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 |
| 0.628 | 0.437 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.660 |
| Model: | OLS | Adj. R-squared: | 0.606 |
| Method: | Least Squares | F-statistic: | 12.29 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.000107 |
| Time: | 21:54:49 | Log-Likelihood: | -100.70 |
| No. Observations: | 23 | AIC: | 209.4 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -157.4447 | 326.144 | -0.483 | 0.635 | -840.073 525.184 |
| C(dose)[T.1] | 91.9495 | 521.516 | 0.176 | 0.862 | -999.596 1183.495 |
| expression | 22.0035 | 33.900 | 0.649 | 0.524 | -48.950 92.957 |
| expression:C(dose)[T.1] | -4.0071 | 54.222 | -0.074 | 0.942 | -117.495 109.481 |
| Omnibus: | 0.259 | Durbin-Watson: | 2.141 |
| Prob(Omnibus): | 0.878 | Jarque-Bera (JB): | 0.446 |
| Skew: | 0.022 | Prob(JB): | 0.800 |
| Kurtosis: | 2.320 | Cond. No. | 1.42e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.660 |
| Model: | OLS | Adj. R-squared: | 0.626 |
| Method: | Least Squares | F-statistic: | 19.39 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 2.08e-05 |
| Time: | 21:54:49 | Log-Likelihood: | -100.71 |
| No. Observations: | 23 | AIC: | 207.4 |
| Df Residuals: | 20 | BIC: | 210.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -142.3781 | 248.161 | -0.574 | 0.573 | -660.032 375.276 |
| C(dose)[T.1] | 53.4141 | 8.636 | 6.185 | 0.000 | 35.400 71.428 |
| expression | 20.4372 | 25.791 | 0.792 | 0.437 | -33.363 74.237 |
| Omnibus: | 0.232 | Durbin-Watson: | 2.138 |
| Prob(Omnibus): | 0.890 | Jarque-Bera (JB): | 0.428 |
| Skew: | 0.011 | Prob(JB): | 0.807 |
| Kurtosis: | 2.332 | Cond. No. | 560. |
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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 21:54:49 | 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: | 0.009 |
| Model: | OLS | Adj. R-squared: | -0.038 |
| Method: | Least Squares | F-statistic: | 0.1884 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.669 |
| Time: | 21:54:49 | Log-Likelihood: | -113.00 |
| No. Observations: | 23 | AIC: | 230.0 |
| Df Residuals: | 21 | BIC: | 232.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -99.5809 | 413.165 | -0.241 | 0.812 | -958.805 759.643 |
| expression | 18.6434 | 42.954 | 0.434 | 0.669 | -70.685 107.972 |
| Omnibus: | 4.419 | Durbin-Watson: | 2.606 |
| Prob(Omnibus): | 0.110 | Jarque-Bera (JB): | 1.778 |
| Skew: | 0.294 | Prob(JB): | 0.411 |
| Kurtosis: | 1.771 | Cond. No. | 559. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.381 | 0.263 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.543 |
| Model: | OLS | Adj. R-squared: | 0.419 |
| Method: | Least Squares | F-statistic: | 4.359 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0297 |
| Time: | 21:54:49 | Log-Likelihood: | -69.425 |
| No. Observations: | 15 | AIC: | 146.9 |
| Df Residuals: | 11 | BIC: | 149.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -313.8916 | 265.736 | -1.181 | 0.262 | -898.773 270.990 |
| C(dose)[T.1] | 738.5268 | 724.709 | 1.019 | 0.330 | -856.547 2333.601 |
| expression | 43.3127 | 30.158 | 1.436 | 0.179 | -23.065 109.691 |
| expression:C(dose)[T.1] | -78.4492 | 82.607 | -0.950 | 0.363 | -260.265 103.367 |
| Omnibus: | 3.437 | Durbin-Watson: | 0.913 |
| Prob(Omnibus): | 0.179 | Jarque-Bera (JB): | 1.627 |
| Skew: | -0.789 | Prob(JB): | 0.443 |
| Kurtosis: | 3.333 | Cond. No. | 1.01e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.506 |
| Model: | OLS | Adj. R-squared: | 0.423 |
| Method: | Least Squares | F-statistic: | 6.137 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0146 |
| Time: | 21:54:49 | Log-Likelihood: | -70.016 |
| No. Observations: | 15 | AIC: | 146.0 |
| Df Residuals: | 12 | BIC: | 148.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -221.8361 | 246.412 | -0.900 | 0.386 | -758.722 315.050 |
| C(dose)[T.1] | 50.4384 | 14.943 | 3.375 | 0.006 | 17.881 82.996 |
| expression | 32.8565 | 27.962 | 1.175 | 0.263 | -28.067 93.780 |
| Omnibus: | 2.150 | Durbin-Watson: | 0.824 |
| Prob(Omnibus): | 0.341 | Jarque-Bera (JB): | 1.481 |
| Skew: | -0.743 | Prob(JB): | 0.477 |
| Kurtosis: | 2.601 | Cond. No. | 295. |
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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 21:54:49 | 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: | 0.036 |
| Model: | OLS | Adj. R-squared: | -0.038 |
| Method: | Least Squares | F-statistic: | 0.4896 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.496 |
| Time: | 21:54:49 | Log-Likelihood: | -75.023 |
| No. Observations: | 15 | AIC: | 154.0 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -136.2987 | 328.797 | -0.415 | 0.685 | -846.622 574.024 |
| expression | 26.1808 | 37.415 | 0.700 | 0.496 | -54.650 107.012 |
| Omnibus: | 3.102 | Durbin-Watson: | 1.723 |
| Prob(Omnibus): | 0.212 | Jarque-Bera (JB): | 1.347 |
| Skew: | 0.337 | Prob(JB): | 0.510 |
| Kurtosis: | 1.696 | Cond. No. | 293. |