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 |
| 0.048 | 0.828 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.713 |
| Model: | OLS | Adj. R-squared: | 0.667 |
| Method: | Least Squares | F-statistic: | 15.70 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 2.23e-05 |
| Time: | 20:15:01 | Log-Likelihood: | -98.768 |
| No. Observations: | 23 | AIC: | 205.5 |
| Df Residuals: | 19 | BIC: | 210.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -139.1384 | 123.435 | -1.127 | 0.274 | -397.492 119.215 |
| C(dose)[T.1] | 419.9438 | 180.403 | 2.328 | 0.031 | 42.356 797.532 |
| expression | 26.3628 | 16.813 | 1.568 | 0.133 | -8.827 61.553 |
| expression:C(dose)[T.1] | -49.8597 | 24.503 | -2.035 | 0.056 | -101.144 1.425 |
| Omnibus: | 1.538 | Durbin-Watson: | 1.847 |
| Prob(Omnibus): | 0.464 | Jarque-Bera (JB): | 0.529 |
| Skew: | -0.324 | Prob(JB): | 0.768 |
| Kurtosis: | 3.362 | Cond. No. | 425. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.56 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 2.77e-05 |
| Time: | 20:15:01 | Log-Likelihood: | -101.04 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 33.0295 | 96.675 | 0.342 | 0.736 | -168.632 234.691 |
| C(dose)[T.1] | 53.2226 | 8.775 | 6.065 | 0.000 | 34.919 71.527 |
| expression | 2.8877 | 13.156 | 0.220 | 0.828 | -24.555 30.330 |
| Omnibus: | 0.355 | Durbin-Watson: | 1.919 |
| Prob(Omnibus): | 0.837 | Jarque-Bera (JB): | 0.508 |
| Skew: | 0.102 | Prob(JB): | 0.776 |
| Kurtosis: | 2.301 | Cond. No. | 166. |
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: | Sun, 09 Nov 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 20:15:01 | 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: | 0.006 |
| Model: | OLS | Adj. R-squared: | -0.041 |
| Method: | Least Squares | F-statistic: | 0.1249 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 0.727 |
| Time: | 20:15:01 | Log-Likelihood: | -113.04 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 23.5887 | 158.957 | 0.148 | 0.883 | -306.981 354.158 |
| expression | 7.6334 | 21.596 | 0.353 | 0.727 | -37.277 52.544 |
| Omnibus: | 2.743 | Durbin-Watson: | 2.497 |
| Prob(Omnibus): | 0.254 | Jarque-Bera (JB): | 1.661 |
| Skew: | 0.407 | Prob(JB): | 0.436 |
| Kurtosis: | 1.966 | Cond. No. | 166. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.066 | 0.802 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.303 |
| Method: | Least Squares | F-statistic: | 3.027 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 0.0753 |
| Time: | 20:15:01 | Log-Likelihood: | -70.786 |
| No. Observations: | 15 | AIC: | 149.6 |
| Df Residuals: | 11 | BIC: | 152.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 46.3332 | 399.706 | 0.116 | 0.910 | -833.413 926.079 |
| C(dose)[T.1] | 9.0297 | 465.755 | 0.019 | 0.985 | -1016.089 1034.149 |
| expression | 2.8259 | 53.520 | 0.053 | 0.959 | -114.970 120.622 |
| expression:C(dose)[T.1] | 5.7294 | 63.061 | 0.091 | 0.929 | -133.067 144.526 |
| Omnibus: | 3.167 | Durbin-Watson: | 0.812 |
| Prob(Omnibus): | 0.205 | Jarque-Bera (JB): | 2.060 |
| Skew: | -0.900 | Prob(JB): | 0.357 |
| Kurtosis: | 2.762 | Cond. No. | 621. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 4.944 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 0.0271 |
| Time: | 20:15:01 | Log-Likelihood: | -70.792 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 15.5266 | 202.708 | 0.077 | 0.940 | -426.136 457.189 |
| C(dose)[T.1] | 51.3122 | 17.733 | 2.894 | 0.013 | 12.676 89.949 |
| expression | 6.9527 | 27.111 | 0.256 | 0.802 | -52.117 66.022 |
| Omnibus: | 3.156 | Durbin-Watson: | 0.812 |
| Prob(Omnibus): | 0.206 | Jarque-Bera (JB): | 2.076 |
| Skew: | -0.902 | Prob(JB): | 0.354 |
| Kurtosis: | 2.740 | Cond. No. | 193. |
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: | Sun, 09 Nov 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 20:15:01 | 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: | 0.069 |
| Model: | OLS | Adj. R-squared: | -0.002 |
| Method: | Least Squares | F-statistic: | 0.9672 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 0.343 |
| Time: | 20:15:02 | Log-Likelihood: | -74.762 |
| No. Observations: | 15 | AIC: | 153.5 |
| Df Residuals: | 13 | BIC: | 154.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 309.4296 | 219.610 | 1.409 | 0.182 | -165.008 783.867 |
| expression | -29.5454 | 30.042 | -0.983 | 0.343 | -94.448 35.357 |
| Omnibus: | 0.707 | Durbin-Watson: | 1.368 |
| Prob(Omnibus): | 0.702 | Jarque-Bera (JB): | 0.643 |
| Skew: | 0.163 | Prob(JB): | 0.725 |
| Kurtosis: | 2.039 | Cond. No. | 167. |