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 |
| 0.458 | 0.506 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.720 |
| Model: | OLS | Adj. R-squared: | 0.676 |
| Method: | Least Squares | F-statistic: | 16.32 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 1.73e-05 |
| Time: | 18:55:21 | Log-Likelihood: | -98.450 |
| No. Observations: | 23 | AIC: | 204.9 |
| Df Residuals: | 19 | BIC: | 209.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 203.5775 | 105.382 | 1.932 | 0.068 | -16.990 424.145 |
| C(dose)[T.1] | -195.3283 | 120.918 | -1.615 | 0.123 | -448.412 57.756 |
| expression | -19.4012 | 13.669 | -1.419 | 0.172 | -48.010 9.208 |
| expression:C(dose)[T.1] | 32.9368 | 15.860 | 2.077 | 0.052 | -0.259 66.132 |
| Omnibus: | 0.963 | Durbin-Watson: | 2.245 |
| Prob(Omnibus): | 0.618 | Jarque-Bera (JB): | 0.591 |
| Skew: | 0.386 | Prob(JB): | 0.744 |
| Kurtosis: | 2.862 | Cond. No. | 334. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 19.15 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 2.26e-05 |
| Time: | 18:55:21 | Log-Likelihood: | -100.80 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 15.2257 | 57.935 | 0.263 | 0.795 | -105.625 136.077 |
| C(dose)[T.1] | 55.1753 | 9.087 | 6.072 | 0.000 | 36.220 74.130 |
| expression | 5.0634 | 7.485 | 0.676 | 0.506 | -10.549 20.676 |
| Omnibus: | 0.146 | Durbin-Watson: | 1.856 |
| Prob(Omnibus): | 0.930 | Jarque-Bera (JB): | 0.335 |
| Skew: | 0.134 | Prob(JB): | 0.846 |
| Kurtosis: | 2.473 | Cond. No. | 103. |
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: | 18:55:21 | 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: | 0.024 |
| Model: | OLS | Adj. R-squared: | -0.022 |
| Method: | Least Squares | F-statistic: | 0.5262 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.476 |
| Time: | 18:55:21 | Log-Likelihood: | -112.82 |
| No. Observations: | 23 | AIC: | 229.6 |
| Df Residuals: | 21 | BIC: | 231.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 143.8771 | 88.734 | 1.621 | 0.120 | -40.655 328.410 |
| expression | -8.5258 | 11.753 | -0.725 | 0.476 | -32.968 15.916 |
| Omnibus: | 4.190 | Durbin-Watson: | 2.404 |
| Prob(Omnibus): | 0.123 | Jarque-Bera (JB): | 2.084 |
| Skew: | 0.453 | Prob(JB): | 0.353 |
| Kurtosis: | 1.837 | Cond. No. | 95.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.291 | 0.095 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.571 |
| Model: | OLS | Adj. R-squared: | 0.454 |
| Method: | Least Squares | F-statistic: | 4.881 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0214 |
| Time: | 18:55:21 | Log-Likelihood: | -68.952 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 11 | BIC: | 148.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 276.3484 | 138.849 | 1.990 | 0.072 | -29.256 581.953 |
| C(dose)[T.1] | 162.0318 | 374.209 | 0.433 | 0.673 | -661.596 985.660 |
| expression | -24.2925 | 16.098 | -1.509 | 0.159 | -59.724 11.139 |
| expression:C(dose)[T.1] | -13.3167 | 43.676 | -0.305 | 0.766 | -109.448 82.814 |
| Omnibus: | 7.393 | Durbin-Watson: | 0.791 |
| Prob(Omnibus): | 0.025 | Jarque-Bera (JB): | 4.002 |
| Skew: | -1.087 | Prob(JB): | 0.135 |
| Kurtosis: | 4.295 | Cond. No. | 528. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.567 |
| Model: | OLS | Adj. R-squared: | 0.495 |
| Method: | Least Squares | F-statistic: | 7.870 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00655 |
| Time: | 18:55:21 | Log-Likelihood: | -69.015 |
| No. Observations: | 15 | AIC: | 144.0 |
| Df Residuals: | 12 | BIC: | 146.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 291.9063 | 124.157 | 2.351 | 0.037 | 21.392 562.421 |
| C(dose)[T.1] | 48.0228 | 13.958 | 3.440 | 0.005 | 17.610 78.435 |
| expression | -26.1015 | 14.388 | -1.814 | 0.095 | -57.450 5.247 |
| Omnibus: | 5.939 | Durbin-Watson: | 0.819 |
| Prob(Omnibus): | 0.051 | Jarque-Bera (JB): | 3.023 |
| Skew: | -1.003 | Prob(JB): | 0.221 |
| Kurtosis: | 3.902 | Cond. No. | 156. |
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: | 18:55:21 | 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: | 0.141 |
| Model: | OLS | Adj. R-squared: | 0.075 |
| Method: | Least Squares | F-statistic: | 2.129 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.168 |
| Time: | 18:55:21 | Log-Likelihood: | -74.163 |
| No. Observations: | 15 | AIC: | 152.3 |
| Df Residuals: | 13 | BIC: | 153.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 337.1947 | 167.173 | 2.017 | 0.065 | -23.961 698.350 |
| expression | -28.3958 | 19.462 | -1.459 | 0.168 | -70.440 13.649 |
| Omnibus: | 2.967 | Durbin-Watson: | 1.921 |
| Prob(Omnibus): | 0.227 | Jarque-Bera (JB): | 1.254 |
| Skew: | 0.274 | Prob(JB): | 0.534 |
| Kurtosis: | 1.694 | Cond. No. | 155. |