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 |
| 0.026 | 0.874 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.596 |
| Method: | Least Squares | F-statistic: | 11.80 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.000137 |
| Time: | 11:47:02 | Log-Likelihood: | -101.01 |
| No. Observations: | 23 | AIC: | 210.0 |
| Df Residuals: | 19 | BIC: | 214.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 49.2013 | 21.970 | 2.239 | 0.037 | 3.217 95.186 |
| C(dose)[T.1] | 65.9396 | 48.807 | 1.351 | 0.193 | -36.215 168.094 |
| expression | 1.3305 | 5.600 | 0.238 | 0.815 | -10.391 13.052 |
| expression:C(dose)[T.1] | -3.8604 | 15.409 | -0.251 | 0.805 | -36.111 28.390 |
| Omnibus: | 0.338 | Durbin-Watson: | 1.879 |
| Prob(Omnibus): | 0.844 | Jarque-Bera (JB): | 0.492 |
| Skew: | 0.010 | Prob(JB): | 0.782 |
| Kurtosis: | 2.284 | Cond. No. | 46.0 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.53 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 2.80e-05 |
| Time: | 11:47:02 | Log-Likelihood: | -101.05 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 51.1204 | 20.104 | 2.543 | 0.019 | 9.185 93.056 |
| C(dose)[T.1] | 53.9615 | 9.583 | 5.631 | 0.000 | 33.972 73.951 |
| expression | 0.8206 | 5.094 | 0.161 | 0.874 | -9.805 11.446 |
| Omnibus: | 0.403 | Durbin-Watson: | 1.899 |
| Prob(Omnibus): | 0.817 | Jarque-Bera (JB): | 0.532 |
| Skew: | 0.068 | Prob(JB): | 0.767 |
| Kurtosis: | 2.268 | Cond. No. | 18.1 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 11:47:02 | 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: | 0.094 |
| Model: | OLS | Adj. R-squared: | 0.051 |
| Method: | Least Squares | F-statistic: | 2.175 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.155 |
| Time: | 11:47:02 | Log-Likelihood: | -111.97 |
| No. Observations: | 23 | AIC: | 227.9 |
| Df Residuals: | 21 | BIC: | 230.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 116.3611 | 25.780 | 4.514 | 0.000 | 62.748 169.974 |
| expression | -10.7798 | 7.310 | -1.475 | 0.155 | -25.981 4.422 |
| Omnibus: | 3.005 | Durbin-Watson: | 2.176 |
| Prob(Omnibus): | 0.223 | Jarque-Bera (JB): | 1.295 |
| Skew: | 0.072 | Prob(JB): | 0.523 |
| Kurtosis: | 1.847 | Cond. No. | 14.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 7.136 | 0.020 | 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: | 0.562 |
| Method: | Least Squares | F-statistic: | 6.984 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00674 |
| Time: | 11:47:02 | Log-Likelihood: | -67.302 |
| No. Observations: | 15 | AIC: | 142.6 |
| Df Residuals: | 11 | BIC: | 145.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 121.5003 | 28.694 | 4.234 | 0.001 | 58.345 184.655 |
| C(dose)[T.1] | 52.8653 | 46.601 | 1.134 | 0.281 | -49.702 155.433 |
| expression | -11.3970 | 5.708 | -1.997 | 0.071 | -23.960 1.166 |
| expression:C(dose)[T.1] | -2.1560 | 10.125 | -0.213 | 0.835 | -24.442 20.130 |
| Omnibus: | 0.068 | Durbin-Watson: | 1.849 |
| Prob(Omnibus): | 0.967 | Jarque-Bera (JB): | 0.108 |
| Skew: | 0.076 | Prob(JB): | 0.948 |
| Kurtosis: | 2.614 | Cond. No. | 43.1 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.597 |
| Method: | Least Squares | F-statistic: | 11.36 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00171 |
| Time: | 11:47:02 | Log-Likelihood: | -67.333 |
| No. Observations: | 15 | AIC: | 140.7 |
| Df Residuals: | 12 | BIC: | 142.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 124.7508 | 23.310 | 5.352 | 0.000 | 73.963 175.539 |
| C(dose)[T.1] | 43.3482 | 12.655 | 3.425 | 0.005 | 15.775 70.921 |
| expression | -12.0822 | 4.523 | -2.671 | 0.020 | -21.937 -2.227 |
| Omnibus: | 0.034 | Durbin-Watson: | 1.904 |
| Prob(Omnibus): | 0.983 | Jarque-Bera (JB): | 0.104 |
| Skew: | -0.017 | Prob(JB): | 0.949 |
| Kurtosis: | 2.594 | Cond. No. | 19.0 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 11:47:02 | 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: | 0.316 |
| Model: | OLS | Adj. R-squared: | 0.264 |
| Method: | Least Squares | F-statistic: | 6.015 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0291 |
| Time: | 11:47:02 | Log-Likelihood: | -72.448 |
| No. Observations: | 15 | AIC: | 148.9 |
| Df Residuals: | 13 | BIC: | 150.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 159.8944 | 28.280 | 5.654 | 0.000 | 98.798 220.990 |
| expression | -14.7625 | 6.019 | -2.453 | 0.029 | -27.766 -1.759 |
| Omnibus: | 2.484 | Durbin-Watson: | 2.127 |
| Prob(Omnibus): | 0.289 | Jarque-Bera (JB): | 1.866 |
| Skew: | 0.808 | Prob(JB): | 0.393 |
| Kurtosis: | 2.391 | Cond. No. | 16.5 |