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 |
| 0.166 | 0.688 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.600 |
| Method: | Least Squares | F-statistic: | 11.98 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000124 |
| Time: | 03:41:55 | Log-Likelihood: | -100.89 |
| No. Observations: | 23 | AIC: | 209.8 |
| Df Residuals: | 19 | BIC: | 214.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 85.3494 | 58.597 | 1.457 | 0.162 | -37.297 207.995 |
| C(dose)[T.1] | 22.7577 | 83.175 | 0.274 | 0.787 | -151.330 196.846 |
| expression | -4.8569 | 9.088 | -0.534 | 0.599 | -23.879 14.165 |
| expression:C(dose)[T.1] | 4.7634 | 13.345 | 0.357 | 0.725 | -23.168 32.694 |
| Omnibus: | 0.137 | Durbin-Watson: | 1.819 |
| Prob(Omnibus): | 0.934 | Jarque-Bera (JB): | 0.357 |
| Skew: | 0.037 | Prob(JB): | 0.836 |
| Kurtosis: | 2.394 | Cond. No. | 153. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.617 |
| Method: | Least Squares | F-statistic: | 18.73 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.61e-05 |
| Time: | 03:41:55 | Log-Likelihood: | -100.97 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 71.1840 | 42.163 | 1.688 | 0.107 | -16.766 159.134 |
| C(dose)[T.1] | 52.2595 | 9.127 | 5.726 | 0.000 | 33.222 71.297 |
| expression | -2.6476 | 6.508 | -0.407 | 0.688 | -16.223 10.928 |
| Omnibus: | 0.155 | Durbin-Watson: | 1.897 |
| Prob(Omnibus): | 0.925 | Jarque-Bera (JB): | 0.373 |
| Skew: | 0.024 | Prob(JB): | 0.830 |
| Kurtosis: | 2.378 | Cond. No. | 62.3 |
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: | 03:41:55 | 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: | 0.081 |
| Model: | OLS | Adj. R-squared: | 0.038 |
| Method: | Least Squares | F-statistic: | 1.859 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.187 |
| Time: | 03:41:55 | Log-Likelihood: | -112.13 |
| No. Observations: | 23 | AIC: | 228.3 |
| Df Residuals: | 21 | BIC: | 230.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 163.4206 | 61.776 | 2.645 | 0.015 | 34.950 291.891 |
| expression | -13.4635 | 9.874 | -1.364 | 0.187 | -33.998 7.071 |
| Omnibus: | 2.452 | Durbin-Watson: | 2.317 |
| Prob(Omnibus): | 0.293 | Jarque-Bera (JB): | 1.421 |
| Skew: | 0.315 | Prob(JB): | 0.492 |
| Kurtosis: | 1.958 | Cond. No. | 57.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.887 | 0.365 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.488 |
| Model: | OLS | Adj. R-squared: | 0.349 |
| Method: | Least Squares | F-statistic: | 3.497 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0533 |
| Time: | 03:41:56 | Log-Likelihood: | -70.277 |
| No. Observations: | 15 | AIC: | 148.6 |
| Df Residuals: | 11 | BIC: | 151.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 11.8457 | 121.046 | 0.098 | 0.924 | -254.574 278.266 |
| C(dose)[T.1] | 24.4849 | 157.971 | 0.155 | 0.880 | -323.207 372.177 |
| expression | 7.8576 | 17.034 | 0.461 | 0.654 | -29.633 45.348 |
| expression:C(dose)[T.1] | 3.9390 | 22.583 | 0.174 | 0.865 | -45.766 53.644 |
| Omnibus: | 4.076 | Durbin-Watson: | 1.186 |
| Prob(Omnibus): | 0.130 | Jarque-Bera (JB): | 2.139 |
| Skew: | -0.913 | Prob(JB): | 0.343 |
| Kurtosis: | 3.292 | Cond. No. | 195. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.487 |
| Model: | OLS | Adj. R-squared: | 0.401 |
| Method: | Least Squares | F-statistic: | 5.689 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0183 |
| Time: | 03:41:56 | Log-Likelihood: | -70.298 |
| No. Observations: | 15 | AIC: | 146.6 |
| Df Residuals: | 12 | BIC: | 148.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -4.0064 | 76.655 | -0.052 | 0.959 | -171.023 163.010 |
| C(dose)[T.1] | 51.8950 | 15.456 | 3.358 | 0.006 | 18.219 85.571 |
| expression | 10.0985 | 10.722 | 0.942 | 0.365 | -13.264 33.461 |
| Omnibus: | 3.770 | Durbin-Watson: | 1.199 |
| Prob(Omnibus): | 0.152 | Jarque-Bera (JB): | 2.029 |
| Skew: | -0.896 | Prob(JB): | 0.363 |
| Kurtosis: | 3.185 | Cond. No. | 72.2 |
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: | 03:41:56 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.072 |
| Method: | Least Squares | F-statistic: | 0.05901 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.812 |
| Time: | 03:41:56 | Log-Likelihood: | -75.266 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 69.9289 | 98.241 | 0.712 | 0.489 | -142.308 282.166 |
| expression | 3.4247 | 14.098 | 0.243 | 0.812 | -27.032 33.881 |
| Omnibus: | 0.521 | Durbin-Watson: | 1.756 |
| Prob(Omnibus): | 0.771 | Jarque-Bera (JB): | 0.550 |
| Skew: | 0.043 | Prob(JB): | 0.760 |
| Kurtosis: | 2.066 | Cond. No. | 68.9 |