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 |
| 0.940 | 0.344 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.672 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 12.97 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.63e-05 |
| Time: | 04:41:27 | Log-Likelihood: | -100.29 |
| No. Observations: | 23 | AIC: | 208.6 |
| Df Residuals: | 19 | BIC: | 213.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 35.8735 | 42.889 | 0.836 | 0.413 | -53.895 125.642 |
| C(dose)[T.1] | 3.0979 | 77.464 | 0.040 | 0.969 | -159.037 165.232 |
| expression | 3.4136 | 7.906 | 0.432 | 0.671 | -13.134 19.961 |
| expression:C(dose)[T.1] | 9.1010 | 14.134 | 0.644 | 0.527 | -20.483 38.685 |
| Omnibus: | 0.063 | Durbin-Watson: | 1.747 |
| Prob(Omnibus): | 0.969 | Jarque-Bera (JB): | 0.263 |
| Skew: | 0.080 | Prob(JB): | 0.877 |
| Kurtosis: | 2.502 | Cond. No. | 121. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.665 |
| Model: | OLS | Adj. R-squared: | 0.631 |
| Method: | Least Squares | F-statistic: | 19.83 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.79e-05 |
| Time: | 04:41:27 | Log-Likelihood: | -100.53 |
| No. Observations: | 23 | AIC: | 207.1 |
| Df Residuals: | 20 | BIC: | 210.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 20.5792 | 35.185 | 0.585 | 0.565 | -52.814 93.973 |
| C(dose)[T.1] | 52.6586 | 8.599 | 6.124 | 0.000 | 34.721 70.596 |
| expression | 6.2611 | 6.457 | 0.970 | 0.344 | -7.208 19.730 |
| Omnibus: | 0.019 | Durbin-Watson: | 1.861 |
| Prob(Omnibus): | 0.990 | Jarque-Bera (JB): | 0.225 |
| Skew: | 0.000 | Prob(JB): | 0.894 |
| Kurtosis: | 2.515 | Cond. No. | 46.6 |
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: | 04:41:27 | 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: | 0.036 |
| Model: | OLS | Adj. R-squared: | -0.010 |
| Method: | Least Squares | F-statistic: | 0.7923 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.384 |
| Time: | 04:41:27 | Log-Likelihood: | -112.68 |
| No. Observations: | 23 | AIC: | 229.4 |
| Df Residuals: | 21 | BIC: | 231.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 28.3151 | 58.183 | 0.487 | 0.632 | -92.682 149.312 |
| expression | 9.4786 | 10.649 | 0.890 | 0.384 | -12.667 31.625 |
| Omnibus: | 2.168 | Durbin-Watson: | 2.292 |
| Prob(Omnibus): | 0.338 | Jarque-Bera (JB): | 1.218 |
| Skew: | 0.214 | Prob(JB): | 0.544 |
| Kurtosis: | 1.957 | Cond. No. | 46.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.240 | 0.633 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.466 |
| Model: | OLS | Adj. R-squared: | 0.321 |
| Method: | Least Squares | F-statistic: | 3.205 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0659 |
| Time: | 04:41:27 | Log-Likelihood: | -70.589 |
| No. Observations: | 15 | AIC: | 149.2 |
| Df Residuals: | 11 | BIC: | 152.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -12.4244 | 133.024 | -0.093 | 0.927 | -305.207 280.358 |
| C(dose)[T.1] | 129.8483 | 206.533 | 0.629 | 0.542 | -324.728 584.425 |
| expression | 13.9108 | 23.082 | 0.603 | 0.559 | -36.892 64.714 |
| expression:C(dose)[T.1] | -14.0604 | 37.477 | -0.375 | 0.715 | -96.546 68.425 |
| Omnibus: | 2.535 | Durbin-Watson: | 0.873 |
| Prob(Omnibus): | 0.282 | Jarque-Bera (JB): | 1.739 |
| Skew: | -0.812 | Prob(JB): | 0.419 |
| Kurtosis: | 2.619 | Cond. No. | 185. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.460 |
| Model: | OLS | Adj. R-squared: | 0.369 |
| Method: | Least Squares | F-statistic: | 5.102 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0249 |
| Time: | 04:41:27 | Log-Likelihood: | -70.685 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 12 | BIC: | 149.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 18.1920 | 101.221 | 0.180 | 0.860 | -202.349 238.733 |
| C(dose)[T.1] | 52.6484 | 17.106 | 3.078 | 0.010 | 15.378 89.919 |
| expression | 8.5773 | 17.521 | 0.490 | 0.633 | -29.598 46.753 |
| Omnibus: | 2.447 | Durbin-Watson: | 0.785 |
| Prob(Omnibus): | 0.294 | Jarque-Bera (JB): | 1.865 |
| Skew: | -0.795 | Prob(JB): | 0.394 |
| Kurtosis: | 2.323 | Cond. No. | 75.0 |
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: | 04:41:27 | 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: | 0.033 |
| Model: | OLS | Adj. R-squared: | -0.041 |
| Method: | Least Squares | F-statistic: | 0.4429 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.517 |
| Time: | 04:41:27 | Log-Likelihood: | -75.049 |
| No. Observations: | 15 | AIC: | 154.1 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 169.1109 | 113.806 | 1.486 | 0.161 | -76.752 414.974 |
| expression | -13.6533 | 20.516 | -0.665 | 0.517 | -57.976 30.669 |
| Omnibus: | 0.171 | Durbin-Watson: | 1.511 |
| Prob(Omnibus): | 0.918 | Jarque-Bera (JB): | 0.376 |
| Skew: | -0.096 | Prob(JB): | 0.829 |
| Kurtosis: | 2.249 | Cond. No. | 65.2 |