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 |
| 0.340 | 0.567 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.663 |
| Model: | OLS | Adj. R-squared: | 0.610 |
| Method: | Least Squares | F-statistic: | 12.47 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 9.74e-05 |
| Time: | 17:18:20 | Log-Likelihood: | -100.59 |
| No. Observations: | 23 | AIC: | 209.2 |
| Df Residuals: | 19 | BIC: | 213.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 61.2646 | 42.487 | 1.442 | 0.166 | -27.663 150.192 |
| C(dose)[T.1] | 132.0462 | 106.656 | 1.238 | 0.231 | -91.187 355.279 |
| expression | -1.2383 | 7.379 | -0.168 | 0.869 | -16.683 14.206 |
| expression:C(dose)[T.1] | -10.3659 | 15.130 | -0.685 | 0.502 | -42.033 21.301 |
| Omnibus: | 2.339 | Durbin-Watson: | 1.719 |
| Prob(Omnibus): | 0.311 | Jarque-Bera (JB): | 1.259 |
| Skew: | 0.214 | Prob(JB): | 0.533 |
| Kurtosis: | 1.936 | Cond. No. | 198. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.655 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 18.98 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 2.39e-05 |
| Time: | 17:18:21 | Log-Likelihood: | -100.87 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 75.3151 | 36.714 | 2.051 | 0.054 | -1.268 151.898 |
| C(dose)[T.1] | 59.6066 | 13.833 | 4.309 | 0.000 | 30.751 88.462 |
| expression | -3.7041 | 6.356 | -0.583 | 0.567 | -16.962 9.554 |
| Omnibus: | 1.106 | Durbin-Watson: | 1.737 |
| Prob(Omnibus): | 0.575 | Jarque-Bera (JB): | 0.813 |
| Skew: | 0.025 | Prob(JB): | 0.666 |
| Kurtosis: | 2.080 | Cond. No. | 59.4 |
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, 28 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 17:18:21 | 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: | 0.335 |
| Model: | OLS | Adj. R-squared: | 0.303 |
| Method: | Least Squares | F-statistic: | 10.56 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00384 |
| Time: | 17:18:21 | Log-Likelihood: | -108.42 |
| No. Observations: | 23 | AIC: | 220.8 |
| Df Residuals: | 21 | BIC: | 223.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -34.7838 | 35.727 | -0.974 | 0.341 | -109.082 39.514 |
| expression | 17.5945 | 5.415 | 3.249 | 0.004 | 6.334 28.855 |
| Omnibus: | 2.132 | Durbin-Watson: | 2.496 |
| Prob(Omnibus): | 0.344 | Jarque-Bera (JB): | 1.327 |
| Skew: | 0.308 | Prob(JB): | 0.515 |
| Kurtosis: | 1.997 | Cond. No. | 40.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.127 | 0.728 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.462 |
| Model: | OLS | Adj. R-squared: | 0.315 |
| Method: | Least Squares | F-statistic: | 3.150 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0687 |
| Time: | 17:18:21 | Log-Likelihood: | -70.649 |
| No. Observations: | 15 | AIC: | 149.3 |
| Df Residuals: | 11 | BIC: | 152.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 96.0841 | 179.273 | 0.536 | 0.603 | -298.494 490.662 |
| C(dose)[T.1] | -37.5571 | 214.304 | -0.175 | 0.864 | -509.237 434.123 |
| expression | -7.9630 | 49.708 | -0.160 | 0.876 | -117.370 101.444 |
| expression:C(dose)[T.1] | 22.8168 | 58.001 | 0.393 | 0.702 | -104.842 150.476 |
| Omnibus: | 1.754 | Durbin-Watson: | 0.992 |
| Prob(Omnibus): | 0.416 | Jarque-Bera (JB): | 1.389 |
| Skew: | -0.654 | Prob(JB): | 0.499 |
| Kurtosis: | 2.287 | Cond. No. | 159. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.364 |
| Method: | Least Squares | F-statistic: | 5.000 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0263 |
| Time: | 17:18:21 | Log-Likelihood: | -70.754 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 35.7757 | 89.599 | 0.399 | 0.697 | -159.444 230.995 |
| C(dose)[T.1] | 46.4459 | 17.458 | 2.660 | 0.021 | 8.408 84.483 |
| expression | 8.7959 | 24.695 | 0.356 | 0.728 | -45.009 62.601 |
| Omnibus: | 1.965 | Durbin-Watson: | 0.780 |
| Prob(Omnibus): | 0.374 | Jarque-Bera (JB): | 1.522 |
| Skew: | -0.705 | Prob(JB): | 0.467 |
| Kurtosis: | 2.333 | Cond. No. | 47.1 |
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, 28 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 17:18:21 | 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: | 0.133 |
| Model: | OLS | Adj. R-squared: | 0.066 |
| Method: | Least Squares | F-statistic: | 1.991 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.182 |
| Time: | 17:18:21 | Log-Likelihood: | -74.231 |
| No. Observations: | 15 | AIC: | 152.5 |
| Df Residuals: | 13 | BIC: | 153.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -48.8793 | 101.467 | -0.482 | 0.638 | -268.085 170.327 |
| expression | 37.8570 | 26.830 | 1.411 | 0.182 | -20.105 95.819 |
| Omnibus: | 0.149 | Durbin-Watson: | 1.263 |
| Prob(Omnibus): | 0.928 | Jarque-Bera (JB): | 0.301 |
| Skew: | 0.186 | Prob(JB): | 0.860 |
| Kurtosis: | 2.413 | Cond. No. | 43.4 |