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 |
| 0.901 | 0.354 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.725 |
| Model: | OLS | Adj. R-squared: | 0.682 |
| Method: | Least Squares | F-statistic: | 16.69 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.48e-05 |
| Time: | 05:03:18 | Log-Likelihood: | -98.260 |
| No. Observations: | 23 | AIC: | 204.5 |
| Df Residuals: | 19 | BIC: | 209.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -24.4221 | 38.669 | -0.632 | 0.535 | -105.357 56.513 |
| C(dose)[T.1] | 177.9939 | 59.997 | 2.967 | 0.008 | 52.420 303.568 |
| expression | 17.9356 | 8.730 | 2.054 | 0.054 | -0.337 36.208 |
| expression:C(dose)[T.1] | -29.5388 | 14.417 | -2.049 | 0.055 | -59.714 0.637 |
| Omnibus: | 1.540 | Durbin-Watson: | 1.437 |
| Prob(Omnibus): | 0.463 | Jarque-Bera (JB): | 0.385 |
| Skew: | -0.005 | Prob(JB): | 0.825 |
| Kurtosis: | 3.633 | Cond. No. | 82.3 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.664 |
| Model: | OLS | Adj. R-squared: | 0.631 |
| Method: | Least Squares | F-statistic: | 19.78 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.82e-05 |
| Time: | 05:03:18 | Log-Likelihood: | -100.56 |
| No. Observations: | 23 | AIC: | 207.1 |
| Df Residuals: | 20 | BIC: | 210.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 23.0652 | 33.336 | 0.692 | 0.497 | -46.472 92.603 |
| C(dose)[T.1] | 56.3019 | 9.129 | 6.167 | 0.000 | 37.258 75.345 |
| expression | 7.1037 | 7.483 | 0.949 | 0.354 | -8.504 22.712 |
| Omnibus: | 1.037 | Durbin-Watson: | 1.729 |
| Prob(Omnibus): | 0.595 | Jarque-Bera (JB): | 0.807 |
| Skew: | -0.097 | Prob(JB): | 0.668 |
| Kurtosis: | 2.103 | Cond. No. | 35.1 |
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: | 05:03:18 | 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: | 0.026 |
| Model: | OLS | Adj. R-squared: | -0.021 |
| Method: | Least Squares | F-statistic: | 0.5516 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.466 |
| Time: | 05:03:18 | Log-Likelihood: | -112.81 |
| No. Observations: | 23 | AIC: | 229.6 |
| Df Residuals: | 21 | BIC: | 231.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 116.0420 | 49.425 | 2.348 | 0.029 | 13.256 218.828 |
| expression | -8.6809 | 11.688 | -0.743 | 0.466 | -32.988 15.626 |
| Omnibus: | 2.489 | Durbin-Watson: | 2.521 |
| Prob(Omnibus): | 0.288 | Jarque-Bera (JB): | 1.233 |
| Skew: | 0.148 | Prob(JB): | 0.540 |
| Kurtosis: | 1.905 | Cond. No. | 30.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.738 | 0.212 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.519 |
| Model: | OLS | Adj. R-squared: | 0.388 |
| Method: | Least Squares | F-statistic: | 3.962 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0386 |
| Time: | 05:03:18 | Log-Likelihood: | -69.806 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 11 | BIC: | 150.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -20.3953 | 99.550 | -0.205 | 0.841 | -239.503 198.713 |
| C(dose)[T.1] | 68.0179 | 125.626 | 0.541 | 0.599 | -208.484 344.520 |
| expression | 16.6746 | 18.781 | 0.888 | 0.394 | -24.661 58.011 |
| expression:C(dose)[T.1] | -3.2933 | 23.862 | -0.138 | 0.893 | -55.813 49.227 |
| Omnibus: | 3.117 | Durbin-Watson: | 1.074 |
| Prob(Omnibus): | 0.210 | Jarque-Bera (JB): | 1.749 |
| Skew: | -0.836 | Prob(JB): | 0.417 |
| Kurtosis: | 3.007 | Cond. No. | 125. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.518 |
| Model: | OLS | Adj. R-squared: | 0.438 |
| Method: | Least Squares | F-statistic: | 6.461 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0125 |
| Time: | 05:03:18 | Log-Likelihood: | -69.819 |
| No. Observations: | 15 | AIC: | 145.6 |
| Df Residuals: | 12 | BIC: | 147.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -9.6505 | 59.452 | -0.162 | 0.874 | -139.185 119.884 |
| C(dose)[T.1] | 50.8104 | 14.761 | 3.442 | 0.005 | 18.648 82.973 |
| expression | 14.6345 | 11.102 | 1.318 | 0.212 | -9.554 38.824 |
| Omnibus: | 3.092 | Durbin-Watson: | 1.082 |
| Prob(Omnibus): | 0.213 | Jarque-Bera (JB): | 1.722 |
| Skew: | -0.830 | Prob(JB): | 0.423 |
| Kurtosis: | 3.015 | Cond. No. | 44.3 |
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: | 05:03:18 | 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: | 0.043 |
| Model: | OLS | Adj. R-squared: | -0.031 |
| Method: | Least Squares | F-statistic: | 0.5854 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.458 |
| Time: | 05:03:18 | Log-Likelihood: | -74.970 |
| No. Observations: | 15 | AIC: | 153.9 |
| Df Residuals: | 13 | BIC: | 155.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 33.9563 | 78.674 | 0.432 | 0.673 | -136.008 203.920 |
| expression | 11.4649 | 14.985 | 0.765 | 0.458 | -20.908 43.838 |
| Omnibus: | 0.328 | Durbin-Watson: | 1.896 |
| Prob(Omnibus): | 0.849 | Jarque-Bera (JB): | 0.470 |
| Skew: | 0.110 | Prob(JB): | 0.791 |
| Kurtosis: | 2.162 | Cond. No. | 43.0 |