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 |
| 0.039 | 0.846 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.595 |
| Method: | Least Squares | F-statistic: | 11.78 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000138 |
| Time: | 04:19:49 | Log-Likelihood: | -101.02 |
| 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 | 57.4088 | 100.247 | 0.573 | 0.574 | -152.411 267.228 |
| C(dose)[T.1] | 81.5808 | 157.280 | 0.519 | 0.610 | -247.610 410.772 |
| expression | -0.4276 | 13.368 | -0.032 | 0.975 | -28.407 27.552 |
| expression:C(dose)[T.1] | -3.6885 | 20.728 | -0.178 | 0.861 | -47.073 39.696 |
| Omnibus: | 0.334 | Durbin-Watson: | 1.902 |
| Prob(Omnibus): | 0.846 | Jarque-Bera (JB): | 0.491 |
| Skew: | 0.050 | Prob(JB): | 0.782 |
| Kurtosis: | 2.291 | Cond. No. | 338. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.55 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.78e-05 |
| Time: | 04:19:49 | Log-Likelihood: | -101.04 |
| 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 | 68.8911 | 74.839 | 0.921 | 0.368 | -87.220 225.002 |
| C(dose)[T.1] | 53.6404 | 8.896 | 6.030 | 0.000 | 35.084 72.197 |
| expression | -1.9617 | 9.966 | -0.197 | 0.846 | -22.751 18.827 |
| Omnibus: | 0.368 | Durbin-Watson: | 1.852 |
| Prob(Omnibus): | 0.832 | Jarque-Bera (JB): | 0.512 |
| Skew: | 0.064 | Prob(JB): | 0.774 |
| Kurtosis: | 2.280 | Cond. No. | 132. |
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, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 04:19:49 | 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: | 0.013 |
| Model: | OLS | Adj. R-squared: | -0.034 |
| Method: | Least Squares | F-statistic: | 0.2759 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.605 |
| Time: | 04:19:49 | Log-Likelihood: | -112.95 |
| No. Observations: | 23 | AIC: | 229.9 |
| Df Residuals: | 21 | BIC: | 232.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 15.8757 | 121.753 | 0.130 | 0.897 | -237.324 269.075 |
| expression | 8.4462 | 16.080 | 0.525 | 0.605 | -24.994 41.886 |
| Omnibus: | 2.768 | Durbin-Watson: | 2.495 |
| Prob(Omnibus): | 0.251 | Jarque-Bera (JB): | 1.459 |
| Skew: | 0.291 | Prob(JB): | 0.482 |
| Kurtosis: | 1.912 | Cond. No. | 131. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 5.054 | 0.044 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.702 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 8.648 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00312 |
| Time: | 04:19:49 | Log-Likelihood: | -66.214 |
| No. Observations: | 15 | AIC: | 140.4 |
| Df Residuals: | 11 | BIC: | 143.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -80.3468 | 188.970 | -0.425 | 0.679 | -496.268 335.574 |
| C(dose)[T.1] | -556.3572 | 317.214 | -1.754 | 0.107 | -1254.541 141.826 |
| expression | 19.0661 | 24.355 | 0.783 | 0.450 | -34.538 72.670 |
| expression:C(dose)[T.1] | 71.2619 | 39.057 | 1.825 | 0.095 | -14.702 157.226 |
| Omnibus: | 0.652 | Durbin-Watson: | 1.093 |
| Prob(Omnibus): | 0.722 | Jarque-Bera (JB): | 0.387 |
| Skew: | -0.365 | Prob(JB): | 0.824 |
| Kurtosis: | 2.707 | Cond. No. | 548. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.612 |
| Model: | OLS | Adj. R-squared: | 0.547 |
| Method: | Least Squares | F-statistic: | 9.469 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00340 |
| Time: | 04:19:49 | Log-Likelihood: | -68.197 |
| No. Observations: | 15 | AIC: | 142.4 |
| Df Residuals: | 12 | BIC: | 144.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -295.1075 | 161.545 | -1.827 | 0.093 | -647.084 56.869 |
| C(dose)[T.1] | 21.6343 | 18.017 | 1.201 | 0.253 | -17.621 60.890 |
| expression | 46.7748 | 20.806 | 2.248 | 0.044 | 1.443 92.106 |
| Omnibus: | 0.187 | Durbin-Watson: | 1.314 |
| Prob(Omnibus): | 0.911 | Jarque-Bera (JB): | 0.387 |
| Skew: | 0.080 | Prob(JB): | 0.824 |
| Kurtosis: | 2.229 | Cond. No. | 202. |
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, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 04:19:49 | 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: | 0.566 |
| Model: | OLS | Adj. R-squared: | 0.532 |
| Method: | Least Squares | F-statistic: | 16.92 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00122 |
| Time: | 04:19:49 | Log-Likelihood: | -69.048 |
| No. Observations: | 15 | AIC: | 142.1 |
| Df Residuals: | 13 | BIC: | 143.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -420.6682 | 125.211 | -3.360 | 0.005 | -691.171 -150.166 |
| expression | 63.7742 | 15.503 | 4.114 | 0.001 | 30.282 97.267 |
| Omnibus: | 1.123 | Durbin-Watson: | 1.662 |
| Prob(Omnibus): | 0.570 | Jarque-Bera (JB): | 0.955 |
| Skew: | 0.520 | Prob(JB): | 0.620 |
| Kurtosis: | 2.330 | Cond. No. | 153. |