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 |
| 0.360 | 0.555 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.601 |
| Method: | Least Squares | F-statistic: | 12.06 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.000120 |
| Time: | 11:50:21 | Log-Likelihood: | -100.84 |
| No. Observations: | 23 | AIC: | 209.7 |
| Df Residuals: | 19 | BIC: | 214.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 54.4657 | 71.284 | 0.764 | 0.454 | -94.734 203.665 |
| C(dose)[T.1] | 37.5736 | 76.054 | 0.494 | 0.627 | -121.609 196.756 |
| expression | -0.0885 | 24.418 | -0.004 | 0.997 | -51.196 51.019 |
| expression:C(dose)[T.1] | 3.6542 | 25.124 | 0.145 | 0.886 | -48.931 56.239 |
| Omnibus: | 0.893 | Durbin-Watson: | 1.818 |
| Prob(Omnibus): | 0.640 | Jarque-Bera (JB): | 0.773 |
| Skew: | 0.142 | Prob(JB): | 0.679 |
| Kurtosis: | 2.148 | Cond. No. | 116. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.655 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 19.01 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 2.37e-05 |
| Time: | 11:50:21 | Log-Likelihood: | -100.86 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 44.4266 | 17.374 | 2.557 | 0.019 | 8.184 80.669 |
| C(dose)[T.1] | 48.4931 | 11.862 | 4.088 | 0.001 | 23.748 73.238 |
| expression | 3.3633 | 5.605 | 0.600 | 0.555 | -8.328 15.055 |
| Omnibus: | 0.988 | Durbin-Watson: | 1.850 |
| Prob(Omnibus): | 0.610 | Jarque-Bera (JB): | 0.810 |
| Skew: | 0.145 | Prob(JB): | 0.667 |
| Kurtosis: | 2.127 | Cond. No. | 17.5 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 11:50:21 | 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: | 0.367 |
| Model: | OLS | Adj. R-squared: | 0.337 |
| Method: | Least Squares | F-statistic: | 12.19 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00218 |
| Time: | 11:50:22 | Log-Likelihood: | -107.84 |
| No. Observations: | 23 | AIC: | 219.7 |
| Df Residuals: | 21 | BIC: | 222.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 11.5290 | 20.359 | 0.566 | 0.577 | -30.810 53.868 |
| expression | 18.9559 | 5.430 | 3.491 | 0.002 | 7.663 30.248 |
| Omnibus: | 3.923 | Durbin-Watson: | 1.999 |
| Prob(Omnibus): | 0.141 | Jarque-Bera (JB): | 2.840 |
| Skew: | 0.861 | Prob(JB): | 0.242 |
| Kurtosis: | 2.998 | Cond. No. | 14.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.051 | 0.826 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.612 |
| Model: | OLS | Adj. R-squared: | 0.506 |
| Method: | Least Squares | F-statistic: | 5.776 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0127 |
| Time: | 11:50:22 | Log-Likelihood: | -68.206 |
| No. Observations: | 15 | AIC: | 144.4 |
| Df Residuals: | 11 | BIC: | 147.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -158.2034 | 150.006 | -1.055 | 0.314 | -488.365 171.958 |
| C(dose)[T.1] | 459.3052 | 192.670 | 2.384 | 0.036 | 35.241 883.369 |
| expression | 82.8999 | 54.990 | 1.508 | 0.160 | -38.131 203.931 |
| expression:C(dose)[T.1] | -150.0404 | 70.347 | -2.133 | 0.056 | -304.872 4.791 |
| Omnibus: | 0.087 | Durbin-Watson: | 0.990 |
| Prob(Omnibus): | 0.957 | Jarque-Bera (JB): | 0.292 |
| Skew: | -0.116 | Prob(JB): | 0.864 |
| Kurtosis: | 2.357 | Cond. No. | 121. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 4.931 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0274 |
| Time: | 11:50:22 | Log-Likelihood: | -70.801 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 91.3304 | 106.870 | 0.855 | 0.410 | -141.518 324.179 |
| C(dose)[T.1] | 49.4237 | 15.739 | 3.140 | 0.009 | 15.131 83.716 |
| expression | -8.7818 | 39.038 | -0.225 | 0.826 | -93.839 76.275 |
| Omnibus: | 2.767 | Durbin-Watson: | 0.776 |
| Prob(Omnibus): | 0.251 | Jarque-Bera (JB): | 1.967 |
| Skew: | -0.858 | Prob(JB): | 0.374 |
| Kurtosis: | 2.551 | Cond. No. | 43.1 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 11:50:22 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.077 |
| Method: | Least Squares | F-statistic: | 0.0003258 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.986 |
| Time: | 11:50:22 | Log-Likelihood: | -75.300 |
| 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 | 96.1613 | 138.571 | 0.694 | 0.500 | -203.202 395.525 |
| expression | -0.9119 | 50.519 | -0.018 | 0.986 | -110.052 108.228 |
| Omnibus: | 0.553 | Durbin-Watson: | 1.621 |
| Prob(Omnibus): | 0.759 | Jarque-Bera (JB): | 0.561 |
| Skew: | 0.037 | Prob(JB): | 0.755 |
| Kurtosis: | 2.055 | Cond. No. | 42.4 |