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 |
| 0.311 | 0.583 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.602 |
| Method: | Least Squares | F-statistic: | 12.10 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000117 |
| Time: | 03:50:07 | Log-Likelihood: | -100.82 |
| No. Observations: | 23 | AIC: | 209.6 |
| Df Residuals: | 19 | BIC: | 214.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 37.5896 | 136.931 | 0.275 | 0.787 | -249.010 324.189 |
| C(dose)[T.1] | -10.9419 | 188.430 | -0.058 | 0.954 | -405.330 383.446 |
| expression | 2.1957 | 18.073 | 0.121 | 0.905 | -35.631 40.023 |
| expression:C(dose)[T.1] | 8.0498 | 24.387 | 0.330 | 0.745 | -42.993 59.092 |
| Omnibus: | 0.540 | Durbin-Watson: | 1.976 |
| Prob(Omnibus): | 0.763 | Jarque-Bera (JB): | 0.628 |
| Skew: | 0.177 | Prob(JB): | 0.731 |
| Kurtosis: | 2.272 | Cond. No. | 442. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 18.94 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.43e-05 |
| Time: | 03:50:07 | Log-Likelihood: | -100.89 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 4.1270 | 89.975 | 0.046 | 0.964 | -183.557 191.811 |
| C(dose)[T.1] | 51.1728 | 9.528 | 5.371 | 0.000 | 31.298 71.048 |
| expression | 6.6167 | 11.861 | 0.558 | 0.583 | -18.125 31.358 |
| Omnibus: | 0.763 | Durbin-Watson: | 1.902 |
| Prob(Omnibus): | 0.683 | Jarque-Bera (JB): | 0.711 |
| Skew: | 0.118 | Prob(JB): | 0.701 |
| Kurtosis: | 2.172 | Cond. No. | 163. |
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: | 03:50:07 | 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: | 0.156 |
| Model: | OLS | Adj. R-squared: | 0.116 |
| Method: | Least Squares | F-statistic: | 3.883 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0621 |
| Time: | 03:50:07 | Log-Likelihood: | -111.15 |
| No. Observations: | 23 | AIC: | 226.3 |
| Df Residuals: | 21 | BIC: | 228.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -171.7832 | 127.806 | -1.344 | 0.193 | -437.570 94.004 |
| expression | 32.5553 | 16.521 | 1.970 | 0.062 | -1.803 66.914 |
| Omnibus: | 0.469 | Durbin-Watson: | 2.592 |
| Prob(Omnibus): | 0.791 | Jarque-Bera (JB): | 0.590 |
| Skew: | 0.240 | Prob(JB): | 0.745 |
| Kurtosis: | 2.379 | Cond. No. | 152. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.000 | 0.994 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.579 |
| Model: | OLS | Adj. R-squared: | 0.464 |
| Method: | Least Squares | F-statistic: | 5.042 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0194 |
| Time: | 03:50:07 | Log-Likelihood: | -68.812 |
| No. Observations: | 15 | AIC: | 145.6 |
| Df Residuals: | 11 | BIC: | 148.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -3.9272 | 106.187 | -0.037 | 0.971 | -237.644 229.790 |
| C(dose)[T.1] | 646.7144 | 324.574 | 1.992 | 0.072 | -67.669 1361.098 |
| expression | 11.1660 | 16.535 | 0.675 | 0.513 | -25.228 47.560 |
| expression:C(dose)[T.1] | -84.3023 | 45.707 | -1.844 | 0.092 | -184.903 16.298 |
| Omnibus: | 0.591 | Durbin-Watson: | 1.311 |
| Prob(Omnibus): | 0.744 | Jarque-Bera (JB): | 0.139 |
| Skew: | -0.233 | Prob(JB): | 0.933 |
| Kurtosis: | 2.925 | Cond. No. | 379. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.885 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0281 |
| Time: | 03:50:07 | Log-Likelihood: | -70.833 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 66.5794 | 108.530 | 0.613 | 0.551 | -169.888 303.047 |
| C(dose)[T.1] | 49.0896 | 20.785 | 2.362 | 0.036 | 3.803 94.376 |
| expression | 0.1329 | 16.888 | 0.008 | 0.994 | -36.662 36.928 |
| Omnibus: | 2.727 | Durbin-Watson: | 0.809 |
| Prob(Omnibus): | 0.256 | Jarque-Bera (JB): | 1.876 |
| Skew: | -0.845 | Prob(JB): | 0.391 |
| Kurtosis: | 2.622 | Cond. No. | 97.7 |
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: | 03:50:07 | 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: | 0.193 |
| Model: | OLS | Adj. R-squared: | 0.130 |
| Method: | Least Squares | F-statistic: | 3.100 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.102 |
| Time: | 03:50:07 | Log-Likelihood: | -73.696 |
| No. Observations: | 15 | AIC: | 151.4 |
| Df Residuals: | 13 | BIC: | 152.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -84.8759 | 101.815 | -0.834 | 0.420 | -304.833 135.081 |
| expression | 26.1825 | 14.871 | 1.761 | 0.102 | -5.943 58.308 |
| Omnibus: | 1.230 | Durbin-Watson: | 1.266 |
| Prob(Omnibus): | 0.541 | Jarque-Bera (JB): | 0.807 |
| Skew: | -0.164 | Prob(JB): | 0.668 |
| Kurtosis: | 1.912 | Cond. No. | 78.0 |