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 |
| 0.096 | 0.760 | 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: | 12.61 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.12e-05 |
| Time: | 04:55:51 | Log-Likelihood: | -100.51 |
| No. Observations: | 23 | AIC: | 209.0 |
| Df Residuals: | 19 | BIC: | 213.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 81.7156 | 32.126 | 2.544 | 0.020 | 14.475 148.956 |
| C(dose)[T.1] | 11.9983 | 46.260 | 0.259 | 0.798 | -84.825 108.821 |
| expression | -6.6446 | 7.620 | -0.872 | 0.394 | -22.594 9.305 |
| expression:C(dose)[T.1] | 9.8080 | 10.673 | 0.919 | 0.370 | -12.531 32.147 |
| Omnibus: | 0.210 | Durbin-Watson: | 1.869 |
| Prob(Omnibus): | 0.900 | Jarque-Bera (JB): | 0.413 |
| Skew: | -0.032 | Prob(JB): | 0.813 |
| Kurtosis: | 2.347 | Cond. No. | 62.5 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.63 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.70e-05 |
| Time: | 04:55:51 | Log-Likelihood: | -101.01 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 61.0175 | 22.819 | 2.674 | 0.015 | 13.419 108.616 |
| C(dose)[T.1] | 53.7198 | 8.836 | 6.080 | 0.000 | 35.288 72.151 |
| expression | -1.6448 | 5.315 | -0.309 | 0.760 | -12.731 9.442 |
| Omnibus: | 0.658 | Durbin-Watson: | 1.889 |
| Prob(Omnibus): | 0.720 | Jarque-Bera (JB): | 0.655 |
| Skew: | 0.078 | Prob(JB): | 0.721 |
| Kurtosis: | 2.188 | Cond. No. | 23.9 |
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: | 04:55:52 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.042 |
| Method: | Least Squares | F-statistic: | 0.1102 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.743 |
| Time: | 04:55:52 | Log-Likelihood: | -113.04 |
| 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 | 67.4869 | 37.541 | 1.798 | 0.087 | -10.583 145.557 |
| expression | 2.8770 | 8.667 | 0.332 | 0.743 | -15.147 20.901 |
| Omnibus: | 3.057 | Durbin-Watson: | 2.425 |
| Prob(Omnibus): | 0.217 | Jarque-Bera (JB): | 1.441 |
| Skew: | 0.234 | Prob(JB): | 0.486 |
| Kurtosis: | 1.867 | Cond. No. | 23.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.483 | 0.500 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.495 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 3.593 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0498 |
| Time: | 04:55:52 | Log-Likelihood: | -70.177 |
| No. Observations: | 15 | AIC: | 148.4 |
| Df Residuals: | 11 | BIC: | 151.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 65.4427 | 62.381 | 1.049 | 0.317 | -71.857 202.742 |
| C(dose)[T.1] | -10.0371 | 87.969 | -0.114 | 0.911 | -203.655 183.581 |
| expression | 0.3755 | 11.594 | 0.032 | 0.975 | -25.143 25.894 |
| expression:C(dose)[T.1] | 13.0041 | 17.682 | 0.735 | 0.477 | -25.914 51.923 |
| Omnibus: | 2.691 | Durbin-Watson: | 1.036 |
| Prob(Omnibus): | 0.260 | Jarque-Bera (JB): | 1.987 |
| Skew: | -0.847 | Prob(JB): | 0.370 |
| Kurtosis: | 2.445 | Cond. No. | 75.0 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.470 |
| Model: | OLS | Adj. R-squared: | 0.382 |
| Method: | Least Squares | F-statistic: | 5.323 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0221 |
| Time: | 04:55:52 | Log-Likelihood: | -70.537 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 12 | BIC: | 149.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 35.8762 | 46.777 | 0.767 | 0.458 | -66.042 137.794 |
| C(dose)[T.1] | 53.4488 | 16.601 | 3.220 | 0.007 | 17.279 89.619 |
| expression | 5.9665 | 8.585 | 0.695 | 0.500 | -12.738 24.671 |
| Omnibus: | 1.854 | Durbin-Watson: | 0.854 |
| Prob(Omnibus): | 0.396 | Jarque-Bera (JB): | 1.403 |
| Skew: | -0.692 | Prob(JB): | 0.496 |
| Kurtosis: | 2.427 | Cond. No. | 32.2 |
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: | 04:55:52 | 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: | 0.012 |
| Model: | OLS | Adj. R-squared: | -0.064 |
| Method: | Least Squares | F-statistic: | 0.1626 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.693 |
| Time: | 04:55:52 | Log-Likelihood: | -75.207 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 114.3829 | 52.360 | 2.185 | 0.048 | 1.265 227.500 |
| expression | -4.2208 | 10.468 | -0.403 | 0.693 | -26.835 18.393 |
| Omnibus: | 0.418 | Durbin-Watson: | 1.499 |
| Prob(Omnibus): | 0.812 | Jarque-Bera (JB): | 0.505 |
| Skew: | 0.013 | Prob(JB): | 0.777 |
| Kurtosis: | 2.101 | Cond. No. | 26.9 |