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 |
| 0.783 | 0.387 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.722 |
| Model: | OLS | Adj. R-squared: | 0.678 |
| Method: | Least Squares | F-statistic: | 16.47 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.62e-05 |
| Time: | 22:50:03 | Log-Likelihood: | -98.372 |
| No. Observations: | 23 | AIC: | 204.7 |
| Df Residuals: | 19 | BIC: | 209.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -306.8778 | 179.543 | -1.709 | 0.104 | -682.665 68.909 |
| C(dose)[T.1] | 636.5664 | 289.180 | 2.201 | 0.040 | 31.305 1241.828 |
| expression | 40.7907 | 20.273 | 2.012 | 0.059 | -1.641 83.222 |
| expression:C(dose)[T.1] | -65.4300 | 32.292 | -2.026 | 0.057 | -133.019 2.158 |
| Omnibus: | 0.593 | Durbin-Watson: | 1.890 |
| Prob(Omnibus): | 0.744 | Jarque-Bera (JB): | 0.649 |
| Skew: | -0.158 | Prob(JB): | 0.723 |
| Kurtosis: | 2.240 | Cond. No. | 810. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 19.61 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.93e-05 |
| Time: | 22:50:03 | Log-Likelihood: | -100.62 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -78.6054 | 150.258 | -0.523 | 0.607 | -392.038 234.827 |
| C(dose)[T.1] | 50.8820 | 9.040 | 5.629 | 0.000 | 32.025 69.739 |
| expression | 15.0035 | 16.961 | 0.885 | 0.387 | -20.376 50.383 |
| Omnibus: | 0.284 | Durbin-Watson: | 1.959 |
| Prob(Omnibus): | 0.868 | Jarque-Bera (JB): | 0.425 |
| Skew: | -0.211 | Prob(JB): | 0.809 |
| Kurtosis: | 2.485 | Cond. No. | 317. |
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: | 22:50:03 | 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: | 0.127 |
| Model: | OLS | Adj. R-squared: | 0.086 |
| Method: | Least Squares | F-statistic: | 3.062 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0947 |
| Time: | 22:50:03 | Log-Likelihood: | -111.54 |
| No. Observations: | 23 | AIC: | 227.1 |
| Df Residuals: | 21 | BIC: | 229.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -316.0282 | 226.242 | -1.397 | 0.177 | -786.525 154.468 |
| expression | 44.3143 | 25.323 | 1.750 | 0.095 | -8.347 96.976 |
| Omnibus: | 3.678 | Durbin-Watson: | 2.381 |
| Prob(Omnibus): | 0.159 | Jarque-Bera (JB): | 2.061 |
| Skew: | 0.484 | Prob(JB): | 0.357 |
| Kurtosis: | 1.899 | Cond. No. | 304. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.652 | 0.223 | 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: | 0.418 |
| Method: | Least Squares | F-statistic: | 4.357 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0297 |
| Time: | 22:50:03 | Log-Likelihood: | -69.426 |
| 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 | -400.1791 | 318.689 | -1.256 | 0.235 | -1101.608 301.250 |
| C(dose)[T.1] | 411.9245 | 445.271 | 0.925 | 0.375 | -568.110 1391.959 |
| expression | 54.1397 | 36.876 | 1.468 | 0.170 | -27.024 135.304 |
| expression:C(dose)[T.1] | -41.9921 | 51.534 | -0.815 | 0.432 | -155.418 71.433 |
| Omnibus: | 1.563 | Durbin-Watson: | 1.437 |
| Prob(Omnibus): | 0.458 | Jarque-Bera (JB): | 1.079 |
| Skew: | -0.625 | Prob(JB): | 0.583 |
| Kurtosis: | 2.597 | Cond. No. | 698. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.515 |
| Model: | OLS | Adj. R-squared: | 0.435 |
| Method: | Least Squares | F-statistic: | 6.383 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0129 |
| Time: | 22:50:04 | Log-Likelihood: | -69.866 |
| No. Observations: | 15 | AIC: | 145.7 |
| Df Residuals: | 12 | BIC: | 147.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -214.4679 | 219.613 | -0.977 | 0.348 | -692.965 264.029 |
| C(dose)[T.1] | 49.3028 | 14.757 | 3.341 | 0.006 | 17.150 81.456 |
| expression | 32.6380 | 25.396 | 1.285 | 0.223 | -22.696 87.972 |
| Omnibus: | 0.967 | Durbin-Watson: | 1.125 |
| Prob(Omnibus): | 0.617 | Jarque-Bera (JB): | 0.873 |
| Skew: | -0.454 | Prob(JB): | 0.646 |
| Kurtosis: | 2.243 | Cond. No. | 262. |
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: | 22:50:04 | 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: | 0.065 |
| Model: | OLS | Adj. R-squared: | -0.007 |
| Method: | Least Squares | F-statistic: | 0.9002 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.360 |
| Time: | 22:50:04 | Log-Likelihood: | -74.798 |
| No. Observations: | 15 | AIC: | 153.6 |
| Df Residuals: | 13 | BIC: | 155.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -184.0618 | 292.889 | -0.628 | 0.541 | -816.809 448.685 |
| expression | 32.1619 | 33.898 | 0.949 | 0.360 | -41.071 105.395 |
| Omnibus: | 1.800 | Durbin-Watson: | 1.663 |
| Prob(Omnibus): | 0.407 | Jarque-Bera (JB): | 1.217 |
| Skew: | 0.456 | Prob(JB): | 0.544 |
| Kurtosis: | 1.944 | Cond. No. | 261. |