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 |
| 0.492 | 0.491 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.660 |
| Model: | OLS | Adj. R-squared: | 0.606 |
| Method: | Least Squares | F-statistic: | 12.29 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000106 |
| Time: | 03:37:35 | Log-Likelihood: | -100.70 |
| No. Observations: | 23 | AIC: | 209.4 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -25.8627 | 106.482 | -0.243 | 0.811 | -248.732 197.007 |
| C(dose)[T.1] | 109.8263 | 154.443 | 0.711 | 0.486 | -213.426 433.078 |
| expression | 12.3364 | 16.378 | 0.753 | 0.461 | -21.944 46.617 |
| expression:C(dose)[T.1] | -8.7939 | 23.446 | -0.375 | 0.712 | -57.867 40.279 |
| Omnibus: | 0.113 | Durbin-Watson: | 1.963 |
| Prob(Omnibus): | 0.945 | Jarque-Bera (JB): | 0.337 |
| Skew: | 0.003 | Prob(JB): | 0.845 |
| Kurtosis: | 2.407 | Cond. No. | 303. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 19.20 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.22e-05 |
| Time: | 03:37:35 | Log-Likelihood: | -100.78 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 1.9899 | 74.657 | 0.027 | 0.979 | -153.741 157.721 |
| C(dose)[T.1] | 51.9996 | 8.871 | 5.862 | 0.000 | 33.495 70.504 |
| expression | 8.0452 | 11.465 | 0.702 | 0.491 | -15.871 31.961 |
| Omnibus: | 0.306 | Durbin-Watson: | 1.978 |
| Prob(Omnibus): | 0.858 | Jarque-Bera (JB): | 0.477 |
| Skew: | 0.085 | Prob(JB): | 0.788 |
| Kurtosis: | 2.315 | Cond. No. | 116. |
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: | 03:37:35 | 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: | 0.069 |
| Model: | OLS | Adj. R-squared: | 0.025 |
| Method: | Least Squares | F-statistic: | 1.558 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.226 |
| Time: | 03:37:35 | Log-Likelihood: | -112.28 |
| No. Observations: | 23 | AIC: | 228.6 |
| Df Residuals: | 21 | BIC: | 230.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -68.0145 | 118.569 | -0.574 | 0.572 | -314.592 178.562 |
| expression | 22.4853 | 18.015 | 1.248 | 0.226 | -14.980 59.950 |
| Omnibus: | 1.205 | Durbin-Watson: | 2.399 |
| Prob(Omnibus): | 0.547 | Jarque-Bera (JB): | 1.100 |
| Skew: | 0.391 | Prob(JB): | 0.577 |
| Kurtosis: | 2.268 | Cond. No. | 115. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.012 | 0.182 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.539 |
| Model: | OLS | Adj. R-squared: | 0.413 |
| Method: | Least Squares | F-statistic: | 4.288 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0311 |
| Time: | 03:37:35 | Log-Likelihood: | -69.492 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 11 | BIC: | 149.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -92.6822 | 125.910 | -0.736 | 0.477 | -369.808 184.443 |
| C(dose)[T.1] | 142.8411 | 156.140 | 0.915 | 0.380 | -200.822 486.504 |
| expression | 29.0023 | 22.720 | 1.276 | 0.228 | -21.005 79.009 |
| expression:C(dose)[T.1] | -15.2548 | 29.605 | -0.515 | 0.617 | -80.415 49.906 |
| Omnibus: | 0.601 | Durbin-Watson: | 1.296 |
| Prob(Omnibus): | 0.741 | Jarque-Bera (JB): | 0.246 |
| Skew: | -0.301 | Prob(JB): | 0.884 |
| Kurtosis: | 2.820 | Cond. No. | 155. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.528 |
| Model: | OLS | Adj. R-squared: | 0.449 |
| Method: | Least Squares | F-statistic: | 6.710 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0111 |
| Time: | 03:37:35 | Log-Likelihood: | -69.671 |
| No. Observations: | 15 | AIC: | 145.3 |
| Df Residuals: | 12 | BIC: | 147.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -43.0817 | 78.639 | -0.548 | 0.594 | -214.420 128.257 |
| C(dose)[T.1] | 62.9253 | 17.489 | 3.598 | 0.004 | 24.820 101.030 |
| expression | 20.0177 | 14.114 | 1.418 | 0.182 | -10.733 50.769 |
| Omnibus: | 0.391 | Durbin-Watson: | 1.378 |
| Prob(Omnibus): | 0.823 | Jarque-Bera (JB): | 0.030 |
| Skew: | -0.099 | Prob(JB): | 0.985 |
| Kurtosis: | 2.912 | Cond. No. | 58.9 |
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: | 03:37:35 | 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: | 0.019 |
| Model: | OLS | Adj. R-squared: | -0.057 |
| Method: | Least Squares | F-statistic: | 0.2467 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.628 |
| Time: | 03:37:35 | Log-Likelihood: | -75.159 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 135.3580 | 84.539 | 1.601 | 0.133 | -47.277 317.993 |
| expression | -8.0878 | 16.283 | -0.497 | 0.628 | -43.266 27.090 |
| Omnibus: | 0.207 | Durbin-Watson: | 1.443 |
| Prob(Omnibus): | 0.902 | Jarque-Bera (JB): | 0.397 |
| Skew: | -0.132 | Prob(JB): | 0.820 |
| Kurtosis: | 2.248 | Cond. No. | 45.2 |