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 |
| 0.527 | 0.476 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.675 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 13.13 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.06e-05 |
| Time: | 03:56:48 | Log-Likelihood: | -100.19 |
| No. Observations: | 23 | AIC: | 208.4 |
| Df Residuals: | 19 | BIC: | 212.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 80.8020 | 227.433 | 0.355 | 0.726 | -395.220 556.824 |
| C(dose)[T.1] | 489.8877 | 443.192 | 1.105 | 0.283 | -437.724 1417.500 |
| expression | -2.7041 | 23.118 | -0.117 | 0.908 | -51.091 45.683 |
| expression:C(dose)[T.1] | -44.3675 | 45.041 | -0.985 | 0.337 | -138.639 49.904 |
| Omnibus: | 1.444 | Durbin-Watson: | 1.802 |
| Prob(Omnibus): | 0.486 | Jarque-Bera (JB): | 0.956 |
| Skew: | 0.134 | Prob(JB): | 0.620 |
| Kurtosis: | 2.038 | Cond. No. | 1.20e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 19.25 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.18e-05 |
| Time: | 03:56:48 | Log-Likelihood: | -100.76 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 195.7513 | 195.068 | 1.004 | 0.328 | -211.154 602.657 |
| C(dose)[T.1] | 53.4044 | 8.657 | 6.169 | 0.000 | 35.346 71.463 |
| expression | -14.3925 | 19.826 | -0.726 | 0.476 | -55.749 26.963 |
| Omnibus: | 1.082 | Durbin-Watson: | 1.871 |
| Prob(Omnibus): | 0.582 | Jarque-Bera (JB): | 0.805 |
| Skew: | 0.017 | Prob(JB): | 0.669 |
| Kurtosis: | 2.084 | Cond. No. | 449. |
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: | 03:56:48 | 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: | 0.007 |
| Model: | OLS | Adj. R-squared: | -0.040 |
| Method: | Least Squares | F-statistic: | 0.1575 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.695 |
| Time: | 03:56:48 | Log-Likelihood: | -113.02 |
| No. Observations: | 23 | AIC: | 230.0 |
| Df Residuals: | 21 | BIC: | 232.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 208.4165 | 324.321 | 0.643 | 0.527 | -466.045 882.878 |
| expression | -13.0836 | 32.962 | -0.397 | 0.695 | -81.633 55.465 |
| Omnibus: | 2.907 | Durbin-Watson: | 2.519 |
| Prob(Omnibus): | 0.234 | Jarque-Bera (JB): | 1.424 |
| Skew: | 0.245 | Prob(JB): | 0.491 |
| Kurtosis: | 1.884 | Cond. No. | 448. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.825 | 0.382 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.486 |
| Model: | OLS | Adj. R-squared: | 0.346 |
| Method: | Least Squares | F-statistic: | 3.472 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0543 |
| Time: | 03:56:48 | Log-Likelihood: | -70.303 |
| No. Observations: | 15 | AIC: | 148.6 |
| Df Residuals: | 11 | BIC: | 151.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -296.4770 | 658.283 | -0.450 | 0.661 | -1745.348 1152.394 |
| C(dose)[T.1] | -198.5081 | 1087.030 | -0.183 | 0.858 | -2591.045 2194.029 |
| expression | 38.1306 | 68.965 | 0.553 | 0.591 | -113.661 189.922 |
| expression:C(dose)[T.1] | 24.0256 | 111.730 | 0.215 | 0.834 | -221.889 269.941 |
| Omnibus: | 3.103 | Durbin-Watson: | 0.969 |
| Prob(Omnibus): | 0.212 | Jarque-Bera (JB): | 1.629 |
| Skew: | -0.522 | Prob(JB): | 0.443 |
| Kurtosis: | 1.768 | Cond. No. | 1.70e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.484 |
| Model: | OLS | Adj. R-squared: | 0.398 |
| Method: | Least Squares | F-statistic: | 5.633 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0188 |
| Time: | 03:56:48 | Log-Likelihood: | -70.334 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 12 | BIC: | 148.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -383.8373 | 496.954 | -0.772 | 0.455 | -1466.606 698.932 |
| C(dose)[T.1] | 35.1896 | 21.671 | 1.624 | 0.130 | -12.026 82.406 |
| expression | 47.2844 | 52.059 | 0.908 | 0.382 | -66.142 160.710 |
| Omnibus: | 2.807 | Durbin-Watson: | 0.966 |
| Prob(Omnibus): | 0.246 | Jarque-Bera (JB): | 1.580 |
| Skew: | -0.529 | Prob(JB): | 0.454 |
| Kurtosis: | 1.813 | Cond. No. | 643. |
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: | 03:56:48 | 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: | 0.371 |
| Model: | OLS | Adj. R-squared: | 0.323 |
| Method: | Least Squares | F-statistic: | 7.664 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0160 |
| Time: | 03:56:48 | Log-Likelihood: | -71.824 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 13 | BIC: | 149.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -948.6881 | 376.598 | -2.519 | 0.026 | -1762.278 -135.098 |
| expression | 107.4411 | 38.809 | 2.768 | 0.016 | 23.599 191.283 |
| Omnibus: | 6.670 | Durbin-Watson: | 1.662 |
| Prob(Omnibus): | 0.036 | Jarque-Bera (JB): | 1.567 |
| Skew: | -0.052 | Prob(JB): | 0.457 |
| Kurtosis: | 1.420 | Cond. No. | 458. |