] | 335.0357 | 192.373 | 1.742 | 0.098 | -67.605 737.677 |
| expression | 27.2663 | 22.966 | 1.187 | 0.250 | -20.801 75.334 |
| expression:C(dose)[T.1] | -56.4618 | 38.577 | -1.464 | 0.160 | -137.204 24.281 |
| Omnibus: | 0.094 | Durbin-Watson: | 1.874 |
| Prob(Omnibus): | 0.954 | Jarque-Bera (JB): | 0.320 |
| Skew: | -0.011 | Prob(JB): | 0.852 |
| Kurtosis: | 2.423 | Cond. No. | 287. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.617 |
| Method: | Least Squares | F-statistic: | 18.70 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.63e-05 |
| Time: | 03:44:23 | Log-Likelihood: | -100.98 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 17.7917 | 95.413 | 0.186 | 0.854 | -181.235 216.819 |
| C(dose)[T.1] | 53.7550 | 8.806 | 6.104 | 0.000 | 35.386 72.124 |
| expression | 7.2558 | 18.972 | 0.382 | 0.706 | -32.320 46.831 |
| Omnibus: | 0.393 | Durbin-Watson: | 1.842 |
| Prob(Omnibus): | 0.821 | Jarque-Bera (JB): | 0.525 |
| Skew: | 0.055 | Prob(JB): | 0.769 |
| Kurtosis: | 2.268 | Cond. No. | 114. |
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: | 03:44:23 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.045 |
| Method: | Least Squares | F-statistic: | 0.05238 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.821 |
| Time: | 03:44:23 | Log-Likelihood: | -113.08 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 115.2295 | 155.335 | 0.742 | 0.466 | -207.807 438.266 |
| expression | -7.1146 | 31.087 | -0.229 | 0.821 | -71.763 57.534 |
| Omnibus: | 2.968 | Durbin-Watson: | 2.494 |
| Prob(Omnibus): | 0.227 | Jarque-Bera (JB): | 1.430 |
| Skew: | 0.240 | Prob(JB): | 0.489 |
| Kurtosis: | 1.877 | Cond. No. | 112. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.616 | 0.082 | 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: | 5.565 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0143 |
| Time: | 03:44:23 | Log-Likelihood: | -68.375 |
| 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 | -475.7355 | 353.837 | -1.345 | 0.206 | -1254.526 303.056 |
| C(dose)[T.1] | 369.5907 | 388.965 | 0.950 | 0.362 | -486.516 1225.697 |
| expression | 105.4767 | 68.683 | 1.536 | 0.153 | -45.694 256.647 |
| expression:C(dose)[T.1] | -64.1007 | 74.929 | -0.855 | 0.411 | -229.018 100.817 |
| Omnibus: | 1.432 | Durbin-Watson: | 1.326 |
| Prob(Omnibus): | 0.489 | Jarque-Bera (JB): | 0.978 |
| Skew: | -0.332 | Prob(JB): | 0.613 |
| Kurtosis: | 1.940 | Cond. No. | 474. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.576 |
| Model: | OLS | Adj. R-squared: | 0.506 |
| Method: | Least Squares | F-statistic: | 8.165 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00578 |
| Time: | 03:44:23 | Log-Likelihood: | -68.858 |
| No. Observations: | 15 | AIC: | 143.7 |
| Df Residuals: | 12 | BIC: | 145.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -198.3806 | 140.148 | -1.416 | 0.182 | -503.738 106.977 |
| C(dose)[T.1] | 37.0963 | 15.194 | 2.441 | 0.031 | 3.991 70.202 |
| expression | 51.6173 | 27.145 | 1.902 | 0.082 | -7.526 110.761 |
| Omnibus: | 0.862 | Durbin-Watson: | 0.950 |
| Prob(Omnibus): | 0.650 | Jarque-Bera (JB): | 0.790 |
| Skew: | -0.354 | Prob(JB): | 0.674 |
| Kurtosis: | 2.126 | Cond. No. | 112. |
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: | 03:44:23 | 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: | 0.366 |
| Model: | OLS | Adj. R-squared: | 0.317 |
| Method: | Least Squares | F-statistic: | 7.505 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0169 |
| Time: | 03:44:23 | Log-Likelihood: | -71.882 |
| No. Observations: | 15 | AIC: | 147.8 |
| Df Residuals: | 13 | BIC: | 149.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -324.9944 | 153.040 | -2.124 | 0.053 | -655.616 5.628 |
| expression | 79.3725 | 28.974 | 2.739 | 0.017 | 16.779 141.966 |
| Omnibus: | 1.459 | Durbin-Watson: | 1.364 |
| Prob(Omnibus): | 0.482 | Jarque-Bera (JB): | 0.865 |
| Skew: | 0.575 | Prob(JB): | 0.649 |
| Kurtosis: | 2.753 | Cond. No. | 103. |