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 |
| 0.428 | 0.521 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.602 |
| Method: | Least Squares | F-statistic: | 12.10 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000117 |
| Time: | 04:27:32 | Log-Likelihood: | -100.82 |
| No. Observations: | 23 | AIC: | 209.6 |
| Df Residuals: | 19 | BIC: | 214.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 203.7696 | 329.971 | 0.618 | 0.544 | -486.867 894.406 |
| C(dose)[T.1] | 97.4681 | 539.620 | 0.181 | 0.859 | -1031.969 1226.905 |
| expression | -13.5190 | 29.821 | -0.453 | 0.655 | -75.935 48.897 |
| expression:C(dose)[T.1] | -3.3599 | 47.680 | -0.070 | 0.945 | -103.156 96.436 |
| Omnibus: | 1.847 | Durbin-Watson: | 1.815 |
| Prob(Omnibus): | 0.397 | Jarque-Bera (JB): | 1.032 |
| Skew: | 0.050 | Prob(JB): | 0.597 |
| Kurtosis: | 1.967 | Cond. No. | 1.71e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 19.10 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.29e-05 |
| Time: | 04:27:32 | Log-Likelihood: | -100.82 |
| 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 | 218.3098 | 251.009 | 0.870 | 0.395 | -305.285 741.905 |
| C(dose)[T.1] | 59.4538 | 12.759 | 4.660 | 0.000 | 32.840 86.068 |
| expression | -14.8333 | 22.682 | -0.654 | 0.521 | -62.148 32.481 |
| Omnibus: | 1.763 | Durbin-Watson: | 1.819 |
| Prob(Omnibus): | 0.414 | Jarque-Bera (JB): | 1.007 |
| Skew: | 0.038 | Prob(JB): | 0.604 |
| Kurtosis: | 1.978 | Cond. No. | 659. |
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: | 04:27:32 | 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: | 0.283 |
| Model: | OLS | Adj. R-squared: | 0.249 |
| Method: | Least Squares | F-statistic: | 8.303 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00893 |
| Time: | 04:27:32 | Log-Likelihood: | -109.27 |
| No. Observations: | 23 | AIC: | 222.5 |
| Df Residuals: | 21 | BIC: | 224.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -625.7654 | 244.905 | -2.555 | 0.018 | -1135.073 -116.458 |
| expression | 62.6524 | 21.743 | 2.882 | 0.009 | 17.436 107.869 |
| Omnibus: | 1.960 | Durbin-Watson: | 2.272 |
| Prob(Omnibus): | 0.375 | Jarque-Bera (JB): | 1.475 |
| Skew: | 0.431 | Prob(JB): | 0.478 |
| Kurtosis: | 2.109 | Cond. No. | 455. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.820 | 0.383 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.486 |
| Model: | OLS | Adj. R-squared: | 0.346 |
| Method: | Least Squares | F-statistic: | 3.472 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0543 |
| Time: | 04:27:32 | Log-Likelihood: | -70.303 |
| No. Observations: | 15 | AIC: | 148.6 |
| Df Residuals: | 11 | BIC: | 151.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -198.5250 | 412.639 | -0.481 | 0.640 | -1106.737 709.687 |
| C(dose)[T.1] | -133.0322 | 828.108 | -0.161 | 0.875 | -1955.685 1689.620 |
| expression | 28.5760 | 44.319 | 0.645 | 0.532 | -68.970 126.122 |
| expression:C(dose)[T.1] | 20.0847 | 89.663 | 0.224 | 0.827 | -177.263 217.433 |
| Omnibus: | 1.341 | Durbin-Watson: | 0.796 |
| Prob(Omnibus): | 0.512 | Jarque-Bera (JB): | 1.116 |
| Skew: | -0.553 | Prob(JB): | 0.572 |
| Kurtosis: | 2.249 | Cond. No. | 1.18e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.484 |
| Model: | OLS | Adj. R-squared: | 0.398 |
| Method: | Least Squares | F-statistic: | 5.629 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0189 |
| Time: | 04:27:32 | Log-Likelihood: | -70.337 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 12 | BIC: | 148.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -244.1946 | 344.262 | -0.709 | 0.492 | -994.277 505.887 |
| C(dose)[T.1] | 52.4289 | 15.641 | 3.352 | 0.006 | 18.351 86.507 |
| expression | 33.4830 | 36.971 | 0.906 | 0.383 | -47.069 114.035 |
| Omnibus: | 1.331 | Durbin-Watson: | 0.758 |
| Prob(Omnibus): | 0.514 | Jarque-Bera (JB): | 1.094 |
| Skew: | -0.569 | Prob(JB): | 0.579 |
| Kurtosis: | 2.326 | Cond. No. | 425. |
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: | 04:27:32 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.076 |
| Method: | Least Squares | F-statistic: | 0.01168 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.916 |
| Time: | 04:27:32 | Log-Likelihood: | -75.293 |
| 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 | 45.5203 | 445.521 | 0.102 | 0.920 | -916.970 1008.010 |
| expression | 5.2020 | 48.124 | 0.108 | 0.916 | -98.763 109.167 |
| Omnibus: | 0.922 | Durbin-Watson: | 1.629 |
| Prob(Omnibus): | 0.631 | Jarque-Bera (JB): | 0.692 |
| Skew: | 0.079 | Prob(JB): | 0.708 |
| Kurtosis: | 1.960 | Cond. No. | 411. |