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 |
| 5.532 | 0.029 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.741 |
| Model: | OLS | Adj. R-squared: | 0.700 |
| Method: | Least Squares | F-statistic: | 18.12 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.44e-06 |
| Time: | 04:36:31 | Log-Likelihood: | -97.570 |
| No. Observations: | 23 | AIC: | 203.1 |
| Df Residuals: | 19 | BIC: | 207.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 20.6871 | 66.099 | 0.313 | 0.758 | -117.659 159.033 |
| C(dose)[T.1] | -50.6109 | 85.526 | -0.592 | 0.561 | -229.618 128.396 |
| expression | 7.5811 | 14.900 | 0.509 | 0.617 | -23.605 38.767 |
| expression:C(dose)[T.1] | 19.8446 | 18.385 | 1.079 | 0.294 | -18.636 58.325 |
| Omnibus: | 1.680 | Durbin-Watson: | 1.481 |
| Prob(Omnibus): | 0.432 | Jarque-Bera (JB): | 1.439 |
| Skew: | 0.563 | Prob(JB): | 0.487 |
| Kurtosis: | 2.516 | Cond. No. | 153. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.725 |
| Model: | OLS | Adj. R-squared: | 0.698 |
| Method: | Least Squares | F-statistic: | 26.38 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.46e-06 |
| Time: | 04:36:31 | Log-Likelihood: | -98.254 |
| No. Observations: | 23 | AIC: | 202.5 |
| Df Residuals: | 20 | BIC: | 205.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -36.9456 | 39.124 | -0.944 | 0.356 | -118.557 44.666 |
| C(dose)[T.1] | 41.1585 | 9.330 | 4.411 | 0.000 | 21.696 60.621 |
| expression | 20.6153 | 8.765 | 2.352 | 0.029 | 2.333 38.898 |
| Omnibus: | 1.747 | Durbin-Watson: | 1.659 |
| Prob(Omnibus): | 0.418 | Jarque-Bera (JB): | 1.407 |
| Skew: | 0.578 | Prob(JB): | 0.495 |
| Kurtosis: | 2.635 | Cond. No. | 50.6 |
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: | 04:36:31 | 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: | 0.458 |
| Model: | OLS | Adj. R-squared: | 0.432 |
| Method: | Least Squares | F-statistic: | 17.72 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000394 |
| Time: | 04:36:31 | Log-Likelihood: | -106.07 |
| No. Observations: | 23 | AIC: | 216.1 |
| Df Residuals: | 21 | BIC: | 218.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -118.1935 | 47.316 | -2.498 | 0.021 | -216.592 -19.795 |
| expression | 42.0710 | 9.995 | 4.209 | 0.000 | 21.286 62.856 |
| Omnibus: | 1.406 | Durbin-Watson: | 1.631 |
| Prob(Omnibus): | 0.495 | Jarque-Bera (JB): | 0.965 |
| Skew: | 0.166 | Prob(JB): | 0.617 |
| Kurtosis: | 2.053 | Cond. No. | 44.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.243 | 0.631 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.497 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 3.626 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0487 |
| Time: | 04:36:31 | Log-Likelihood: | -70.143 |
| No. Observations: | 15 | AIC: | 148.3 |
| Df Residuals: | 11 | BIC: | 151.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 42.8720 | 160.564 | 0.267 | 0.794 | -310.527 396.271 |
| C(dose)[T.1] | 291.3147 | 267.497 | 1.089 | 0.299 | -297.443 880.073 |
| expression | 5.5422 | 36.145 | 0.153 | 0.881 | -74.013 85.098 |
| expression:C(dose)[T.1] | -54.4785 | 60.138 | -0.906 | 0.384 | -186.841 77.884 |
| Omnibus: | 2.940 | Durbin-Watson: | 0.905 |
| Prob(Omnibus): | 0.230 | Jarque-Bera (JB): | 1.562 |
| Skew: | -0.790 | Prob(JB): | 0.458 |
| Kurtosis: | 3.059 | Cond. No. | 200. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.460 |
| Model: | OLS | Adj. R-squared: | 0.370 |
| Method: | Least Squares | F-statistic: | 5.105 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0249 |
| Time: | 04:36:31 | Log-Likelihood: | -70.683 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 12 | BIC: | 149.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 130.0731 | 127.546 | 1.020 | 0.328 | -147.827 407.973 |
| C(dose)[T.1] | 49.4083 | 15.588 | 3.170 | 0.008 | 15.444 83.373 |
| expression | -14.1383 | 28.671 | -0.493 | 0.631 | -76.608 48.331 |
| Omnibus: | 3.013 | Durbin-Watson: | 0.768 |
| Prob(Omnibus): | 0.222 | Jarque-Bera (JB): | 1.951 |
| Skew: | -0.875 | Prob(JB): | 0.377 |
| Kurtosis: | 2.757 | Cond. No. | 77.2 |
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: | 04:36:31 | 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: | 0.007 |
| Model: | OLS | Adj. R-squared: | -0.069 |
| Method: | Least Squares | F-statistic: | 0.09715 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.760 |
| Time: | 04:36:31 | Log-Likelihood: | -75.244 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 145.3040 | 165.979 | 0.875 | 0.397 | -213.272 503.879 |
| expression | -11.6331 | 37.323 | -0.312 | 0.760 | -92.264 68.998 |
| Omnibus: | 0.350 | Durbin-Watson: | 1.562 |
| Prob(Omnibus): | 0.840 | Jarque-Bera (JB): | 0.475 |
| Skew: | -0.014 | Prob(JB): | 0.789 |
| Kurtosis: | 2.129 | Cond. No. | 76.6 |