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 |
| 0.002 | 0.968 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.595 |
| Method: | Least Squares | F-statistic: | 11.76 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000139 |
| Time: | 04:45:01 | Log-Likelihood: | -101.03 |
| No. Observations: | 23 | AIC: | 210.1 |
| Df Residuals: | 19 | BIC: | 214.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 32.1808 | 107.354 | 0.300 | 0.768 | -192.514 256.876 |
| C(dose)[T.1] | 81.7123 | 128.985 | 0.634 | 0.534 | -188.257 351.682 |
| expression | 3.4032 | 16.558 | 0.206 | 0.839 | -31.253 38.059 |
| expression:C(dose)[T.1] | -4.3871 | 19.900 | -0.220 | 0.828 | -46.037 37.263 |
| Omnibus: | 0.403 | Durbin-Watson: | 1.831 |
| Prob(Omnibus): | 0.817 | Jarque-Bera (JB): | 0.531 |
| Skew: | 0.058 | Prob(JB): | 0.767 |
| Kurtosis: | 2.265 | Cond. No. | 269. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.50 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.83e-05 |
| Time: | 04:45:01 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 51.8411 | 58.330 | 0.889 | 0.385 | -69.834 173.516 |
| C(dose)[T.1] | 53.3450 | 8.772 | 6.082 | 0.000 | 35.048 71.642 |
| expression | 0.3657 | 8.963 | 0.041 | 0.968 | -18.331 19.062 |
| Omnibus: | 0.323 | Durbin-Watson: | 1.878 |
| Prob(Omnibus): | 0.851 | Jarque-Bera (JB): | 0.485 |
| Skew: | 0.057 | Prob(JB): | 0.785 |
| Kurtosis: | 2.298 | Cond. No. | 88.6 |
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: | 04:45:01 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.003165 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.956 |
| Time: | 04:45:01 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 85.0841 | 95.664 | 0.889 | 0.384 | -113.861 284.029 |
| expression | -0.8305 | 14.761 | -0.056 | 0.956 | -31.528 29.867 |
| Omnibus: | 3.289 | Durbin-Watson: | 2.494 |
| Prob(Omnibus): | 0.193 | Jarque-Bera (JB): | 1.566 |
| Skew: | 0.289 | Prob(JB): | 0.457 |
| Kurtosis: | 1.860 | Cond. No. | 88.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.898 | 0.114 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.565 |
| Model: | OLS | Adj. R-squared: | 0.446 |
| Method: | Least Squares | F-statistic: | 4.756 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0231 |
| Time: | 04:45:01 | Log-Likelihood: | -69.062 |
| No. Observations: | 15 | AIC: | 146.1 |
| Df Residuals: | 11 | BIC: | 149.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 321.6559 | 172.596 | 1.864 | 0.089 | -58.226 701.538 |
| C(dose)[T.1] | -58.9929 | 241.411 | -0.244 | 0.811 | -590.335 472.349 |
| expression | -35.4464 | 24.019 | -1.476 | 0.168 | -88.311 17.419 |
| expression:C(dose)[T.1] | 15.5548 | 33.221 | 0.468 | 0.649 | -57.564 88.673 |
| Omnibus: | 2.007 | Durbin-Watson: | 1.104 |
| Prob(Omnibus): | 0.367 | Jarque-Bera (JB): | 1.513 |
| Skew: | -0.722 | Prob(JB): | 0.469 |
| Kurtosis: | 2.420 | Cond. No. | 330. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.556 |
| Model: | OLS | Adj. R-squared: | 0.482 |
| Method: | Least Squares | F-statistic: | 7.514 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00766 |
| Time: | 04:45:01 | Log-Likelihood: | -69.210 |
| No. Observations: | 15 | AIC: | 144.4 |
| Df Residuals: | 12 | BIC: | 146.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 263.3387 | 115.534 | 2.279 | 0.042 | 11.612 515.065 |
| C(dose)[T.1] | 53.8270 | 14.385 | 3.742 | 0.003 | 22.484 85.170 |
| expression | -27.3154 | 16.044 | -1.702 | 0.114 | -62.273 7.642 |
| Omnibus: | 2.208 | Durbin-Watson: | 0.958 |
| Prob(Omnibus): | 0.332 | Jarque-Bera (JB): | 1.630 |
| Skew: | -0.761 | Prob(JB): | 0.443 |
| Kurtosis: | 2.460 | Cond. No. | 122. |
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: | 04:45:01 | 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: | 0.038 |
| Model: | OLS | Adj. R-squared: | -0.036 |
| Method: | Least Squares | F-statistic: | 0.5134 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.486 |
| Time: | 04:45:01 | Log-Likelihood: | -75.010 |
| No. Observations: | 15 | AIC: | 154.0 |
| Df Residuals: | 13 | BIC: | 155.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 209.6104 | 162.126 | 1.293 | 0.219 | -140.642 559.862 |
| expression | -15.9646 | 22.281 | -0.717 | 0.486 | -64.100 32.171 |
| Omnibus: | 1.839 | Durbin-Watson: | 1.810 |
| Prob(Omnibus): | 0.399 | Jarque-Bera (JB): | 1.005 |
| Skew: | 0.241 | Prob(JB): | 0.605 |
| Kurtosis: | 1.827 | Cond. No. | 121. |