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 |
| 1.147 | 0.297 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.677 |
| Model: | OLS | Adj. R-squared: | 0.626 |
| Method: | Least Squares | F-statistic: | 13.27 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.62e-05 |
| Time: | 04:39:09 | Log-Likelihood: | -100.11 |
| No. Observations: | 23 | AIC: | 208.2 |
| Df Residuals: | 19 | BIC: | 212.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 173.9514 | 94.110 | 1.848 | 0.080 | -23.023 370.926 |
| C(dose)[T.1] | -88.5555 | 198.201 | -0.447 | 0.660 | -503.395 326.284 |
| expression | -15.0040 | 11.768 | -1.275 | 0.218 | -39.636 9.628 |
| expression:C(dose)[T.1] | 17.7437 | 24.564 | 0.722 | 0.479 | -33.670 69.158 |
| Omnibus: | 0.196 | Durbin-Watson: | 1.810 |
| Prob(Omnibus): | 0.906 | Jarque-Bera (JB): | 0.403 |
| Skew: | 0.036 | Prob(JB): | 0.817 |
| Kurtosis: | 2.355 | Cond. No. | 439. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.635 |
| Method: | Least Squares | F-statistic: | 20.13 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.62e-05 |
| Time: | 04:39:09 | Log-Likelihood: | -100.42 |
| No. Observations: | 23 | AIC: | 206.8 |
| Df Residuals: | 20 | BIC: | 210.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 141.4495 | 81.662 | 1.732 | 0.099 | -28.895 311.794 |
| C(dose)[T.1] | 54.4738 | 8.594 | 6.338 | 0.000 | 36.546 72.401 |
| expression | -10.9315 | 10.206 | -1.071 | 0.297 | -32.220 10.357 |
| Omnibus: | 0.140 | Durbin-Watson: | 1.849 |
| Prob(Omnibus): | 0.932 | Jarque-Bera (JB): | 0.045 |
| Skew: | -0.065 | Prob(JB): | 0.978 |
| Kurtosis: | 2.825 | Cond. No. | 157. |
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: | 04:39:09 | 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: | 0.02950 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.865 |
| Time: | 04:39:09 | 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 | 103.3612 | 137.859 | 0.750 | 0.462 | -183.333 390.055 |
| expression | -2.9443 | 17.144 | -0.172 | 0.865 | -38.596 32.708 |
| Omnibus: | 3.509 | Durbin-Watson: | 2.480 |
| Prob(Omnibus): | 0.173 | Jarque-Bera (JB): | 1.647 |
| Skew: | 0.311 | Prob(JB): | 0.439 |
| Kurtosis: | 1.846 | Cond. No. | 156. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.372 | 0.554 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.514 |
| Model: | OLS | Adj. R-squared: | 0.381 |
| Method: | Least Squares | F-statistic: | 3.872 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0410 |
| Time: | 04:39:09 | Log-Likelihood: | -69.894 |
| No. Observations: | 15 | AIC: | 147.8 |
| Df Residuals: | 11 | BIC: | 150.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 234.8208 | 160.185 | 1.466 | 0.171 | -117.743 587.385 |
| C(dose)[T.1] | -285.7074 | 319.104 | -0.895 | 0.390 | -988.051 416.636 |
| expression | -19.5900 | 18.700 | -1.048 | 0.317 | -60.748 21.568 |
| expression:C(dose)[T.1] | 39.6290 | 37.923 | 1.045 | 0.318 | -43.838 123.096 |
| Omnibus: | 3.860 | Durbin-Watson: | 1.350 |
| Prob(Omnibus): | 0.145 | Jarque-Bera (JB): | 2.277 |
| Skew: | -0.954 | Prob(JB): | 0.320 |
| Kurtosis: | 3.030 | Cond. No. | 426. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.465 |
| Model: | OLS | Adj. R-squared: | 0.376 |
| Method: | Least Squares | F-statistic: | 5.222 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0234 |
| Time: | 04:39:09 | Log-Likelihood: | -70.604 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 152.4832 | 140.000 | 1.089 | 0.297 | -152.551 457.517 |
| C(dose)[T.1] | 47.3495 | 15.795 | 2.998 | 0.011 | 12.935 81.764 |
| expression | -9.9540 | 16.331 | -0.610 | 0.554 | -45.535 25.627 |
| Omnibus: | 2.088 | Durbin-Watson: | 0.974 |
| Prob(Omnibus): | 0.352 | Jarque-Bera (JB): | 1.511 |
| Skew: | -0.738 | Prob(JB): | 0.470 |
| Kurtosis: | 2.510 | Cond. No. | 156. |
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: | 04:39:09 | 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: | 0.065 |
| Model: | OLS | Adj. R-squared: | -0.007 |
| Method: | Least Squares | F-statistic: | 0.9025 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.359 |
| Time: | 04:39:09 | Log-Likelihood: | -74.797 |
| No. Observations: | 15 | AIC: | 153.6 |
| Df Residuals: | 13 | BIC: | 155.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 257.0556 | 172.271 | 1.492 | 0.160 | -115.112 629.223 |
| expression | -19.3455 | 20.364 | -0.950 | 0.359 | -63.339 24.648 |
| Omnibus: | 1.450 | Durbin-Watson: | 1.716 |
| Prob(Omnibus): | 0.484 | Jarque-Bera (JB): | 0.991 |
| Skew: | 0.341 | Prob(JB): | 0.609 |
| Kurtosis: | 1.941 | Cond. No. | 150. |