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 |
| 0.001 | 0.971 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.594 |
| Method: | Least Squares | F-statistic: | 11.71 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000143 |
| Time: | 03:55:41 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 210.1 |
| Df Residuals: | 19 | BIC: | 214.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 53.3154 | 26.683 | 1.998 | 0.060 | -2.533 109.164 |
| C(dose)[T.1] | 53.8309 | 34.492 | 1.561 | 0.135 | -18.362 126.024 |
| expression | 0.2019 | 5.867 | 0.034 | 0.973 | -12.077 12.481 |
| expression:C(dose)[T.1] | -0.1135 | 7.466 | -0.015 | 0.988 | -15.741 15.514 |
| Omnibus: | 0.313 | Durbin-Watson: | 1.889 |
| Prob(Omnibus): | 0.855 | Jarque-Bera (JB): | 0.480 |
| Skew: | 0.062 | Prob(JB): | 0.787 |
| Kurtosis: | 2.303 | Cond. No. | 50.8 |
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: | 03:55:41 | 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 | 53.6254 | 16.778 | 3.196 | 0.005 | 18.626 88.625 |
| C(dose)[T.1] | 53.3245 | 8.776 | 6.076 | 0.000 | 35.018 71.631 |
| expression | 0.1318 | 3.537 | 0.037 | 0.971 | -7.246 7.510 |
| Omnibus: | 0.300 | Durbin-Watson: | 1.891 |
| Prob(Omnibus): | 0.861 | Jarque-Bera (JB): | 0.472 |
| Skew: | 0.060 | Prob(JB): | 0.790 |
| Kurtosis: | 2.308 | Cond. No. | 18.7 |
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: | 03:55:41 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.046 |
| Method: | Least Squares | F-statistic: | 0.02713 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.871 |
| Time: | 03:55:41 | Log-Likelihood: | -113.09 |
| 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 | 75.4348 | 26.984 | 2.796 | 0.011 | 19.319 131.551 |
| expression | 0.9584 | 5.819 | 0.165 | 0.871 | -11.143 13.059 |
| Omnibus: | 3.467 | Durbin-Watson: | 2.510 |
| Prob(Omnibus): | 0.177 | Jarque-Bera (JB): | 1.603 |
| Skew: | 0.290 | Prob(JB): | 0.449 |
| Kurtosis: | 1.844 | Cond. No. | 18.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.460 | 0.511 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.469 |
| Model: | OLS | Adj. R-squared: | 0.324 |
| Method: | Least Squares | F-statistic: | 3.241 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0642 |
| Time: | 03:55:41 | Log-Likelihood: | -70.550 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 151.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 84.6100 | 34.318 | 2.465 | 0.031 | 9.076 160.144 |
| C(dose)[T.1] | 49.1235 | 58.399 | 0.841 | 0.418 | -79.413 177.660 |
| expression | -4.0364 | 7.572 | -0.533 | 0.605 | -20.703 12.630 |
| expression:C(dose)[T.1] | 0.4192 | 12.317 | 0.034 | 0.973 | -26.691 27.529 |
| Omnibus: | 2.770 | Durbin-Watson: | 0.905 |
| Prob(Omnibus): | 0.250 | Jarque-Bera (JB): | 1.789 |
| Skew: | -0.836 | Prob(JB): | 0.409 |
| Kurtosis: | 2.742 | Cond. No. | 45.0 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.469 |
| Model: | OLS | Adj. R-squared: | 0.381 |
| Method: | Least Squares | F-statistic: | 5.302 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0224 |
| Time: | 03:55:41 | Log-Likelihood: | -70.551 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 12 | BIC: | 149.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 83.9356 | 26.828 | 3.129 | 0.009 | 25.483 142.388 |
| C(dose)[T.1] | 51.0314 | 15.682 | 3.254 | 0.007 | 16.864 85.199 |
| expression | -3.8779 | 5.718 | -0.678 | 0.511 | -16.337 8.581 |
| Omnibus: | 2.699 | Durbin-Watson: | 0.903 |
| Prob(Omnibus): | 0.259 | Jarque-Bera (JB): | 1.739 |
| Skew: | -0.824 | Prob(JB): | 0.419 |
| Kurtosis: | 2.740 | Cond. No. | 17.2 |
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: | 03:55:41 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.076 |
| Method: | Least Squares | F-statistic: | 0.008072 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.930 |
| Time: | 03:55:41 | Log-Likelihood: | -75.295 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 96.6746 | 34.986 | 2.763 | 0.016 | 21.091 172.258 |
| expression | -0.6671 | 7.425 | -0.090 | 0.930 | -16.708 15.373 |
| Omnibus: | 0.496 | Durbin-Watson: | 1.626 |
| Prob(Omnibus): | 0.780 | Jarque-Bera (JB): | 0.541 |
| Skew: | 0.054 | Prob(JB): | 0.763 |
| Kurtosis: | 2.076 | Cond. No. | 16.9 |