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 |
| 0.027 | 0.872 | 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: | 0.623 |
| Method: | Least Squares | F-statistic: | 13.14 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.03e-05 |
| Time: | 04:36:26 | Log-Likelihood: | -100.19 |
| No. Observations: | 23 | AIC: | 208.4 |
| Df Residuals: | 19 | BIC: | 212.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 86.2841 | 48.294 | 1.787 | 0.090 | -14.796 187.365 |
| C(dose)[T.1] | -40.6467 | 77.418 | -0.525 | 0.606 | -202.685 121.392 |
| expression | -9.4041 | 14.050 | -0.669 | 0.511 | -38.810 20.002 |
| expression:C(dose)[T.1] | 26.1566 | 21.509 | 1.216 | 0.239 | -18.862 71.175 |
| Omnibus: | 0.329 | Durbin-Watson: | 1.862 |
| Prob(Omnibus): | 0.848 | Jarque-Bera (JB): | 0.083 |
| Skew: | 0.142 | Prob(JB): | 0.959 |
| Kurtosis: | 2.917 | Cond. No. | 86.5 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.53 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.80e-05 |
| Time: | 04:36:26 | Log-Likelihood: | -101.05 |
| 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 | 48.2178 | 37.214 | 1.296 | 0.210 | -29.409 125.844 |
| C(dose)[T.1] | 52.8373 | 9.284 | 5.691 | 0.000 | 33.471 72.203 |
| expression | 1.7563 | 10.765 | 0.163 | 0.872 | -20.699 24.211 |
| Omnibus: | 0.240 | Durbin-Watson: | 1.927 |
| Prob(Omnibus): | 0.887 | Jarque-Bera (JB): | 0.433 |
| Skew: | 0.070 | Prob(JB): | 0.805 |
| Kurtosis: | 2.342 | Cond. No. | 33.1 |
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, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 04:36:26 | 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: | 0.082 |
| Model: | OLS | Adj. R-squared: | 0.038 |
| Method: | Least Squares | F-statistic: | 1.874 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.185 |
| Time: | 04:36:26 | Log-Likelihood: | -112.12 |
| No. Observations: | 23 | AIC: | 228.2 |
| Df Residuals: | 21 | BIC: | 230.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 1.7817 | 57.348 | 0.031 | 0.976 | -117.480 121.043 |
| expression | 21.9726 | 16.050 | 1.369 | 0.185 | -11.406 55.351 |
| Omnibus: | 1.521 | Durbin-Watson: | 2.594 |
| Prob(Omnibus): | 0.467 | Jarque-Bera (JB): | 0.993 |
| Skew: | 0.157 | Prob(JB): | 0.609 |
| Kurtosis: | 2.032 | Cond. No. | 31.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.561 | 0.468 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.508 |
| Model: | OLS | Adj. R-squared: | 0.374 |
| Method: | Least Squares | F-statistic: | 3.786 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0435 |
| Time: | 04:36:26 | Log-Likelihood: | -69.980 |
| No. Observations: | 15 | AIC: | 148.0 |
| Df Residuals: | 11 | BIC: | 150.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 38.6736 | 109.861 | 0.352 | 0.731 | -203.129 280.477 |
| C(dose)[T.1] | -162.9969 | 241.698 | -0.674 | 0.514 | -694.972 368.978 |
| expression | 8.3930 | 31.895 | 0.263 | 0.797 | -61.807 78.594 |
| expression:C(dose)[T.1] | 62.0152 | 70.465 | 0.880 | 0.398 | -93.077 217.108 |
| Omnibus: | 1.313 | Durbin-Watson: | 0.633 |
| Prob(Omnibus): | 0.519 | Jarque-Bera (JB): | 1.055 |
| Skew: | -0.574 | Prob(JB): | 0.590 |
| Kurtosis: | 2.392 | Cond. No. | 138. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.473 |
| Model: | OLS | Adj. R-squared: | 0.386 |
| Method: | Least Squares | F-statistic: | 5.394 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0213 |
| Time: | 04:36:26 | Log-Likelihood: | -70.490 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 12 | BIC: | 149.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -4.8566 | 97.171 | -0.050 | 0.961 | -216.575 206.861 |
| C(dose)[T.1] | 49.2787 | 15.385 | 3.203 | 0.008 | 15.758 82.799 |
| expression | 21.0987 | 28.172 | 0.749 | 0.468 | -40.283 82.481 |
| Omnibus: | 1.859 | Durbin-Watson: | 0.688 |
| Prob(Omnibus): | 0.395 | Jarque-Bera (JB): | 1.461 |
| Skew: | -0.676 | Prob(JB): | 0.482 |
| Kurtosis: | 2.288 | Cond. No. | 47.7 |
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, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 04:36:26 | 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: | 0.023 |
| Model: | OLS | Adj. R-squared: | -0.052 |
| Method: | Least Squares | F-statistic: | 0.3079 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.588 |
| Time: | 04:36:26 | Log-Likelihood: | -75.125 |
| No. Observations: | 15 | AIC: | 154.2 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 23.6306 | 126.620 | 0.187 | 0.855 | -249.915 297.176 |
| expression | 20.4546 | 36.864 | 0.555 | 0.588 | -59.185 100.094 |
| Omnibus: | 1.930 | Durbin-Watson: | 1.534 |
| Prob(Omnibus): | 0.381 | Jarque-Bera (JB): | 0.939 |
| Skew: | 0.073 | Prob(JB): | 0.625 |
| Kurtosis: | 1.783 | Cond. No. | 47.0 |