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 |
| 1.997 | 0.173 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.696 |
| Model: | OLS | Adj. R-squared: | 0.648 |
| Method: | Least Squares | F-statistic: | 14.50 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 3.75e-05 |
| Time: | 22:45:15 | Log-Likelihood: | -99.409 |
| No. Observations: | 23 | AIC: | 206.8 |
| Df Residuals: | 19 | BIC: | 211.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 33.2727 | 49.507 | 0.672 | 0.510 | -70.347 136.892 |
| C(dose)[T.1] | -16.6941 | 74.082 | -0.225 | 0.824 | -171.750 138.362 |
| expression | 3.5840 | 8.417 | 0.426 | 0.675 | -14.033 21.201 |
| expression:C(dose)[T.1] | 12.4761 | 12.821 | 0.973 | 0.343 | -14.358 39.310 |
| Omnibus: | 2.039 | Durbin-Watson: | 1.906 |
| Prob(Omnibus): | 0.361 | Jarque-Bera (JB): | 1.732 |
| Skew: | 0.566 | Prob(JB): | 0.421 |
| Kurtosis: | 2.273 | Cond. No. | 133. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.681 |
| Model: | OLS | Adj. R-squared: | 0.649 |
| Method: | Least Squares | F-statistic: | 21.34 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.09e-05 |
| Time: | 22:45:15 | Log-Likelihood: | -99.968 |
| No. Observations: | 23 | AIC: | 205.9 |
| Df Residuals: | 20 | BIC: | 209.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 1.8620 | 37.487 | 0.050 | 0.961 | -76.335 80.059 |
| C(dose)[T.1] | 54.9260 | 8.437 | 6.510 | 0.000 | 37.326 72.526 |
| expression | 8.9611 | 6.341 | 1.413 | 0.173 | -4.265 22.187 |
| Omnibus: | 1.983 | Durbin-Watson: | 2.146 |
| Prob(Omnibus): | 0.371 | Jarque-Bera (JB): | 1.418 |
| Skew: | 0.393 | Prob(JB): | 0.492 |
| Kurtosis: | 2.072 | Cond. No. | 53.7 |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:45:16 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.043 |
| Method: | Least Squares | F-statistic: | 0.1021 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.752 |
| Time: | 22:45:16 | Log-Likelihood: | -113.05 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 59.7925 | 62.762 | 0.953 | 0.352 | -70.728 190.312 |
| expression | 3.4612 | 10.830 | 0.320 | 0.752 | -19.062 25.984 |
| Omnibus: | 3.937 | Durbin-Watson: | 2.538 |
| Prob(Omnibus): | 0.140 | Jarque-Bera (JB): | 1.684 |
| Skew: | 0.287 | Prob(JB): | 0.431 |
| Kurtosis: | 1.805 | Cond. No. | 52.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.167 | 0.690 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.517 |
| Model: | OLS | Adj. R-squared: | 0.385 |
| Method: | Least Squares | F-statistic: | 3.925 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0396 |
| Time: | 22:45:16 | Log-Likelihood: | -69.842 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 11 | BIC: | 150.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 95.5176 | 61.580 | 1.551 | 0.149 | -40.020 231.055 |
| C(dose)[T.1] | -52.3262 | 89.045 | -0.588 | 0.569 | -248.313 143.661 |
| expression | -4.9647 | 10.701 | -0.464 | 0.652 | -28.519 18.589 |
| expression:C(dose)[T.1] | 19.0626 | 16.215 | 1.176 | 0.265 | -16.626 54.751 |
| Omnibus: | 1.619 | Durbin-Watson: | 0.767 |
| Prob(Omnibus): | 0.445 | Jarque-Bera (JB): | 1.152 |
| Skew: | -0.448 | Prob(JB): | 0.562 |
| Kurtosis: | 1.980 | Cond. No. | 85.1 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.456 |
| Model: | OLS | Adj. R-squared: | 0.366 |
| Method: | Least Squares | F-statistic: | 5.036 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0258 |
| Time: | 22:45:16 | Log-Likelihood: | -70.729 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 48.5405 | 47.595 | 1.020 | 0.328 | -55.161 152.242 |
| C(dose)[T.1] | 50.6952 | 16.055 | 3.158 | 0.008 | 15.713 85.677 |
| expression | 3.3384 | 8.167 | 0.409 | 0.690 | -14.456 21.132 |
| Omnibus: | 2.464 | Durbin-Watson: | 0.695 |
| Prob(Omnibus): | 0.292 | Jarque-Bera (JB): | 1.857 |
| Skew: | -0.804 | Prob(JB): | 0.395 |
| Kurtosis: | 2.381 | Cond. No. | 35.1 |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:45:16 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.072 |
| Method: | Least Squares | F-statistic: | 0.06087 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.809 |
| Time: | 22:45:16 | Log-Likelihood: | -75.265 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 107.4848 | 56.916 | 1.888 | 0.081 | -15.474 230.444 |
| expression | -2.5503 | 10.336 | -0.247 | 0.809 | -24.881 19.780 |
| Omnibus: | 0.839 | Durbin-Watson: | 1.674 |
| Prob(Omnibus): | 0.657 | Jarque-Bera (JB): | 0.666 |
| Skew: | 0.077 | Prob(JB): | 0.717 |
| Kurtosis: | 1.979 | Cond. No. | 31.9 |