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 |
| 3.023 | 0.097 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.721 |
| Model: | OLS | Adj. R-squared: | 0.678 |
| Method: | Least Squares | F-statistic: | 16.41 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.66e-05 |
| Time: | 04:17:55 | Log-Likelihood: | -98.405 |
| No. Observations: | 23 | AIC: | 204.8 |
| Df Residuals: | 19 | BIC: | 209.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -6.2823 | 94.103 | -0.067 | 0.947 | -203.242 190.677 |
| C(dose)[T.1] | -141.8313 | 152.741 | -0.929 | 0.365 | -461.522 177.859 |
| expression | 8.8975 | 13.817 | 0.644 | 0.527 | -20.023 37.818 |
| expression:C(dose)[T.1] | 31.4579 | 23.468 | 1.340 | 0.196 | -17.660 80.576 |
| Omnibus: | 0.605 | Durbin-Watson: | 2.164 |
| Prob(Omnibus): | 0.739 | Jarque-Bera (JB): | 0.670 |
| Skew: | -0.198 | Prob(JB): | 0.716 |
| Kurtosis: | 2.264 | Cond. No. | 312. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.695 |
| Model: | OLS | Adj. R-squared: | 0.665 |
| Method: | Least Squares | F-statistic: | 22.80 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.93e-06 |
| Time: | 04:17:55 | Log-Likelihood: | -99.444 |
| No. Observations: | 23 | AIC: | 204.9 |
| Df Residuals: | 20 | BIC: | 208.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -80.4241 | 77.634 | -1.036 | 0.313 | -242.367 81.518 |
| C(dose)[T.1] | 62.5149 | 9.730 | 6.425 | 0.000 | 42.219 82.811 |
| expression | 19.8028 | 11.389 | 1.739 | 0.097 | -3.954 43.559 |
| Omnibus: | 2.923 | Durbin-Watson: | 2.248 |
| Prob(Omnibus): | 0.232 | Jarque-Bera (JB): | 1.513 |
| Skew: | -0.304 | Prob(JB): | 0.469 |
| Kurtosis: | 1.900 | Cond. No. | 129. |
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: | 04:17:55 | 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: | 0.066 |
| Model: | OLS | Adj. R-squared: | 0.021 |
| Method: | Least Squares | F-statistic: | 1.481 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.237 |
| Time: | 04:17:55 | Log-Likelihood: | -112.32 |
| No. Observations: | 23 | AIC: | 228.6 |
| Df Residuals: | 21 | BIC: | 230.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 210.5488 | 107.718 | 1.955 | 0.064 | -13.462 434.560 |
| expression | -19.8923 | 16.344 | -1.217 | 0.237 | -53.881 14.096 |
| Omnibus: | 2.480 | Durbin-Watson: | 2.016 |
| Prob(Omnibus): | 0.289 | Jarque-Bera (JB): | 1.492 |
| Skew: | 0.353 | Prob(JB): | 0.474 |
| Kurtosis: | 1.972 | Cond. No. | 104. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.441 | 0.253 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.508 |
| Model: | OLS | Adj. R-squared: | 0.374 |
| Method: | Least Squares | F-statistic: | 3.790 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0434 |
| Time: | 04:17:55 | Log-Likelihood: | -69.976 |
| No. Observations: | 15 | AIC: | 148.0 |
| Df Residuals: | 11 | BIC: | 150.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 182.9929 | 123.132 | 1.486 | 0.165 | -88.019 454.005 |
| C(dose)[T.1] | 31.5960 | 192.215 | 0.164 | 0.872 | -391.467 454.659 |
| expression | -20.3013 | 21.539 | -0.943 | 0.366 | -67.708 27.105 |
| expression:C(dose)[T.1] | 3.2493 | 33.475 | 0.097 | 0.924 | -70.428 76.927 |
| Omnibus: | 3.187 | Durbin-Watson: | 1.200 |
| Prob(Omnibus): | 0.203 | Jarque-Bera (JB): | 1.671 |
| Skew: | -0.815 | Prob(JB): | 0.434 |
| Kurtosis: | 3.122 | Cond. No. | 188. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.508 |
| Model: | OLS | Adj. R-squared: | 0.426 |
| Method: | Least Squares | F-statistic: | 6.192 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0142 |
| Time: | 04:17:55 | Log-Likelihood: | -69.983 |
| No. Observations: | 15 | AIC: | 146.0 |
| Df Residuals: | 12 | BIC: | 148.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 175.3352 | 90.554 | 1.936 | 0.077 | -21.965 372.635 |
| C(dose)[T.1] | 50.1924 | 14.895 | 3.370 | 0.006 | 17.738 82.647 |
| expression | -18.9561 | 15.793 | -1.200 | 0.253 | -53.366 15.454 |
| Omnibus: | 3.243 | Durbin-Watson: | 1.178 |
| Prob(Omnibus): | 0.198 | Jarque-Bera (JB): | 1.653 |
| Skew: | -0.808 | Prob(JB): | 0.438 |
| Kurtosis: | 3.175 | Cond. No. | 72.5 |
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: | 04:17:55 | 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: | 0.042 |
| Model: | OLS | Adj. R-squared: | -0.031 |
| Method: | Least Squares | F-statistic: | 0.5725 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.463 |
| Time: | 04:17:55 | Log-Likelihood: | -74.977 |
| 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 | 185.1454 | 121.310 | 1.526 | 0.151 | -76.930 447.221 |
| expression | -15.9914 | 21.135 | -0.757 | 0.463 | -61.651 29.668 |
| Omnibus: | 0.720 | Durbin-Watson: | 1.779 |
| Prob(Omnibus): | 0.698 | Jarque-Bera (JB): | 0.648 |
| Skew: | 0.161 | Prob(JB): | 0.723 |
| Kurtosis: | 2.034 | Cond. No. | 72.1 |