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 |
| 0.455 | 0.508 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.603 |
| Method: | Least Squares | F-statistic: | 12.12 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000116 |
| Time: | 06:27:02 | Log-Likelihood: | -100.80 |
| No. Observations: | 23 | AIC: | 209.6 |
| Df Residuals: | 19 | BIC: | 214.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 86.4945 | 95.191 | 0.909 | 0.375 | -112.742 285.731 |
| C(dose)[T.1] | 53.6157 | 111.550 | 0.481 | 0.636 | -179.860 287.092 |
| expression | -5.3195 | 15.651 | -0.340 | 0.738 | -38.077 27.438 |
| expression:C(dose)[T.1] | -0.4394 | 18.693 | -0.024 | 0.981 | -39.565 38.686 |
| Omnibus: | 0.228 | Durbin-Watson: | 1.938 |
| Prob(Omnibus): | 0.892 | Jarque-Bera (JB): | 0.377 |
| Skew: | 0.191 | Prob(JB): | 0.828 |
| Kurtosis: | 2.502 | Cond. No. | 215. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 19.14 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.26e-05 |
| Time: | 06:27:02 | Log-Likelihood: | -100.80 |
| 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 | 88.3640 | 50.981 | 1.733 | 0.098 | -17.981 194.710 |
| C(dose)[T.1] | 51.0033 | 9.336 | 5.463 | 0.000 | 31.528 70.478 |
| expression | -5.6275 | 8.341 | -0.675 | 0.508 | -23.028 11.772 |
| Omnibus: | 0.233 | Durbin-Watson: | 1.944 |
| Prob(Omnibus): | 0.890 | Jarque-Bera (JB): | 0.384 |
| Skew: | 0.190 | Prob(JB): | 0.825 |
| Kurtosis: | 2.494 | Cond. No. | 71.8 |
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: | 06:27:02 | 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: | 0.145 |
| Model: | OLS | Adj. R-squared: | 0.104 |
| Method: | Least Squares | F-statistic: | 3.557 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0732 |
| Time: | 06:27:02 | Log-Likelihood: | -111.31 |
| No. Observations: | 23 | AIC: | 226.6 |
| Df Residuals: | 21 | BIC: | 228.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 211.8861 | 70.395 | 3.010 | 0.007 | 65.491 358.281 |
| expression | -22.5120 | 11.936 | -1.886 | 0.073 | -47.335 2.311 |
| Omnibus: | 1.483 | Durbin-Watson: | 2.685 |
| Prob(Omnibus): | 0.476 | Jarque-Bera (JB): | 1.088 |
| Skew: | 0.519 | Prob(JB): | 0.580 |
| Kurtosis: | 2.757 | Cond. No. | 64.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.030 | 0.865 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.487 |
| Model: | OLS | Adj. R-squared: | 0.347 |
| Method: | Least Squares | F-statistic: | 3.477 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0541 |
| Time: | 06:27:02 | Log-Likelihood: | -70.299 |
| 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 | 241.8709 | 236.643 | 1.022 | 0.329 | -278.977 762.719 |
| C(dose)[T.1] | -177.3186 | 257.375 | -0.689 | 0.505 | -743.797 389.160 |
| expression | -26.7406 | 36.232 | -0.738 | 0.476 | -106.487 53.006 |
| expression:C(dose)[T.1] | 35.0955 | 39.666 | 0.885 | 0.395 | -52.209 122.400 |
| Omnibus: | 2.315 | Durbin-Watson: | 0.968 |
| Prob(Omnibus): | 0.314 | Jarque-Bera (JB): | 1.565 |
| Skew: | -0.770 | Prob(JB): | 0.457 |
| Kurtosis: | 2.637 | Cond. No. | 326. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.450 |
| Model: | OLS | Adj. R-squared: | 0.359 |
| Method: | Least Squares | F-statistic: | 4.912 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0276 |
| Time: | 06:27:02 | Log-Likelihood: | -70.814 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 50.8501 | 96.012 | 0.530 | 0.606 | -158.343 260.043 |
| C(dose)[T.1] | 49.9356 | 16.284 | 3.066 | 0.010 | 14.455 85.416 |
| expression | 2.5414 | 14.612 | 0.174 | 0.865 | -29.296 34.379 |
| Omnibus: | 2.450 | Durbin-Watson: | 0.838 |
| Prob(Omnibus): | 0.294 | Jarque-Bera (JB): | 1.717 |
| Skew: | -0.801 | Prob(JB): | 0.424 |
| Kurtosis: | 2.574 | Cond. No. | 80.5 |
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: | 06:27:02 | 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: | 0.019 |
| Model: | OLS | Adj. R-squared: | -0.056 |
| Method: | Least Squares | F-statistic: | 0.2557 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.622 |
| Time: | 06:27:02 | Log-Likelihood: | -75.154 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 151.9582 | 115.704 | 1.313 | 0.212 | -98.005 401.921 |
| expression | -9.1533 | 18.100 | -0.506 | 0.622 | -48.255 29.949 |
| Omnibus: | 0.875 | Durbin-Watson: | 1.564 |
| Prob(Omnibus): | 0.646 | Jarque-Bera (JB): | 0.675 |
| Skew: | 0.062 | Prob(JB): | 0.714 |
| Kurtosis: | 1.969 | Cond. No. | 75.3 |