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 |
| 1.728 | 0.204 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.679 |
| Model: | OLS | Adj. R-squared: | 0.629 |
| Method: | Least Squares | F-statistic: | 13.42 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.18e-05 |
| Time: | 04:43:22 | Log-Likelihood: | -100.03 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 19 | BIC: | 212.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 41.6018 | 51.655 | 0.805 | 0.431 | -66.513 149.717 |
| C(dose)[T.1] | 24.8932 | 60.655 | 0.410 | 0.686 | -102.060 151.846 |
| expression | 3.7561 | 15.288 | 0.246 | 0.809 | -28.242 35.754 |
| expression:C(dose)[T.1] | 6.4015 | 17.124 | 0.374 | 0.713 | -29.440 42.244 |
| Omnibus: | 1.137 | Durbin-Watson: | 1.894 |
| Prob(Omnibus): | 0.566 | Jarque-Bera (JB): | 0.341 |
| Skew: | -0.273 | Prob(JB): | 0.843 |
| Kurtosis: | 3.240 | Cond. No. | 85.2 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.677 |
| Model: | OLS | Adj. R-squared: | 0.645 |
| Method: | Least Squares | F-statistic: | 20.96 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.24e-05 |
| Time: | 04:43:22 | Log-Likelihood: | -100.11 |
| No. Observations: | 23 | AIC: | 206.2 |
| Df Residuals: | 20 | BIC: | 209.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 24.4775 | 23.352 | 1.048 | 0.307 | -24.234 73.189 |
| C(dose)[T.1] | 47.2691 | 9.597 | 4.925 | 0.000 | 27.250 67.288 |
| expression | 8.8581 | 6.738 | 1.315 | 0.204 | -5.197 22.914 |
| Omnibus: | 1.188 | Durbin-Watson: | 1.873 |
| Prob(Omnibus): | 0.552 | Jarque-Bera (JB): | 0.493 |
| Skew: | -0.354 | Prob(JB): | 0.781 |
| Kurtosis: | 3.114 | Cond. No. | 22.8 |
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: | 04:43:22 | 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: | 0.285 |
| Model: | OLS | Adj. R-squared: | 0.251 |
| Method: | Least Squares | F-statistic: | 8.376 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00868 |
| Time: | 04:43:22 | Log-Likelihood: | -109.24 |
| 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 | -11.7227 | 32.179 | -0.364 | 0.719 | -78.643 55.198 |
| expression | 24.8212 | 8.576 | 2.894 | 0.009 | 6.985 42.657 |
| Omnibus: | 0.165 | Durbin-Watson: | 2.406 |
| Prob(Omnibus): | 0.921 | Jarque-Bera (JB): | 0.370 |
| Skew: | -0.106 | Prob(JB): | 0.831 |
| Kurtosis: | 2.415 | Cond. No. | 21.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.155 | 0.168 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.549 |
| Model: | OLS | Adj. R-squared: | 0.426 |
| Method: | Least Squares | F-statistic: | 4.467 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0277 |
| Time: | 04:43:22 | Log-Likelihood: | -69.325 |
| No. Observations: | 15 | AIC: | 146.6 |
| Df Residuals: | 11 | BIC: | 149.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 152.9494 | 58.646 | 2.608 | 0.024 | 23.871 282.028 |
| C(dose)[T.1] | -7.1002 | 83.577 | -0.085 | 0.934 | -191.052 176.852 |
| expression | -15.7693 | 10.627 | -1.484 | 0.166 | -39.159 7.620 |
| expression:C(dose)[T.1] | 9.9857 | 15.740 | 0.634 | 0.539 | -24.659 44.630 |
| Omnibus: | 2.418 | Durbin-Watson: | 1.075 |
| Prob(Omnibus): | 0.298 | Jarque-Bera (JB): | 1.598 |
| Skew: | -0.783 | Prob(JB): | 0.450 |
| Kurtosis: | 2.679 | Cond. No. | 80.7 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.533 |
| Model: | OLS | Adj. R-squared: | 0.455 |
| Method: | Least Squares | F-statistic: | 6.839 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0104 |
| Time: | 04:43:22 | Log-Likelihood: | -69.594 |
| No. Observations: | 15 | AIC: | 145.2 |
| Df Residuals: | 12 | BIC: | 147.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 128.2657 | 42.773 | 2.999 | 0.011 | 35.071 221.461 |
| C(dose)[T.1] | 45.0429 | 14.766 | 3.050 | 0.010 | 12.871 77.215 |
| expression | -11.2178 | 7.642 | -1.468 | 0.168 | -27.868 5.432 |
| Omnibus: | 1.587 | Durbin-Watson: | 0.979 |
| Prob(Omnibus): | 0.452 | Jarque-Bera (JB): | 1.236 |
| Skew: | -0.635 | Prob(JB): | 0.539 |
| Kurtosis: | 2.398 | Cond. No. | 32.9 |
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: | 04:43:22 | 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: | 0.170 |
| Model: | OLS | Adj. R-squared: | 0.106 |
| Method: | Least Squares | F-statistic: | 2.669 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.126 |
| Time: | 04:43:23 | Log-Likelihood: | -73.900 |
| No. Observations: | 15 | AIC: | 151.8 |
| Df Residuals: | 13 | BIC: | 153.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 175.6319 | 51.022 | 3.442 | 0.004 | 65.405 285.859 |
| expression | -15.6848 | 9.602 | -1.634 | 0.126 | -36.428 5.058 |
| Omnibus: | 1.927 | Durbin-Watson: | 1.909 |
| Prob(Omnibus): | 0.382 | Jarque-Bera (JB): | 1.410 |
| Skew: | 0.573 | Prob(JB): | 0.494 |
| Kurtosis: | 2.028 | Cond. No. | 30.3 |