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 |
| 0.121 | 0.731 | 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: | 11.88 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000131 |
| Time: | 04:52:51 | 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 | -11.4215 | 165.156 | -0.069 | 0.946 | -357.098 334.255 |
| C(dose)[T.1] | 103.3372 | 198.223 | 0.521 | 0.608 | -311.549 518.223 |
| expression | 9.4047 | 23.650 | 0.398 | 0.695 | -40.095 58.905 |
| expression:C(dose)[T.1] | -7.1411 | 28.469 | -0.251 | 0.805 | -66.727 52.445 |
| Omnibus: | 0.826 | Durbin-Watson: | 1.861 |
| Prob(Omnibus): | 0.662 | Jarque-Bera (JB): | 0.713 |
| Skew: | 0.000 | Prob(JB): | 0.700 |
| Kurtosis: | 2.137 | Cond. No. | 444. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.67 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.67e-05 |
| Time: | 04:52:51 | Log-Likelihood: | -100.99 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 22.9699 | 89.897 | 0.256 | 0.801 | -164.553 210.493 |
| C(dose)[T.1] | 53.6662 | 8.794 | 6.102 | 0.000 | 35.322 72.011 |
| expression | 4.4764 | 12.853 | 0.348 | 0.731 | -22.334 31.287 |
| Omnibus: | 0.815 | Durbin-Watson: | 1.872 |
| Prob(Omnibus): | 0.665 | Jarque-Bera (JB): | 0.714 |
| Skew: | 0.054 | Prob(JB): | 0.700 |
| Kurtosis: | 2.144 | Cond. No. | 146. |
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: | 04:52:52 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.046 |
| Method: | Least Squares | F-statistic: | 0.03507 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.853 |
| Time: | 04:52:52 | Log-Likelihood: | -113.09 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 107.1495 | 146.660 | 0.731 | 0.473 | -197.846 412.145 |
| expression | -3.9509 | 21.097 | -0.187 | 0.853 | -47.824 39.923 |
| Omnibus: | 3.426 | Durbin-Watson: | 2.447 |
| Prob(Omnibus): | 0.180 | Jarque-Bera (JB): | 1.593 |
| Skew: | 0.289 | Prob(JB): | 0.451 |
| Kurtosis: | 1.847 | Cond. No. | 144. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.016 | 0.902 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.492 |
| Model: | OLS | Adj. R-squared: | 0.354 |
| Method: | Least Squares | F-statistic: | 3.553 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0512 |
| Time: | 04:52:52 | Log-Likelihood: | -70.219 |
| No. Observations: | 15 | AIC: | 148.4 |
| Df Residuals: | 11 | BIC: | 151.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 145.5958 | 99.932 | 1.457 | 0.173 | -74.352 365.544 |
| C(dose)[T.1] | -79.2838 | 134.335 | -0.590 | 0.567 | -374.954 216.386 |
| expression | -12.8083 | 16.265 | -0.787 | 0.448 | -48.608 22.991 |
| expression:C(dose)[T.1] | 21.3839 | 22.256 | 0.961 | 0.357 | -27.601 70.369 |
| Omnibus: | 2.498 | Durbin-Watson: | 1.056 |
| Prob(Omnibus): | 0.287 | Jarque-Bera (JB): | 1.544 |
| Skew: | -0.779 | Prob(JB): | 0.462 |
| Kurtosis: | 2.788 | Cond. No. | 142. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.899 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0278 |
| Time: | 04:52:52 | Log-Likelihood: | -70.823 |
| 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 | 75.8929 | 68.507 | 1.108 | 0.290 | -73.371 225.157 |
| C(dose)[T.1] | 48.8693 | 15.944 | 3.065 | 0.010 | 14.129 83.609 |
| expression | -1.3870 | 11.066 | -0.125 | 0.902 | -25.499 22.725 |
| Omnibus: | 2.626 | Durbin-Watson: | 0.806 |
| Prob(Omnibus): | 0.269 | Jarque-Bera (JB): | 1.838 |
| Skew: | -0.832 | Prob(JB): | 0.399 |
| Kurtosis: | 2.582 | Cond. No. | 54.3 |
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: | 04:52:52 | 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: | -0.057 |
| Method: | Least Squares | F-statistic: | 0.2455 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.629 |
| Time: | 04:52:52 | Log-Likelihood: | -75.160 |
| 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 | 135.1412 | 84.313 | 1.603 | 0.133 | -47.006 317.288 |
| expression | -6.9390 | 14.005 | -0.495 | 0.629 | -37.195 23.317 |
| Omnibus: | 1.155 | Durbin-Watson: | 1.565 |
| Prob(Omnibus): | 0.561 | Jarque-Bera (JB): | 0.796 |
| Skew: | 0.184 | Prob(JB): | 0.672 |
| Kurtosis: | 1.933 | Cond. No. | 51.8 |