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 |
| 0.754 | 0.396 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.671 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 12.94 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 7.76e-05 |
| Time: | 07:57:39 | Log-Likelihood: | -100.31 |
| No. Observations: | 23 | AIC: | 208.6 |
| Df Residuals: | 19 | BIC: | 213.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 59.4348 | 85.964 | 0.691 | 0.498 | -120.490 239.360 |
| C(dose)[T.1] | 136.9377 | 116.442 | 1.176 | 0.254 | -106.779 380.654 |
| expression | -0.7740 | 12.699 | -0.061 | 0.952 | -27.353 25.805 |
| expression:C(dose)[T.1] | -13.1455 | 17.657 | -0.744 | 0.466 | -50.103 23.812 |
| Omnibus: | 0.996 | Durbin-Watson: | 1.925 |
| Prob(Omnibus): | 0.608 | Jarque-Bera (JB): | 0.344 |
| Skew: | -0.293 | Prob(JB): | 0.842 |
| Kurtosis: | 3.127 | Cond. No. | 236. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 19.57 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 1.96e-05 |
| Time: | 07:57:39 | Log-Likelihood: | -100.64 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 105.3496 | 59.214 | 1.779 | 0.090 | -18.169 228.868 |
| C(dose)[T.1] | 50.5256 | 9.198 | 5.493 | 0.000 | 31.338 69.713 |
| expression | -7.5734 | 8.725 | -0.868 | 0.396 | -25.772 10.626 |
| Omnibus: | 0.413 | Durbin-Watson: | 1.840 |
| Prob(Omnibus): | 0.813 | Jarque-Bera (JB): | 0.042 |
| Skew: | -0.104 | Prob(JB): | 0.979 |
| Kurtosis: | 3.017 | Cond. No. | 93.3 |
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: | Tue, 04 Nov 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 07:57:39 | 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: | 0.152 |
| Model: | OLS | Adj. R-squared: | 0.111 |
| Method: | Least Squares | F-statistic: | 3.752 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0663 |
| Time: | 07:57:39 | Log-Likelihood: | -111.21 |
| No. Observations: | 23 | AIC: | 226.4 |
| Df Residuals: | 21 | BIC: | 228.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 240.4674 | 83.256 | 2.888 | 0.009 | 67.327 413.608 |
| expression | -24.4481 | 12.622 | -1.937 | 0.066 | -50.696 1.800 |
| Omnibus: | 2.609 | Durbin-Watson: | 2.091 |
| Prob(Omnibus): | 0.271 | Jarque-Bera (JB): | 1.201 |
| Skew: | -0.011 | Prob(JB): | 0.548 |
| Kurtosis: | 1.881 | Cond. No. | 84.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.758 | 0.210 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.525 |
| Model: | OLS | Adj. R-squared: | 0.395 |
| Method: | Least Squares | F-statistic: | 4.046 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0365 |
| Time: | 07:57:39 | Log-Likelihood: | -69.724 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 11 | BIC: | 150.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 155.4949 | 121.690 | 1.278 | 0.228 | -112.343 423.333 |
| C(dose)[T.1] | 110.4852 | 181.996 | 0.607 | 0.556 | -290.085 511.055 |
| expression | -15.1092 | 20.790 | -0.727 | 0.483 | -60.868 30.649 |
| expression:C(dose)[T.1] | -11.0841 | 31.499 | -0.352 | 0.732 | -80.412 58.244 |
| Omnibus: | 6.452 | Durbin-Watson: | 1.247 |
| Prob(Omnibus): | 0.040 | Jarque-Bera (JB): | 3.593 |
| Skew: | -1.145 | Prob(JB): | 0.166 |
| Kurtosis: | 3.707 | Cond. No. | 184. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.519 |
| Model: | OLS | Adj. R-squared: | 0.439 |
| Method: | Least Squares | F-statistic: | 6.479 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0124 |
| Time: | 07:57:39 | Log-Likelihood: | -69.808 |
| No. Observations: | 15 | AIC: | 145.6 |
| Df Residuals: | 12 | BIC: | 147.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 183.6397 | 88.302 | 2.080 | 0.060 | -8.754 376.034 |
| C(dose)[T.1] | 46.6717 | 14.822 | 3.149 | 0.008 | 14.376 78.967 |
| expression | -19.9379 | 15.037 | -1.326 | 0.210 | -52.701 12.826 |
| Omnibus: | 5.982 | Durbin-Watson: | 1.246 |
| Prob(Omnibus): | 0.050 | Jarque-Bera (JB): | 3.366 |
| Skew: | -1.128 | Prob(JB): | 0.186 |
| Kurtosis: | 3.547 | Cond. No. | 72.0 |
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: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 07:57:40 | 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: | 0.122 |
| Model: | OLS | Adj. R-squared: | 0.054 |
| Method: | Least Squares | F-statistic: | 1.806 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.202 |
| Time: | 07:57:40 | Log-Likelihood: | -74.324 |
| No. Observations: | 15 | AIC: | 152.6 |
| Df Residuals: | 13 | BIC: | 154.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 243.5734 | 111.952 | 2.176 | 0.049 | 1.715 485.431 |
| expression | -26.0205 | 19.362 | -1.344 | 0.202 | -67.850 15.809 |
| Omnibus: | 0.069 | Durbin-Watson: | 2.185 |
| Prob(Omnibus): | 0.966 | Jarque-Bera (JB): | 0.282 |
| Skew: | -0.092 | Prob(JB): | 0.868 |
| Kurtosis: | 2.354 | Cond. No. | 70.0 |