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 |
| 0.069 | 0.795 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.682 |
| Model: | OLS | Adj. R-squared: | 0.632 |
| Method: | Least Squares | F-statistic: | 13.60 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 5.67e-05 |
| Time: | 19:06:41 | Log-Likelihood: | -99.919 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 19 | BIC: | 212.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 93.4000 | 37.899 | 2.464 | 0.023 | 14.077 172.723 |
| C(dose)[T.1] | -25.6663 | 57.060 | -0.450 | 0.658 | -145.093 93.761 |
| expression | -8.3256 | 7.952 | -1.047 | 0.308 | -24.970 8.318 |
| expression:C(dose)[T.1] | 18.2888 | 13.221 | 1.383 | 0.183 | -9.383 45.960 |
| Omnibus: | 0.990 | Durbin-Watson: | 1.521 |
| Prob(Omnibus): | 0.609 | Jarque-Bera (JB): | 0.956 |
| Skew: | 0.356 | Prob(JB): | 0.620 |
| Kurtosis: | 2.300 | Cond. No. | 75.8 |
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: | 18.59 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 2.74e-05 |
| Time: | 19:06:41 | Log-Likelihood: | -101.02 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 62.2538 | 31.174 | 1.997 | 0.060 | -2.774 127.282 |
| C(dose)[T.1] | 52.1211 | 9.900 | 5.265 | 0.000 | 31.470 72.772 |
| expression | -1.7091 | 6.496 | -0.263 | 0.795 | -15.260 11.842 |
| Omnibus: | 0.089 | Durbin-Watson: | 1.874 |
| Prob(Omnibus): | 0.957 | Jarque-Bera (JB): | 0.300 |
| Skew: | 0.080 | Prob(JB): | 0.861 |
| Kurtosis: | 2.464 | Cond. No. | 33.8 |
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: | 19:06:41 | 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: | 0.166 |
| Model: | OLS | Adj. R-squared: | 0.126 |
| Method: | Least Squares | F-statistic: | 4.167 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0540 |
| Time: | 19:06:41 | Log-Likelihood: | -111.02 |
| No. Observations: | 23 | AIC: | 226.0 |
| Df Residuals: | 21 | BIC: | 228.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 156.9142 | 38.389 | 4.087 | 0.001 | 77.080 236.749 |
| expression | -17.6770 | 8.660 | -2.041 | 0.054 | -35.686 0.332 |
| Omnibus: | 2.326 | Durbin-Watson: | 2.161 |
| Prob(Omnibus): | 0.313 | Jarque-Bera (JB): | 1.851 |
| Skew: | 0.556 | Prob(JB): | 0.396 |
| Kurtosis: | 2.167 | Cond. No. | 27.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.842 | 0.377 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.500 |
| Model: | OLS | Adj. R-squared: | 0.364 |
| Method: | Least Squares | F-statistic: | 3.673 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0471 |
| Time: | 19:06:41 | Log-Likelihood: | -70.095 |
| No. Observations: | 15 | AIC: | 148.2 |
| Df Residuals: | 11 | BIC: | 151.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 209.9032 | 149.894 | 1.400 | 0.189 | -120.010 539.817 |
| C(dose)[T.1] | -50.0297 | 175.307 | -0.285 | 0.781 | -435.879 335.819 |
| expression | -27.0725 | 28.399 | -0.953 | 0.361 | -89.579 35.434 |
| expression:C(dose)[T.1] | 19.2056 | 32.805 | 0.585 | 0.570 | -52.999 91.410 |
| Omnibus: | 0.955 | Durbin-Watson: | 0.821 |
| Prob(Omnibus): | 0.620 | Jarque-Bera (JB): | 0.837 |
| Skew: | -0.365 | Prob(JB): | 0.658 |
| Kurtosis: | 2.102 | Cond. No. | 187. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.485 |
| Model: | OLS | Adj. R-squared: | 0.399 |
| Method: | Least Squares | F-statistic: | 5.648 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0187 |
| Time: | 19:06:41 | Log-Likelihood: | -70.325 |
| No. Observations: | 15 | AIC: | 146.6 |
| Df Residuals: | 12 | BIC: | 148.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 134.1569 | 73.582 | 1.823 | 0.093 | -26.163 294.477 |
| C(dose)[T.1] | 52.1738 | 15.557 | 3.354 | 0.006 | 18.277 86.071 |
| expression | -12.6795 | 13.821 | -0.917 | 0.377 | -42.794 17.435 |
| Omnibus: | 1.019 | Durbin-Watson: | 0.935 |
| Prob(Omnibus): | 0.601 | Jarque-Bera (JB): | 0.810 |
| Skew: | -0.283 | Prob(JB): | 0.667 |
| Kurtosis: | 2.012 | Cond. No. | 54.6 |
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: | 19:06:41 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.075 |
| Method: | Least Squares | F-statistic: | 0.02773 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.870 |
| Time: | 19:06:41 | Log-Likelihood: | -75.284 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 109.8841 | 97.919 | 1.122 | 0.282 | -101.657 321.426 |
| expression | -3.0099 | 18.076 | -0.167 | 0.870 | -42.060 36.040 |
| Omnibus: | 0.737 | Durbin-Watson: | 1.634 |
| Prob(Omnibus): | 0.692 | Jarque-Bera (JB): | 0.641 |
| Skew: | 0.120 | Prob(JB): | 0.726 |
| Kurtosis: | 2.016 | Cond. No. | 54.0 |