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 |
| 0.297 | 0.592 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.722 |
| Model: | OLS | Adj. R-squared: | 0.678 |
| Method: | Least Squares | F-statistic: | 16.46 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.63e-05 |
| Time: | 05:27:14 | Log-Likelihood: | -98.378 |
| No. Observations: | 23 | AIC: | 204.8 |
| Df Residuals: | 19 | BIC: | 209.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 15.0911 | 126.902 | 0.119 | 0.907 | -250.517 280.699 |
| C(dose)[T.1] | 800.2368 | 344.034 | 2.326 | 0.031 | 80.166 1520.308 |
| expression | 4.1064 | 13.309 | 0.309 | 0.761 | -23.750 31.963 |
| expression:C(dose)[T.1] | -72.4981 | 33.639 | -2.155 | 0.044 | -142.905 -2.091 |
| Omnibus: | 0.127 | Durbin-Watson: | 1.497 |
| Prob(Omnibus): | 0.939 | Jarque-Bera (JB): | 0.197 |
| Skew: | 0.146 | Prob(JB): | 0.906 |
| Kurtosis: | 2.654 | Cond. No. | 1.01e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 18.92 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.45e-05 |
| Time: | 05:27:14 | Log-Likelihood: | -100.89 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 123.1958 | 126.745 | 0.972 | 0.343 | -141.189 387.581 |
| C(dose)[T.1] | 59.2980 | 13.980 | 4.242 | 0.000 | 30.136 88.460 |
| expression | -7.2421 | 13.290 | -0.545 | 0.592 | -34.965 20.481 |
| Omnibus: | 0.921 | Durbin-Watson: | 1.978 |
| Prob(Omnibus): | 0.631 | Jarque-Bera (JB): | 0.751 |
| Skew: | 0.036 | Prob(JB): | 0.687 |
| Kurtosis: | 2.118 | Cond. No. | 294. |
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: | 05:27:14 | 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: | 0.343 |
| Model: | OLS | Adj. R-squared: | 0.312 |
| Method: | Least Squares | F-statistic: | 10.97 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00332 |
| Time: | 05:27:14 | Log-Likelihood: | -108.27 |
| No. Observations: | 23 | AIC: | 220.5 |
| Df Residuals: | 21 | BIC: | 222.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -285.9807 | 110.571 | -2.586 | 0.017 | -515.925 -56.037 |
| expression | 36.8665 | 11.131 | 3.312 | 0.003 | 13.718 60.015 |
| Omnibus: | 1.078 | Durbin-Watson: | 1.796 |
| Prob(Omnibus): | 0.583 | Jarque-Bera (JB): | 1.026 |
| Skew: | 0.391 | Prob(JB): | 0.599 |
| Kurtosis: | 2.323 | Cond. No. | 190. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.000 | 0.988 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.469 |
| Model: | OLS | Adj. R-squared: | 0.324 |
| Method: | Least Squares | F-statistic: | 3.241 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0642 |
| Time: | 05:27:14 | Log-Likelihood: | -70.550 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 151.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -197.4067 | 492.600 | -0.401 | 0.696 | -1281.612 886.799 |
| C(dose)[T.1] | 432.6606 | 589.845 | 0.734 | 0.479 | -865.579 1730.901 |
| expression | 30.7575 | 57.193 | 0.538 | 0.601 | -95.124 156.639 |
| expression:C(dose)[T.1] | -44.2948 | 68.119 | -0.650 | 0.529 | -194.224 105.635 |
| Omnibus: | 2.746 | Durbin-Watson: | 0.686 |
| Prob(Omnibus): | 0.253 | Jarque-Bera (JB): | 1.822 |
| Skew: | -0.840 | Prob(JB): | 0.402 |
| Kurtosis: | 2.693 | Cond. No. | 941. |
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: | 4.885 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0280 |
| Time: | 05:27:14 | Log-Likelihood: | -70.833 |
| 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 | 71.4547 | 261.242 | 0.274 | 0.789 | -497.744 640.653 |
| C(dose)[T.1] | 49.2678 | 16.406 | 3.003 | 0.011 | 13.522 85.013 |
| expression | -0.4676 | 30.311 | -0.015 | 0.988 | -66.509 65.574 |
| Omnibus: | 2.757 | Durbin-Watson: | 0.809 |
| Prob(Omnibus): | 0.252 | Jarque-Bera (JB): | 1.891 |
| Skew: | -0.850 | Prob(JB): | 0.388 |
| Kurtosis: | 2.629 | Cond. No. | 294. |
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: | 05:27:14 | 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: | 0.035 |
| Model: | OLS | Adj. R-squared: | -0.040 |
| Method: | Least Squares | F-statistic: | 0.4649 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.507 |
| Time: | 05:27:14 | Log-Likelihood: | -75.037 |
| No. Observations: | 15 | AIC: | 154.1 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -125.4770 | 321.542 | -0.390 | 0.703 | -820.127 569.173 |
| expression | 25.2125 | 36.976 | 0.682 | 0.507 | -54.669 105.093 |
| Omnibus: | 0.796 | Durbin-Watson: | 1.526 |
| Prob(Omnibus): | 0.672 | Jarque-Bera (JB): | 0.739 |
| Skew: | 0.309 | Prob(JB): | 0.691 |
| Kurtosis: | 2.105 | Cond. No. | 284. |