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 |
| 0.139 | 0.713 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.753 |
| Model: | OLS | Adj. R-squared: | 0.714 |
| Method: | Least Squares | F-statistic: | 19.31 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.40e-06 |
| Time: | 04:17:25 | Log-Likelihood: | -97.021 |
| No. Observations: | 23 | AIC: | 202.0 |
| Df Residuals: | 19 | BIC: | 206.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -48.3314 | 67.607 | -0.715 | 0.483 | -189.835 93.172 |
| C(dose)[T.1] | 350.6765 | 106.168 | 3.303 | 0.004 | 128.465 572.888 |
| expression | 17.4316 | 11.459 | 1.521 | 0.145 | -6.552 41.415 |
| expression:C(dose)[T.1] | -48.5237 | 17.357 | -2.796 | 0.012 | -84.852 -12.196 |
| Omnibus: | 1.846 | Durbin-Watson: | 2.116 |
| Prob(Omnibus): | 0.397 | Jarque-Bera (JB): | 1.611 |
| Skew: | 0.571 | Prob(JB): | 0.447 |
| Kurtosis: | 2.386 | Cond. No. | 222. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.617 |
| Method: | Least Squares | F-statistic: | 18.69 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.64e-05 |
| Time: | 04:17:25 | Log-Likelihood: | -100.98 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 76.0775 | 58.934 | 1.291 | 0.211 | -46.857 199.012 |
| C(dose)[T.1] | 54.7604 | 9.536 | 5.743 | 0.000 | 34.869 74.652 |
| expression | -3.7177 | 9.966 | -0.373 | 0.713 | -24.506 17.071 |
| Omnibus: | 0.458 | Durbin-Watson: | 1.947 |
| Prob(Omnibus): | 0.795 | Jarque-Bera (JB): | 0.556 |
| Skew: | 0.025 | Prob(JB): | 0.757 |
| Kurtosis: | 2.240 | Cond. No. | 84.8 |
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: | 04:17:25 | 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: | 0.077 |
| Model: | OLS | Adj. R-squared: | 0.033 |
| Method: | Least Squares | F-statistic: | 1.748 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.200 |
| Time: | 04:17:25 | Log-Likelihood: | -112.19 |
| No. Observations: | 23 | AIC: | 228.4 |
| Df Residuals: | 21 | BIC: | 230.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -36.6115 | 88.263 | -0.415 | 0.682 | -220.165 146.942 |
| expression | 19.1788 | 14.507 | 1.322 | 0.200 | -10.990 49.347 |
| Omnibus: | 2.977 | Durbin-Watson: | 2.388 |
| Prob(Omnibus): | 0.226 | Jarque-Bera (JB): | 2.320 |
| Skew: | 0.648 | Prob(JB): | 0.313 |
| Kurtosis: | 2.139 | Cond. No. | 79.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.397 | 0.541 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.559 |
| Model: | OLS | Adj. R-squared: | 0.439 |
| Method: | Least Squares | F-statistic: | 4.649 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0247 |
| Time: | 04:17:25 | Log-Likelihood: | -69.158 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 11 | BIC: | 149.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -0.7515 | 152.880 | -0.005 | 0.996 | -337.237 335.735 |
| C(dose)[T.1] | 432.5802 | 249.812 | 1.732 | 0.111 | -117.252 982.412 |
| expression | 13.8499 | 30.979 | 0.447 | 0.663 | -54.334 82.034 |
| expression:C(dose)[T.1] | -74.5119 | 49.007 | -1.520 | 0.157 | -182.376 33.352 |
| Omnibus: | 11.331 | Durbin-Watson: | 0.843 |
| Prob(Omnibus): | 0.003 | Jarque-Bera (JB): | 7.445 |
| Skew: | -1.508 | Prob(JB): | 0.0242 |
| Kurtosis: | 4.679 | Cond. No. | 229. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.466 |
| Model: | OLS | Adj. R-squared: | 0.377 |
| Method: | Least Squares | F-statistic: | 5.245 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0231 |
| Time: | 04:17:25 | Log-Likelihood: | -70.589 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 145.8197 | 124.972 | 1.167 | 0.266 | -126.471 418.111 |
| C(dose)[T.1] | 53.5479 | 16.957 | 3.158 | 0.008 | 16.602 90.494 |
| expression | -15.9241 | 25.282 | -0.630 | 0.541 | -71.009 39.161 |
| Omnibus: | 4.591 | Durbin-Watson: | 0.939 |
| Prob(Omnibus): | 0.101 | Jarque-Bera (JB): | 2.825 |
| Skew: | -1.063 | Prob(JB): | 0.243 |
| Kurtosis: | 3.049 | Cond. No. | 86.0 |
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: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 04:17:25 | 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: | 0.023 |
| Model: | OLS | Adj. R-squared: | -0.052 |
| Method: | Least Squares | F-statistic: | 0.3060 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.590 |
| Time: | 04:17:25 | Log-Likelihood: | -75.126 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 9.5094 | 152.473 | 0.062 | 0.951 | -319.888 338.906 |
| expression | 16.6039 | 30.017 | 0.553 | 0.590 | -48.244 81.451 |
| Omnibus: | 1.551 | Durbin-Watson: | 1.490 |
| Prob(Omnibus): | 0.460 | Jarque-Bera (JB): | 0.907 |
| Skew: | 0.196 | Prob(JB): | 0.636 |
| Kurtosis: | 1.861 | Cond. No. | 80.1 |