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 |
| 0.173 | 0.682 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.598 |
| Method: | Least Squares | F-statistic: | 11.89 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000130 |
| Time: | 05:26:29 | Log-Likelihood: | -100.95 |
| No. Observations: | 23 | AIC: | 209.9 |
| Df Residuals: | 19 | BIC: | 214.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 42.3663 | 63.867 | 0.663 | 0.515 | -91.309 176.041 |
| C(dose)[T.1] | 38.3202 | 93.788 | 0.409 | 0.687 | -157.981 234.621 |
| expression | 2.3829 | 12.791 | 0.186 | 0.854 | -24.389 29.155 |
| expression:C(dose)[T.1] | 2.8764 | 18.517 | 0.155 | 0.878 | -35.880 41.633 |
| Omnibus: | 0.309 | Durbin-Watson: | 1.948 |
| Prob(Omnibus): | 0.857 | Jarque-Bera (JB): | 0.478 |
| Skew: | 0.076 | Prob(JB): | 0.787 |
| Kurtosis: | 2.310 | Cond. No. | 141. |
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: | 05:26:29 | 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 | 35.5453 | 45.232 | 0.786 | 0.441 | -58.808 129.898 |
| C(dose)[T.1] | 52.8213 | 8.820 | 5.989 | 0.000 | 34.424 71.219 |
| expression | 3.7554 | 9.020 | 0.416 | 0.682 | -15.061 22.571 |
| Omnibus: | 0.260 | Durbin-Watson: | 1.928 |
| Prob(Omnibus): | 0.878 | Jarque-Bera (JB): | 0.447 |
| Skew: | 0.091 | Prob(JB): | 0.800 |
| Kurtosis: | 2.342 | Cond. No. | 54.7 |
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: | 05:26:29 | 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: | 0.028 |
| Model: | OLS | Adj. R-squared: | -0.018 |
| Method: | Least Squares | F-statistic: | 0.6065 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.445 |
| Time: | 05:26:29 | Log-Likelihood: | -112.78 |
| No. Observations: | 23 | AIC: | 229.6 |
| Df Residuals: | 21 | BIC: | 231.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 22.5954 | 73.694 | 0.307 | 0.762 | -130.659 175.850 |
| expression | 11.3443 | 14.567 | 0.779 | 0.445 | -18.950 41.638 |
| Omnibus: | 3.859 | Durbin-Watson: | 2.490 |
| Prob(Omnibus): | 0.145 | Jarque-Bera (JB): | 1.667 |
| Skew: | 0.284 | Prob(JB): | 0.435 |
| Kurtosis: | 1.810 | Cond. No. | 54.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.384 | 0.547 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.498 |
| Model: | OLS | Adj. R-squared: | 0.362 |
| Method: | Least Squares | F-statistic: | 3.644 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0481 |
| Time: | 05:26:29 | Log-Likelihood: | -70.125 |
| No. Observations: | 15 | AIC: | 148.2 |
| Df Residuals: | 11 | BIC: | 151.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 78.9255 | 99.391 | 0.794 | 0.444 | -139.833 297.684 |
| C(dose)[T.1] | -78.4754 | 150.140 | -0.523 | 0.612 | -408.932 251.981 |
| expression | -2.5516 | 21.912 | -0.116 | 0.909 | -50.779 45.676 |
| expression:C(dose)[T.1] | 27.5115 | 32.548 | 0.845 | 0.416 | -44.126 99.149 |
| Omnibus: | 4.897 | Durbin-Watson: | 0.763 |
| Prob(Omnibus): | 0.086 | Jarque-Bera (JB): | 2.790 |
| Skew: | -1.047 | Prob(JB): | 0.248 |
| Kurtosis: | 3.278 | Cond. No. | 121. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.466 |
| Model: | OLS | Adj. R-squared: | 0.377 |
| Method: | Least Squares | F-statistic: | 5.233 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0232 |
| Time: | 05:26:30 | Log-Likelihood: | -70.597 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 22.7444 | 73.013 | 0.312 | 0.761 | -136.338 181.827 |
| C(dose)[T.1] | 47.7220 | 15.676 | 3.044 | 0.010 | 13.568 81.876 |
| expression | 9.9170 | 16.009 | 0.619 | 0.547 | -24.963 44.797 |
| Omnibus: | 3.592 | Durbin-Watson: | 0.735 |
| Prob(Omnibus): | 0.166 | Jarque-Bera (JB): | 2.252 |
| Skew: | -0.947 | Prob(JB): | 0.324 |
| Kurtosis: | 2.877 | Cond. No. | 45.9 |
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: | 05:26:30 | 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: | 0.053 |
| Model: | OLS | Adj. R-squared: | -0.020 |
| Method: | Least Squares | F-statistic: | 0.7321 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.408 |
| Time: | 05:26:30 | Log-Likelihood: | -74.889 |
| No. Observations: | 15 | AIC: | 153.8 |
| Df Residuals: | 13 | BIC: | 155.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 14.2687 | 93.321 | 0.153 | 0.881 | -187.339 215.877 |
| expression | 17.3166 | 20.239 | 0.856 | 0.408 | -26.406 61.040 |
| Omnibus: | 0.358 | Durbin-Watson: | 1.512 |
| Prob(Omnibus): | 0.836 | Jarque-Bera (JB): | 0.491 |
| Skew: | -0.169 | Prob(JB): | 0.782 |
| Kurtosis: | 2.181 | Cond. No. | 45.5 |