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 |
| 0.273 | 0.607 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.709 |
| Model: | OLS | Adj. R-squared: | 0.663 |
| Method: | Least Squares | F-statistic: | 15.44 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.49e-05 |
| Time: | 05:24:56 | Log-Likelihood: | -98.904 |
| No. Observations: | 23 | AIC: | 205.8 |
| Df Residuals: | 19 | BIC: | 210.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -7.7785 | 161.344 | -0.048 | 0.962 | -345.475 329.918 |
| C(dose)[T.1] | 757.9365 | 368.033 | 2.059 | 0.053 | -12.366 1528.239 |
| expression | 6.7877 | 17.657 | 0.384 | 0.705 | -30.168 43.743 |
| expression:C(dose)[T.1] | -72.7336 | 38.258 | -1.901 | 0.073 | -152.809 7.341 |
| Omnibus: | 1.053 | Durbin-Watson: | 1.760 |
| Prob(Omnibus): | 0.591 | Jarque-Bera (JB): | 0.978 |
| Skew: | 0.435 | Prob(JB): | 0.613 |
| Kurtosis: | 2.485 | Cond. No. | 1.01e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 18.88 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.47e-05 |
| Time: | 05:24:56 | Log-Likelihood: | -100.91 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 133.6974 | 152.226 | 0.878 | 0.390 | -183.841 451.236 |
| C(dose)[T.1] | 58.6668 | 13.412 | 4.374 | 0.000 | 30.689 86.644 |
| expression | -8.7042 | 16.656 | -0.523 | 0.607 | -43.448 26.040 |
| Omnibus: | 1.086 | Durbin-Watson: | 1.810 |
| Prob(Omnibus): | 0.581 | Jarque-Bera (JB): | 0.811 |
| Skew: | 0.054 | Prob(JB): | 0.667 |
| Kurtosis: | 2.086 | Cond. No. | 335. |
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: | 05:24:56 | 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: | 0.323 |
| Model: | OLS | Adj. R-squared: | 0.290 |
| Method: | Least Squares | F-statistic: | 10.00 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00470 |
| Time: | 05:24:56 | Log-Likelihood: | -108.63 |
| No. Observations: | 23 | AIC: | 221.3 |
| Df Residuals: | 21 | BIC: | 223.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -360.3929 | 139.304 | -2.587 | 0.017 | -650.091 -70.694 |
| expression | 46.6957 | 14.767 | 3.162 | 0.005 | 15.987 77.405 |
| Omnibus: | 0.737 | Durbin-Watson: | 2.224 |
| Prob(Omnibus): | 0.692 | Jarque-Bera (JB): | 0.781 |
| Skew: | 0.315 | Prob(JB): | 0.677 |
| Kurtosis: | 2.353 | Cond. No. | 224. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.473 | 0.142 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.544 |
| Model: | OLS | Adj. R-squared: | 0.419 |
| Method: | Least Squares | F-statistic: | 4.370 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0295 |
| Time: | 05:24:56 | Log-Likelihood: | -69.415 |
| No. Observations: | 15 | AIC: | 146.8 |
| Df Residuals: | 11 | BIC: | 149.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -107.7653 | 293.015 | -0.368 | 0.720 | -752.688 537.157 |
| C(dose)[T.1] | 2.2602 | 333.918 | 0.007 | 0.995 | -732.689 737.209 |
| expression | 20.6848 | 34.572 | 0.598 | 0.562 | -55.407 96.777 |
| expression:C(dose)[T.1] | 5.4698 | 39.361 | 0.139 | 0.892 | -81.163 92.102 |
| Omnibus: | 0.137 | Durbin-Watson: | 1.225 |
| Prob(Omnibus): | 0.934 | Jarque-Bera (JB): | 0.355 |
| Skew: | 0.017 | Prob(JB): | 0.837 |
| Kurtosis: | 2.247 | Cond. No. | 578. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.543 |
| Model: | OLS | Adj. R-squared: | 0.467 |
| Method: | Least Squares | F-statistic: | 7.128 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00912 |
| Time: | 05:24:57 | Log-Likelihood: | -69.428 |
| No. Observations: | 15 | AIC: | 144.9 |
| Df Residuals: | 12 | BIC: | 147.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -143.5051 | 134.546 | -1.067 | 0.307 | -436.656 149.646 |
| C(dose)[T.1] | 48.6165 | 14.337 | 3.391 | 0.005 | 17.379 79.854 |
| expression | 24.9045 | 15.837 | 1.573 | 0.142 | -9.602 59.411 |
| Omnibus: | 0.169 | Durbin-Watson: | 1.218 |
| Prob(Omnibus): | 0.919 | Jarque-Bera (JB): | 0.376 |
| Skew: | 0.044 | Prob(JB): | 0.829 |
| Kurtosis: | 2.229 | Cond. No. | 162. |
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: | 05:24:57 | 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: | 0.105 |
| Model: | OLS | Adj. R-squared: | 0.036 |
| Method: | Least Squares | F-statistic: | 1.525 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.239 |
| Time: | 05:24:57 | Log-Likelihood: | -74.468 |
| No. Observations: | 15 | AIC: | 152.9 |
| Df Residuals: | 13 | BIC: | 154.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -129.2950 | 180.806 | -0.715 | 0.487 | -519.903 261.313 |
| expression | 26.2861 | 21.286 | 1.235 | 0.239 | -19.700 72.272 |
| Omnibus: | 0.554 | Durbin-Watson: | 1.752 |
| Prob(Omnibus): | 0.758 | Jarque-Bera (JB): | 0.595 |
| Skew: | 0.349 | Prob(JB): | 0.743 |
| Kurtosis: | 2.318 | Cond. No. | 162. |