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 |
| 0.133 | 0.720 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.597 |
| Method: | Least Squares | F-statistic: | 11.86 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000132 |
| Time: | 23:01:30 | Log-Likelihood: | -100.97 |
| No. Observations: | 23 | AIC: | 209.9 |
| Df Residuals: | 19 | BIC: | 214.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 76.6998 | 69.313 | 1.107 | 0.282 | -68.374 221.773 |
| C(dose)[T.1] | 39.2278 | 78.652 | 0.499 | 0.624 | -125.392 203.848 |
| expression | -6.8499 | 21.025 | -0.326 | 0.748 | -50.856 37.156 |
| expression:C(dose)[T.1] | 4.2852 | 23.822 | 0.180 | 0.859 | -45.575 54.145 |
| Omnibus: | 0.836 | Durbin-Watson: | 1.914 |
| Prob(Omnibus): | 0.658 | Jarque-Bera (JB): | 0.720 |
| Skew: | 0.040 | Prob(JB): | 0.698 |
| Kurtosis: | 2.137 | Cond. No. | 93.0 |
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: | 18.68 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.65e-05 |
| Time: | 23:01:30 | Log-Likelihood: | -100.99 |
| 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 | 65.7395 | 32.234 | 2.039 | 0.055 | -1.499 132.978 |
| C(dose)[T.1] | 53.2839 | 8.742 | 6.095 | 0.000 | 35.048 71.520 |
| expression | -3.5119 | 9.643 | -0.364 | 0.720 | -23.626 16.603 |
| Omnibus: | 0.840 | Durbin-Watson: | 1.892 |
| Prob(Omnibus): | 0.657 | Jarque-Bera (JB): | 0.722 |
| Skew: | 0.047 | Prob(JB): | 0.697 |
| Kurtosis: | 2.137 | Cond. No. | 26.8 |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 23:01:30 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.044 |
| Method: | Least Squares | F-statistic: | 0.07983 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.780 |
| Time: | 23:01:30 | Log-Likelihood: | -113.06 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 94.4406 | 52.605 | 1.795 | 0.087 | -14.957 203.839 |
| expression | -4.4939 | 15.905 | -0.283 | 0.780 | -37.571 28.583 |
| Omnibus: | 3.542 | Durbin-Watson: | 2.499 |
| Prob(Omnibus): | 0.170 | Jarque-Bera (JB): | 1.616 |
| Skew: | 0.289 | Prob(JB): | 0.446 |
| Kurtosis: | 1.837 | Cond. No. | 26.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.674 | 0.220 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.535 |
| Model: | OLS | Adj. R-squared: | 0.409 |
| Method: | Least Squares | F-statistic: | 4.223 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0324 |
| Time: | 23:01:31 | Log-Likelihood: | -69.553 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 11 | BIC: | 149.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 106.6689 | 158.338 | 0.674 | 0.514 | -241.830 455.168 |
| C(dose)[T.1] | -67.5484 | 167.935 | -0.402 | 0.695 | -437.171 302.074 |
| expression | -11.8111 | 47.543 | -0.248 | 0.808 | -116.452 92.830 |
| expression:C(dose)[T.1] | 33.5267 | 49.978 | 0.671 | 0.516 | -76.474 143.528 |
| Omnibus: | 2.211 | Durbin-Watson: | 0.727 |
| Prob(Omnibus): | 0.331 | Jarque-Bera (JB): | 1.667 |
| Skew: | -0.759 | Prob(JB): | 0.435 |
| Kurtosis: | 2.399 | Cond. No. | 135. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.516 |
| Model: | OLS | Adj. R-squared: | 0.436 |
| Method: | Least Squares | F-statistic: | 6.403 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0128 |
| Time: | 23:01:31 | Log-Likelihood: | -69.853 |
| No. Observations: | 15 | AIC: | 145.7 |
| Df Residuals: | 12 | BIC: | 147.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 5.8720 | 48.778 | 0.120 | 0.906 | -100.405 112.150 |
| C(dose)[T.1] | 44.6249 | 15.162 | 2.943 | 0.012 | 11.590 77.660 |
| expression | 18.5281 | 14.320 | 1.294 | 0.220 | -12.672 49.728 |
| Omnibus: | 1.672 | Durbin-Watson: | 0.847 |
| Prob(Omnibus): | 0.433 | Jarque-Bera (JB): | 1.336 |
| Skew: | -0.633 | Prob(JB): | 0.513 |
| Kurtosis: | 2.270 | Cond. No. | 25.3 |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 23:01:31 | 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: | 0.167 |
| Model: | OLS | Adj. R-squared: | 0.103 |
| Method: | Least Squares | F-statistic: | 2.607 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.130 |
| Time: | 23:01:31 | Log-Likelihood: | -73.929 |
| No. Observations: | 15 | AIC: | 151.9 |
| Df Residuals: | 13 | BIC: | 153.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -4.2483 | 61.342 | -0.069 | 0.946 | -136.770 128.273 |
| expression | 28.3489 | 17.556 | 1.615 | 0.130 | -9.578 66.276 |
| Omnibus: | 0.183 | Durbin-Watson: | 1.722 |
| Prob(Omnibus): | 0.912 | Jarque-Bera (JB): | 0.385 |
| Skew: | 0.065 | Prob(JB): | 0.825 |
| Kurtosis: | 2.226 | Cond. No. | 25.0 |