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 |
| 1.390 | 0.252 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.757 |
| Model: | OLS | Adj. R-squared: | 0.718 |
| Method: | Least Squares | F-statistic: | 19.70 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 4.70e-06 |
| Time: | 23:02:21 | Log-Likelihood: | -96.848 |
| No. Observations: | 23 | AIC: | 201.7 |
| Df Residuals: | 19 | BIC: | 206.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -158.5912 | 160.879 | -0.986 | 0.337 | -495.316 178.133 |
| C(dose)[T.1] | 542.7790 | 193.406 | 2.806 | 0.011 | 137.977 947.581 |
| expression | 25.7113 | 19.428 | 1.323 | 0.201 | -14.952 66.375 |
| expression:C(dose)[T.1] | -61.4039 | 23.849 | -2.575 | 0.019 | -111.321 -11.487 |
| Omnibus: | 0.631 | Durbin-Watson: | 1.955 |
| Prob(Omnibus): | 0.730 | Jarque-Bera (JB): | 0.678 |
| Skew: | 0.187 | Prob(JB): | 0.712 |
| Kurtosis: | 2.246 | Cond. No. | 587. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.672 |
| Model: | OLS | Adj. R-squared: | 0.639 |
| Method: | Least Squares | F-statistic: | 20.47 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.45e-05 |
| Time: | 23:02:21 | Log-Likelihood: | -100.29 |
| No. Observations: | 23 | AIC: | 206.6 |
| Df Residuals: | 20 | BIC: | 210.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 178.6587 | 105.735 | 1.690 | 0.107 | -41.901 399.218 |
| C(dose)[T.1] | 45.4311 | 10.812 | 4.202 | 0.000 | 22.878 67.984 |
| expression | -15.0366 | 12.756 | -1.179 | 0.252 | -41.645 11.571 |
| Omnibus: | 1.501 | Durbin-Watson: | 2.113 |
| Prob(Omnibus): | 0.472 | Jarque-Bera (JB): | 1.335 |
| Skew: | 0.477 | Prob(JB): | 0.513 |
| Kurtosis: | 2.306 | Cond. No. | 204. |
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: | 23:02:21 | 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: | 0.382 |
| Model: | OLS | Adj. R-squared: | 0.353 |
| Method: | Least Squares | F-statistic: | 12.99 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00167 |
| Time: | 23:02:21 | Log-Likelihood: | -107.57 |
| No. Observations: | 23 | AIC: | 219.1 |
| Df Residuals: | 21 | BIC: | 221.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 467.2093 | 107.666 | 4.339 | 0.000 | 243.306 691.113 |
| expression | -48.2854 | 13.398 | -3.604 | 0.002 | -76.147 -20.424 |
| Omnibus: | 0.791 | Durbin-Watson: | 2.556 |
| Prob(Omnibus): | 0.673 | Jarque-Bera (JB): | 0.704 |
| Skew: | 0.052 | Prob(JB): | 0.703 |
| Kurtosis: | 2.149 | Cond. No. | 155. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 14.195 | 0.003 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.747 |
| Model: | OLS | Adj. R-squared: | 0.679 |
| Method: | Least Squares | F-statistic: | 10.85 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00129 |
| Time: | 23:02:21 | Log-Likelihood: | -64.978 |
| No. Observations: | 15 | AIC: | 138.0 |
| Df Residuals: | 11 | BIC: | 140.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 374.9449 | 158.858 | 2.360 | 0.038 | 25.302 724.588 |
| C(dose)[T.1] | 38.9736 | 186.662 | 0.209 | 0.838 | -371.867 449.814 |
| expression | -37.3230 | 19.255 | -1.938 | 0.079 | -79.703 5.057 |
| expression:C(dose)[T.1] | -0.3618 | 22.895 | -0.016 | 0.988 | -50.754 50.031 |
| Omnibus: | 0.116 | Durbin-Watson: | 1.322 |
| Prob(Omnibus): | 0.944 | Jarque-Bera (JB): | 0.157 |
| Skew: | -0.144 | Prob(JB): | 0.925 |
| Kurtosis: | 2.589 | Cond. No. | 401. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.747 |
| Model: | OLS | Adj. R-squared: | 0.705 |
| Method: | Least Squares | F-statistic: | 17.76 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000259 |
| Time: | 23:02:21 | Log-Likelihood: | -64.978 |
| No. Observations: | 15 | AIC: | 136.0 |
| Df Residuals: | 12 | BIC: | 138.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 377.0531 | 82.548 | 4.568 | 0.001 | 197.197 556.909 |
| C(dose)[T.1] | 36.0301 | 11.212 | 3.214 | 0.007 | 11.602 60.458 |
| expression | -37.5789 | 9.974 | -3.768 | 0.003 | -59.311 -15.847 |
| Omnibus: | 0.114 | Durbin-Watson: | 1.327 |
| Prob(Omnibus): | 0.945 | Jarque-Bera (JB): | 0.162 |
| Skew: | -0.145 | Prob(JB): | 0.922 |
| Kurtosis: | 2.581 | Cond. No. | 128. |
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: | 23:02:22 | 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: | 0.530 |
| Model: | OLS | Adj. R-squared: | 0.494 |
| Method: | Least Squares | F-statistic: | 14.67 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00209 |
| Time: | 23:02:22 | Log-Likelihood: | -69.635 |
| No. Observations: | 15 | AIC: | 143.3 |
| Df Residuals: | 13 | BIC: | 144.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 476.7195 | 100.256 | 4.755 | 0.000 | 260.130 693.309 |
| expression | -47.5696 | 12.420 | -3.830 | 0.002 | -74.402 -20.737 |
| Omnibus: | 0.898 | Durbin-Watson: | 2.320 |
| Prob(Omnibus): | 0.638 | Jarque-Bera (JB): | 0.550 |
| Skew: | 0.444 | Prob(JB): | 0.760 |
| Kurtosis: | 2.699 | Cond. No. | 118. |