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 |
| 0.005 | 0.946 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.665 |
| Model: | OLS | Adj. R-squared: | 0.612 |
| Method: | Least Squares | F-statistic: | 12.57 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.31e-05 |
| Time: | 04:44:40 | Log-Likelihood: | -100.53 |
| No. Observations: | 23 | AIC: | 209.1 |
| Df Residuals: | 19 | BIC: | 213.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 73.4747 | 26.982 | 2.723 | 0.014 | 17.000 129.950 |
| C(dose)[T.1] | 18.6756 | 37.775 | 0.494 | 0.627 | -60.388 97.740 |
| expression | -4.8356 | 6.598 | -0.733 | 0.473 | -18.645 8.974 |
| expression:C(dose)[T.1] | 8.5830 | 9.086 | 0.945 | 0.357 | -10.434 27.600 |
| Omnibus: | 2.757 | Durbin-Watson: | 2.205 |
| Prob(Omnibus): | 0.252 | Jarque-Bera (JB): | 1.237 |
| Skew: | -0.045 | Prob(JB): | 0.539 |
| Kurtosis: | 1.867 | Cond. No. | 49.5 |
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: | 04:44:40 | 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 | 55.4413 | 19.018 | 2.915 | 0.009 | 15.771 95.111 |
| C(dose)[T.1] | 53.3755 | 8.787 | 6.075 | 0.000 | 35.047 71.704 |
| expression | -0.3095 | 4.524 | -0.068 | 0.946 | -9.746 9.127 |
| Omnibus: | 0.262 | Durbin-Watson: | 1.905 |
| Prob(Omnibus): | 0.877 | Jarque-Bera (JB): | 0.447 |
| Skew: | 0.043 | Prob(JB): | 0.800 |
| Kurtosis: | 2.322 | Cond. No. | 19.2 |
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: | 04:44:40 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.046 |
| Method: | Least Squares | F-statistic: | 0.03772 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.848 |
| Time: | 04:44:40 | Log-Likelihood: | -113.08 |
| 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 | 73.8814 | 30.903 | 2.391 | 0.026 | 9.615 138.147 |
| expression | 1.4433 | 7.432 | 0.194 | 0.848 | -14.012 16.898 |
| Omnibus: | 3.585 | Durbin-Watson: | 2.473 |
| Prob(Omnibus): | 0.167 | Jarque-Bera (JB): | 1.608 |
| Skew: | 0.279 | Prob(JB): | 0.447 |
| Kurtosis: | 1.831 | Cond. No. | 18.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 4.378 | 0.058 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.687 |
| Model: | OLS | Adj. R-squared: | 0.602 |
| Method: | Least Squares | F-statistic: | 8.057 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00405 |
| Time: | 04:44:40 | Log-Likelihood: | -66.582 |
| No. Observations: | 15 | AIC: | 141.2 |
| Df Residuals: | 11 | BIC: | 144.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 120.6042 | 41.688 | 2.893 | 0.015 | 28.849 212.359 |
| C(dose)[T.1] | 188.4379 | 85.742 | 2.198 | 0.050 | -0.278 377.154 |
| expression | -9.4834 | 7.258 | -1.307 | 0.218 | -25.457 6.491 |
| expression:C(dose)[T.1] | -30.7418 | 17.172 | -1.790 | 0.101 | -68.537 7.054 |
| Omnibus: | 1.205 | Durbin-Watson: | 1.471 |
| Prob(Omnibus): | 0.547 | Jarque-Bera (JB): | 0.906 |
| Skew: | -0.330 | Prob(JB): | 0.636 |
| Kurtosis: | 1.992 | Cond. No. | 87.6 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.596 |
| Model: | OLS | Adj. R-squared: | 0.529 |
| Method: | Least Squares | F-statistic: | 8.856 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00434 |
| Time: | 04:44:40 | Log-Likelihood: | -68.500 |
| No. Observations: | 15 | AIC: | 143.0 |
| Df Residuals: | 12 | BIC: | 145.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 151.3953 | 41.316 | 3.664 | 0.003 | 61.375 241.416 |
| C(dose)[T.1] | 36.8609 | 14.706 | 2.507 | 0.028 | 4.820 68.902 |
| expression | -14.9747 | 7.156 | -2.092 | 0.058 | -30.567 0.618 |
| Omnibus: | 2.166 | Durbin-Watson: | 1.831 |
| Prob(Omnibus): | 0.339 | Jarque-Bera (JB): | 1.670 |
| Skew: | -0.708 | Prob(JB): | 0.434 |
| Kurtosis: | 2.184 | Cond. No. | 34.2 |
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: | 04:44:40 | 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: | 0.385 |
| Model: | OLS | Adj. R-squared: | 0.337 |
| Method: | Least Squares | F-statistic: | 8.127 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0136 |
| Time: | 04:44:40 | Log-Likelihood: | -71.658 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 13 | BIC: | 148.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 208.2160 | 40.964 | 5.083 | 0.000 | 119.719 296.713 |
| expression | -22.1655 | 7.775 | -2.851 | 0.014 | -38.962 -5.368 |
| Omnibus: | 1.614 | Durbin-Watson: | 2.542 |
| Prob(Omnibus): | 0.446 | Jarque-Bera (JB): | 0.905 |
| Skew: | -0.166 | Prob(JB): | 0.636 |
| Kurtosis: | 1.843 | Cond. No. | 28.0 |