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 |
| 1.381 | 0.254 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.675 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 13.17 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 6.93e-05 |
| Time: | 23:00:39 | Log-Likelihood: | -100.17 |
| No. Observations: | 23 | AIC: | 208.3 |
| Df Residuals: | 19 | BIC: | 212.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 200.7201 | 196.142 | 1.023 | 0.319 | -209.810 611.250 |
| C(dose)[T.1] | 248.7929 | 397.217 | 0.626 | 0.539 | -582.592 1080.178 |
| expression | -15.4081 | 20.618 | -0.747 | 0.464 | -58.562 27.746 |
| expression:C(dose)[T.1] | -18.3498 | 39.842 | -0.461 | 0.650 | -101.740 65.041 |
| Omnibus: | 1.098 | Durbin-Watson: | 1.860 |
| Prob(Omnibus): | 0.578 | Jarque-Bera (JB): | 0.627 |
| Skew: | 0.403 | Prob(JB): | 0.731 |
| Kurtosis: | 2.921 | Cond. No. | 1.08e+03 |
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: | 20.46 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.45e-05 |
| Time: | 23:00:39 | 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 | 247.4464 | 164.525 | 1.504 | 0.148 | -95.747 590.640 |
| C(dose)[T.1] | 65.9627 | 13.688 | 4.819 | 0.000 | 37.411 94.514 |
| expression | -20.3222 | 17.292 | -1.175 | 0.254 | -56.392 15.747 |
| Omnibus: | 0.750 | Durbin-Watson: | 1.862 |
| Prob(Omnibus): | 0.687 | Jarque-Bera (JB): | 0.437 |
| Skew: | 0.330 | Prob(JB): | 0.804 |
| Kurtosis: | 2.862 | Cond. No. | 386. |
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: | 23:00:39 | 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: | 0.291 |
| Model: | OLS | Adj. R-squared: | 0.257 |
| Method: | Least Squares | F-statistic: | 8.600 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00796 |
| Time: | 23:00:39 | Log-Likelihood: | -109.16 |
| No. Observations: | 23 | AIC: | 222.3 |
| Df Residuals: | 21 | BIC: | 224.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -362.3389 | 150.867 | -2.402 | 0.026 | -676.083 -48.595 |
| expression | 45.0808 | 15.373 | 2.932 | 0.008 | 13.111 77.050 |
| Omnibus: | 2.326 | Durbin-Watson: | 2.279 |
| Prob(Omnibus): | 0.313 | Jarque-Bera (JB): | 1.234 |
| Skew: | 0.193 | Prob(JB): | 0.539 |
| Kurtosis: | 1.932 | Cond. No. | 246. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.618 | 0.228 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.517 |
| Model: | OLS | Adj. R-squared: | 0.385 |
| Method: | Least Squares | F-statistic: | 3.920 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0397 |
| Time: | 23:00:39 | Log-Likelihood: | -69.846 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 11 | BIC: | 150.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 278.2689 | 199.861 | 1.392 | 0.191 | -161.622 718.160 |
| C(dose)[T.1] | -19.9639 | 294.464 | -0.068 | 0.947 | -668.076 628.148 |
| expression | -23.7899 | 22.515 | -1.057 | 0.313 | -73.346 25.766 |
| expression:C(dose)[T.1] | 7.8568 | 33.121 | 0.237 | 0.817 | -65.041 80.755 |
| Omnibus: | 2.981 | Durbin-Watson: | 1.162 |
| Prob(Omnibus): | 0.225 | Jarque-Bera (JB): | 2.046 |
| Skew: | -0.887 | Prob(JB): | 0.360 |
| Kurtosis: | 2.640 | Cond. No. | 451. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.514 |
| Model: | OLS | Adj. R-squared: | 0.433 |
| Method: | Least Squares | F-statistic: | 6.352 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0131 |
| Time: | 23:00:39 | Log-Likelihood: | -69.885 |
| No. Observations: | 15 | AIC: | 145.8 |
| Df Residuals: | 12 | BIC: | 147.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 246.0899 | 140.887 | 1.747 | 0.106 | -60.876 553.056 |
| C(dose)[T.1] | 49.7932 | 14.783 | 3.368 | 0.006 | 17.584 82.002 |
| expression | -20.1590 | 15.850 | -1.272 | 0.228 | -54.693 14.375 |
| Omnibus: | 2.817 | Durbin-Watson: | 1.094 |
| Prob(Omnibus): | 0.244 | Jarque-Bera (JB): | 1.917 |
| Skew: | -0.858 | Prob(JB): | 0.383 |
| Kurtosis: | 2.648 | Cond. No. | 172. |
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: | 23:00:40 | 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: | 0.055 |
| Model: | OLS | Adj. R-squared: | -0.018 |
| Method: | Least Squares | F-statistic: | 0.7565 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.400 |
| Time: | 23:00:40 | Log-Likelihood: | -74.876 |
| No. Observations: | 15 | AIC: | 153.8 |
| Df Residuals: | 13 | BIC: | 155.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 257.6012 | 188.745 | 1.365 | 0.195 | -150.157 665.359 |
| expression | -18.4644 | 21.230 | -0.870 | 0.400 | -64.329 27.400 |
| Omnibus: | 1.581 | Durbin-Watson: | 1.987 |
| Prob(Omnibus): | 0.454 | Jarque-Bera (JB): | 0.902 |
| Skew: | 0.175 | Prob(JB): | 0.637 |
| Kurtosis: | 1.851 | Cond. No. | 172. |