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 |
| 0.459 | 0.506 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.606 |
| Method: | Least Squares | F-statistic: | 12.26 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.000108 |
| Time: | 22:45:42 | Log-Likelihood: | -100.72 |
| No. Observations: | 23 | AIC: | 209.4 |
| Df Residuals: | 19 | BIC: | 214.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 39.7030 | 104.455 | 0.380 | 0.708 | -178.923 258.329 |
| C(dose)[T.1] | 6.7126 | 133.037 | 0.050 | 0.960 | -271.736 285.162 |
| expression | 2.4748 | 17.791 | 0.139 | 0.891 | -34.762 39.712 |
| expression:C(dose)[T.1] | 8.4247 | 23.044 | 0.366 | 0.719 | -39.806 56.656 |
| Omnibus: | 1.265 | Durbin-Watson: | 1.770 |
| Prob(Omnibus): | 0.531 | Jarque-Bera (JB): | 0.896 |
| Skew: | 0.127 | Prob(JB): | 0.639 |
| Kurtosis: | 2.067 | Cond. No. | 241. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 19.15 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 2.26e-05 |
| Time: | 22:45:42 | Log-Likelihood: | -100.80 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 10.2707 | 65.098 | 0.158 | 0.876 | -125.522 146.064 |
| C(dose)[T.1] | 55.2310 | 9.110 | 6.063 | 0.000 | 36.228 74.234 |
| expression | 7.4965 | 11.060 | 0.678 | 0.506 | -15.573 30.566 |
| Omnibus: | 1.172 | Durbin-Watson: | 1.756 |
| Prob(Omnibus): | 0.557 | Jarque-Bera (JB): | 0.896 |
| Skew: | 0.177 | Prob(JB): | 0.639 |
| Kurtosis: | 2.101 | Cond. No. | 89.4 |
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: | 22:45:42 | 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: | 0.026 |
| Model: | OLS | Adj. R-squared: | -0.020 |
| Method: | Least Squares | F-statistic: | 0.5703 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.459 |
| Time: | 22:45:42 | Log-Likelihood: | -112.80 |
| No. Observations: | 23 | AIC: | 229.6 |
| Df Residuals: | 21 | BIC: | 231.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 154.7381 | 99.594 | 1.554 | 0.135 | -52.379 361.855 |
| expression | -13.0691 | 17.306 | -0.755 | 0.459 | -49.058 22.920 |
| Omnibus: | 1.942 | Durbin-Watson: | 2.529 |
| Prob(Omnibus): | 0.379 | Jarque-Bera (JB): | 1.215 |
| Skew: | 0.263 | Prob(JB): | 0.545 |
| Kurtosis: | 2.005 | Cond. No. | 82.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.218 | 0.291 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.524 |
| Model: | OLS | Adj. R-squared: | 0.394 |
| Method: | Least Squares | F-statistic: | 4.033 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0368 |
| Time: | 22:45:42 | Log-Likelihood: | -69.736 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 11 | BIC: | 150.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 32.6487 | 196.347 | 0.166 | 0.871 | -399.509 464.807 |
| C(dose)[T.1] | -142.1230 | 262.108 | -0.542 | 0.598 | -719.019 434.773 |
| expression | 4.3710 | 24.636 | 0.177 | 0.862 | -49.853 58.595 |
| expression:C(dose)[T.1] | 24.9619 | 33.355 | 0.748 | 0.470 | -48.451 98.375 |
| Omnibus: | 2.227 | Durbin-Watson: | 0.774 |
| Prob(Omnibus): | 0.328 | Jarque-Bera (JB): | 1.599 |
| Skew: | -0.763 | Prob(JB): | 0.450 |
| Kurtosis: | 2.523 | Cond. No. | 371. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.500 |
| Model: | OLS | Adj. R-squared: | 0.416 |
| Method: | Least Squares | F-statistic: | 5.989 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0157 |
| Time: | 22:45:42 | Log-Likelihood: | -70.108 |
| No. Observations: | 15 | AIC: | 146.2 |
| Df Residuals: | 12 | BIC: | 148.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -75.7091 | 130.167 | -0.582 | 0.572 | -359.318 207.900 |
| C(dose)[T.1] | 53.6754 | 15.537 | 3.455 | 0.005 | 19.824 87.527 |
| expression | 17.9888 | 16.301 | 1.104 | 0.291 | -17.527 53.505 |
| Omnibus: | 3.316 | Durbin-Watson: | 0.888 |
| Prob(Omnibus): | 0.191 | Jarque-Bera (JB): | 2.342 |
| Skew: | -0.946 | Prob(JB): | 0.310 |
| Kurtosis: | 2.592 | Cond. No. | 139. |
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: | 22:45:42 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.075 |
| Method: | Least Squares | F-statistic: | 0.02356 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.880 |
| Time: | 22:45:42 | Log-Likelihood: | -75.286 |
| 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 | 68.0238 | 167.360 | 0.406 | 0.691 | -293.535 429.583 |
| expression | 3.2774 | 21.350 | 0.154 | 0.880 | -42.848 49.402 |
| Omnibus: | 0.436 | Durbin-Watson: | 1.664 |
| Prob(Omnibus): | 0.804 | Jarque-Bera (JB): | 0.514 |
| Skew: | 0.026 | Prob(JB): | 0.773 |
| Kurtosis: | 2.095 | Cond. No. | 131. |