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 |
| 1.021 | 0.324 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.666 |
| Model: | OLS | Adj. R-squared: | 0.613 |
| Method: | Least Squares | F-statistic: | 12.64 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.99e-05 |
| Time: | 06:21:22 | Log-Likelihood: | -100.49 |
| No. Observations: | 23 | AIC: | 209.0 |
| Df Residuals: | 19 | BIC: | 213.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 15.8497 | 54.078 | 0.293 | 0.773 | -97.337 129.036 |
| C(dose)[T.1] | 51.7966 | 80.111 | 0.647 | 0.526 | -115.878 219.472 |
| expression | 10.0150 | 14.030 | 0.714 | 0.484 | -19.350 39.380 |
| expression:C(dose)[T.1] | 0.5937 | 20.999 | 0.028 | 0.978 | -43.358 44.546 |
| Omnibus: | 0.077 | Durbin-Watson: | 2.039 |
| Prob(Omnibus): | 0.962 | Jarque-Bera (JB): | 0.292 |
| Skew: | 0.064 | Prob(JB): | 0.864 |
| Kurtosis: | 2.463 | Cond. No. | 94.4 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.666 |
| Model: | OLS | Adj. R-squared: | 0.633 |
| Method: | Least Squares | F-statistic: | 19.95 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.72e-05 |
| Time: | 06:21:22 | Log-Likelihood: | -100.49 |
| No. Observations: | 23 | AIC: | 207.0 |
| Df Residuals: | 20 | BIC: | 210.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 14.8346 | 39.418 | 0.376 | 0.711 | -67.390 97.059 |
| C(dose)[T.1] | 54.0480 | 8.583 | 6.297 | 0.000 | 36.144 71.952 |
| expression | 10.2800 | 10.175 | 1.010 | 0.324 | -10.945 31.505 |
| Omnibus: | 0.079 | Durbin-Watson: | 2.042 |
| Prob(Omnibus): | 0.961 | Jarque-Bera (JB): | 0.297 |
| Skew: | 0.057 | Prob(JB): | 0.862 |
| Kurtosis: | 2.455 | Cond. No. | 37.9 |
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: | 06:21:22 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.043 |
| Method: | Least Squares | F-statistic: | 0.08654 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.772 |
| Time: | 06:21:22 | Log-Likelihood: | -113.06 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 60.6267 | 65.295 | 0.929 | 0.364 | -75.161 196.414 |
| expression | 5.0277 | 17.091 | 0.294 | 0.772 | -30.515 40.571 |
| Omnibus: | 2.813 | Durbin-Watson: | 2.565 |
| Prob(Omnibus): | 0.245 | Jarque-Bera (JB): | 1.473 |
| Skew: | 0.294 | Prob(JB): | 0.479 |
| Kurtosis: | 1.908 | Cond. No. | 37.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.001 | 0.973 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.598 |
| Model: | OLS | Adj. R-squared: | 0.489 |
| Method: | Least Squares | F-statistic: | 5.462 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0152 |
| Time: | 06:21:22 | Log-Likelihood: | -68.459 |
| No. Observations: | 15 | AIC: | 144.9 |
| Df Residuals: | 11 | BIC: | 147.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 165.8274 | 76.781 | 2.160 | 0.054 | -3.166 334.821 |
| C(dose)[T.1] | -166.6830 | 108.074 | -1.542 | 0.151 | -404.553 71.187 |
| expression | -22.5494 | 17.438 | -1.293 | 0.222 | -60.930 15.831 |
| expression:C(dose)[T.1] | 53.9783 | 26.674 | 2.024 | 0.068 | -4.731 112.688 |
| Omnibus: | 1.728 | Durbin-Watson: | 0.793 |
| Prob(Omnibus): | 0.421 | Jarque-Bera (JB): | 0.855 |
| Skew: | -0.584 | Prob(JB): | 0.652 |
| Kurtosis: | 2.937 | Cond. No. | 85.7 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.886 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0280 |
| Time: | 06:21:22 | Log-Likelihood: | -70.832 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 65.1631 | 65.597 | 0.993 | 0.340 | -77.761 208.087 |
| C(dose)[T.1] | 49.5213 | 18.261 | 2.712 | 0.019 | 9.734 89.309 |
| expression | 0.5192 | 14.800 | 0.035 | 0.973 | -31.727 32.765 |
| Omnibus: | 2.754 | Durbin-Watson: | 0.812 |
| Prob(Omnibus): | 0.252 | Jarque-Bera (JB): | 1.887 |
| Skew: | -0.849 | Prob(JB): | 0.389 |
| Kurtosis: | 2.631 | Cond. No. | 36.7 |
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: | 06:21:22 | 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: | 0.111 |
| Model: | OLS | Adj. R-squared: | 0.043 |
| Method: | Least Squares | F-statistic: | 1.624 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.225 |
| Time: | 06:21:22 | Log-Likelihood: | -74.417 |
| No. Observations: | 15 | AIC: | 152.8 |
| Df Residuals: | 13 | BIC: | 154.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 173.5978 | 63.449 | 2.736 | 0.017 | 36.524 310.671 |
| expression | -19.8341 | 15.564 | -1.274 | 0.225 | -53.458 13.789 |
| Omnibus: | 2.792 | Durbin-Watson: | 1.497 |
| Prob(Omnibus): | 0.248 | Jarque-Bera (JB): | 1.323 |
| Skew: | 0.363 | Prob(JB): | 0.516 |
| Kurtosis: | 1.739 | Cond. No. | 28.6 |