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 |
| 1.335 | 0.262 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.680 |
| Model: | OLS | Adj. R-squared: | 0.630 |
| Method: | Least Squares | F-statistic: | 13.46 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.04e-05 |
| Time: | 04:39:09 | Log-Likelihood: | -99.998 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 19 | BIC: | 212.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 20.6429 | 44.438 | 0.465 | 0.648 | -72.366 113.652 |
| C(dose)[T.1] | -24.7247 | 108.991 | -0.227 | 0.823 | -252.845 203.396 |
| expression | 5.4328 | 7.128 | 0.762 | 0.455 | -9.486 20.352 |
| expression:C(dose)[T.1] | 13.3504 | 18.170 | 0.735 | 0.471 | -24.680 51.380 |
| Omnibus: | 1.574 | Durbin-Watson: | 1.802 |
| Prob(Omnibus): | 0.455 | Jarque-Bera (JB): | 0.986 |
| Skew: | -0.506 | Prob(JB): | 0.611 |
| Kurtosis: | 2.918 | Cond. No. | 181. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.671 |
| Model: | OLS | Adj. R-squared: | 0.638 |
| Method: | Least Squares | F-statistic: | 20.40 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.49e-05 |
| Time: | 04:39:09 | Log-Likelihood: | -100.32 |
| No. Observations: | 23 | AIC: | 206.6 |
| Df Residuals: | 20 | BIC: | 210.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 7.9491 | 40.468 | 0.196 | 0.846 | -76.466 92.364 |
| C(dose)[T.1] | 55.0993 | 8.627 | 6.387 | 0.000 | 37.104 73.095 |
| expression | 7.4874 | 6.481 | 1.155 | 0.262 | -6.031 21.006 |
| Omnibus: | 0.577 | Durbin-Watson: | 1.788 |
| Prob(Omnibus): | 0.749 | Jarque-Bera (JB): | 0.661 |
| Skew: | -0.291 | Prob(JB): | 0.719 |
| Kurtosis: | 2.409 | Cond. No. | 60.0 |
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: | 04:39:09 | 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: | -0.048 |
| Method: | Least Squares | F-statistic: | 0.0002431 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.988 |
| Time: | 04:39:09 | 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 | 78.6911 | 66.225 | 1.188 | 0.248 | -59.031 216.413 |
| expression | 0.1692 | 10.853 | 0.016 | 0.988 | -22.400 22.739 |
| Omnibus: | 3.330 | Durbin-Watson: | 2.491 |
| Prob(Omnibus): | 0.189 | Jarque-Bera (JB): | 1.573 |
| Skew: | 0.288 | Prob(JB): | 0.455 |
| Kurtosis: | 1.856 | Cond. No. | 57.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.040 | 0.845 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.302 |
| Method: | Least Squares | F-statistic: | 3.019 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0758 |
| Time: | 04:39:09 | Log-Likelihood: | -70.795 |
| No. Observations: | 15 | AIC: | 149.6 |
| Df Residuals: | 11 | BIC: | 152.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 92.6159 | 108.492 | 0.854 | 0.411 | -146.173 331.404 |
| C(dose)[T.1] | 17.0044 | 234.862 | 0.072 | 0.944 | -499.923 533.932 |
| expression | -3.8466 | 16.468 | -0.234 | 0.820 | -40.092 32.398 |
| expression:C(dose)[T.1] | 4.9191 | 35.852 | 0.137 | 0.893 | -73.991 83.829 |
| Omnibus: | 2.775 | Durbin-Watson: | 0.887 |
| Prob(Omnibus): | 0.250 | Jarque-Bera (JB): | 1.862 |
| Skew: | -0.847 | Prob(JB): | 0.394 |
| Kurtosis: | 2.673 | Cond. No. | 230. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.359 |
| Method: | Least Squares | F-statistic: | 4.921 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0275 |
| Time: | 04:39:09 | Log-Likelihood: | -70.808 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 85.8204 | 92.496 | 0.928 | 0.372 | -115.712 287.353 |
| C(dose)[T.1] | 49.1499 | 15.715 | 3.128 | 0.009 | 14.910 83.390 |
| expression | -2.8088 | 14.017 | -0.200 | 0.845 | -33.349 27.731 |
| Omnibus: | 2.830 | Durbin-Watson: | 0.862 |
| Prob(Omnibus): | 0.243 | Jarque-Bera (JB): | 1.933 |
| Skew: | -0.861 | Prob(JB): | 0.380 |
| Kurtosis: | 2.642 | Cond. No. | 79.5 |
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: | 04:39:10 | 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: | 0.003 |
| Model: | OLS | Adj. R-squared: | -0.074 |
| Method: | Least Squares | F-statistic: | 0.03629 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.852 |
| Time: | 04:39:10 | Log-Likelihood: | -75.279 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 116.2658 | 119.064 | 0.976 | 0.347 | -140.956 373.488 |
| expression | -3.4560 | 18.142 | -0.190 | 0.852 | -42.649 35.737 |
| Omnibus: | 0.611 | Durbin-Watson: | 1.691 |
| Prob(Omnibus): | 0.737 | Jarque-Bera (JB): | 0.584 |
| Skew: | 0.046 | Prob(JB): | 0.747 |
| Kurtosis: | 2.038 | Cond. No. | 78.8 |