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 |
| 0.174 | 0.681 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.597 |
| Method: | Least Squares | F-statistic: | 11.87 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000132 |
| Time: | 03:36:26 | Log-Likelihood: | -100.96 |
| No. Observations: | 23 | AIC: | 209.9 |
| Df Residuals: | 19 | BIC: | 214.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 83.5442 | 82.531 | 1.012 | 0.324 | -89.196 256.284 |
| C(dose)[T.1] | 66.3035 | 228.448 | 0.290 | 0.775 | -411.844 544.451 |
| expression | -3.2476 | 9.111 | -0.356 | 0.725 | -22.316 15.821 |
| expression:C(dose)[T.1] | -0.6157 | 21.474 | -0.029 | 0.977 | -45.560 44.329 |
| Omnibus: | 0.643 | Durbin-Watson: | 1.816 |
| Prob(Omnibus): | 0.725 | Jarque-Bera (JB): | 0.640 |
| Skew: | 0.012 | Prob(JB): | 0.726 |
| Kurtosis: | 2.183 | Cond. No. | 615. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.617 |
| Method: | Least Squares | F-statistic: | 18.74 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.60e-05 |
| Time: | 03:36:26 | Log-Likelihood: | -100.96 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 84.5454 | 72.889 | 1.160 | 0.260 | -67.499 236.590 |
| C(dose)[T.1] | 59.7739 | 17.714 | 3.374 | 0.003 | 22.823 96.725 |
| expression | -3.3584 | 8.041 | -0.418 | 0.681 | -20.132 13.415 |
| Omnibus: | 0.637 | Durbin-Watson: | 1.818 |
| Prob(Omnibus): | 0.727 | Jarque-Bera (JB): | 0.637 |
| Skew: | 0.009 | Prob(JB): | 0.727 |
| Kurtosis: | 2.185 | Cond. No. | 173. |
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: | 03:36:26 | 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: | 0.454 |
| Model: | OLS | Adj. R-squared: | 0.428 |
| Method: | Least Squares | F-statistic: | 17.46 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000424 |
| Time: | 03:36:26 | Log-Likelihood: | -106.15 |
| No. Observations: | 23 | AIC: | 216.3 |
| Df Residuals: | 21 | BIC: | 218.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -121.7717 | 48.511 | -2.510 | 0.020 | -222.655 -20.888 |
| expression | 20.2504 | 4.846 | 4.179 | 0.000 | 10.173 30.328 |
| Omnibus: | 1.248 | Durbin-Watson: | 2.320 |
| Prob(Omnibus): | 0.536 | Jarque-Bera (JB): | 1.007 |
| Skew: | 0.276 | Prob(JB): | 0.605 |
| Kurtosis: | 2.137 | Cond. No. | 92.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.207 | 0.163 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.551 |
| Model: | OLS | Adj. R-squared: | 0.428 |
| Method: | Least Squares | F-statistic: | 4.492 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0273 |
| Time: | 03:36:26 | Log-Likelihood: | -69.301 |
| No. Observations: | 15 | AIC: | 146.6 |
| Df Residuals: | 11 | BIC: | 149.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 29.8471 | 72.081 | 0.414 | 0.687 | -128.803 188.497 |
| C(dose)[T.1] | -29.6588 | 106.870 | -0.278 | 0.787 | -264.878 205.561 |
| expression | 6.0003 | 11.378 | 0.527 | 0.608 | -19.042 31.042 |
| expression:C(dose)[T.1] | 9.7912 | 15.559 | 0.629 | 0.542 | -24.453 44.036 |
| Omnibus: | 0.167 | Durbin-Watson: | 1.251 |
| Prob(Omnibus): | 0.920 | Jarque-Bera (JB): | 0.190 |
| Skew: | -0.183 | Prob(JB): | 0.910 |
| Kurtosis: | 2.587 | Cond. No. | 138. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.534 |
| Model: | OLS | Adj. R-squared: | 0.457 |
| Method: | Least Squares | F-statistic: | 6.887 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0102 |
| Time: | 03:36:26 | Log-Likelihood: | -69.567 |
| No. Observations: | 15 | AIC: | 145.1 |
| Df Residuals: | 12 | BIC: | 147.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -2.9475 | 48.531 | -0.061 | 0.953 | -108.687 102.792 |
| C(dose)[T.1] | 36.7227 | 16.725 | 2.196 | 0.049 | 0.282 73.164 |
| expression | 11.2364 | 7.563 | 1.486 | 0.163 | -5.241 27.714 |
| Omnibus: | 0.506 | Durbin-Watson: | 1.101 |
| Prob(Omnibus): | 0.777 | Jarque-Bera (JB): | 0.533 |
| Skew: | -0.348 | Prob(JB): | 0.766 |
| Kurtosis: | 2.393 | Cond. No. | 48.3 |
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: | 03:36:26 | 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: | 0.347 |
| Model: | OLS | Adj. R-squared: | 0.297 |
| Method: | Least Squares | F-statistic: | 6.920 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0208 |
| Time: | 03:36:26 | Log-Likelihood: | -72.099 |
| No. Observations: | 15 | AIC: | 148.2 |
| Df Residuals: | 13 | BIC: | 149.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -40.5029 | 51.662 | -0.784 | 0.447 | -152.111 71.105 |
| expression | 19.5716 | 7.440 | 2.631 | 0.021 | 3.498 35.645 |
| Omnibus: | 0.331 | Durbin-Watson: | 1.816 |
| Prob(Omnibus): | 0.848 | Jarque-Bera (JB): | 0.299 |
| Skew: | 0.273 | Prob(JB): | 0.861 |
| Kurtosis: | 2.577 | Cond. No. | 44.6 |