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 |
| 0.350 | 0.561 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.605 |
| Method: | Least Squares | F-statistic: | 12.23 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000110 |
| Time: | 22:57:32 | Log-Likelihood: | -100.74 |
| No. Observations: | 23 | AIC: | 209.5 |
| Df Residuals: | 19 | BIC: | 214.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -128.0806 | 250.770 | -0.511 | 0.615 | -652.947 396.786 |
| C(dose)[T.1] | 210.0055 | 338.370 | 0.621 | 0.542 | -498.210 918.221 |
| expression | 18.4267 | 25.342 | 0.727 | 0.476 | -34.614 71.467 |
| expression:C(dose)[T.1] | -15.7467 | 34.734 | -0.453 | 0.655 | -88.446 56.953 |
| Omnibus: | 0.107 | Durbin-Watson: | 1.798 |
| Prob(Omnibus): | 0.948 | Jarque-Bera (JB): | 0.282 |
| Skew: | 0.127 | Prob(JB): | 0.869 |
| Kurtosis: | 2.521 | Cond. No. | 988. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.655 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 18.99 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.38e-05 |
| Time: | 22:57:32 | Log-Likelihood: | -100.86 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -45.1612 | 168.115 | -0.269 | 0.791 | -395.842 305.520 |
| C(dose)[T.1] | 56.6806 | 10.370 | 5.466 | 0.000 | 35.049 78.313 |
| expression | 10.0448 | 16.983 | 0.591 | 0.561 | -25.381 45.471 |
| Omnibus: | 0.181 | Durbin-Watson: | 1.813 |
| Prob(Omnibus): | 0.913 | Jarque-Bera (JB): | 0.382 |
| Skew: | 0.119 | Prob(JB): | 0.826 |
| Kurtosis: | 2.415 | Cond. No. | 382. |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:57:32 | 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: | 0.140 |
| Model: | OLS | Adj. R-squared: | 0.099 |
| Method: | Least Squares | F-statistic: | 3.416 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0787 |
| Time: | 22:57:32 | Log-Likelihood: | -111.37 |
| No. Observations: | 23 | AIC: | 226.7 |
| Df Residuals: | 21 | BIC: | 229.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 474.4423 | 213.677 | 2.220 | 0.038 | 30.078 918.807 |
| expression | -40.5535 | 21.942 | -1.848 | 0.079 | -86.185 5.078 |
| Omnibus: | 2.194 | Durbin-Watson: | 2.635 |
| Prob(Omnibus): | 0.334 | Jarque-Bera (JB): | 1.256 |
| Skew: | 0.242 | Prob(JB): | 0.534 |
| Kurtosis: | 1.963 | Cond. No. | 314. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.712 | 0.415 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.497 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 3.623 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0488 |
| Time: | 22:57:33 | Log-Likelihood: | -70.146 |
| No. Observations: | 15 | AIC: | 148.3 |
| Df Residuals: | 11 | BIC: | 151.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -24.1156 | 658.679 | -0.037 | 0.971 | -1473.858 1425.626 |
| C(dose)[T.1] | -534.2538 | 934.949 | -0.571 | 0.579 | -2592.062 1523.555 |
| expression | 9.7487 | 70.133 | 0.139 | 0.892 | -144.614 164.111 |
| expression:C(dose)[T.1] | 60.7181 | 98.569 | 0.616 | 0.550 | -156.230 277.666 |
| Omnibus: | 2.442 | Durbin-Watson: | 0.687 |
| Prob(Omnibus): | 0.295 | Jarque-Bera (JB): | 1.700 |
| Skew: | -0.649 | Prob(JB): | 0.427 |
| Kurtosis: | 1.982 | Cond. No. | 1.53e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.480 |
| Model: | OLS | Adj. R-squared: | 0.393 |
| Method: | Least Squares | F-statistic: | 5.530 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0199 |
| Time: | 22:57:33 | Log-Likelihood: | -70.401 |
| No. Observations: | 15 | AIC: | 146.8 |
| Df Residuals: | 12 | BIC: | 148.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -312.7671 | 450.777 | -0.694 | 0.501 | -1294.925 669.391 |
| C(dose)[T.1] | 41.5645 | 17.768 | 2.339 | 0.037 | 2.852 80.277 |
| expression | 40.4877 | 47.989 | 0.844 | 0.415 | -64.072 145.047 |
| Omnibus: | 2.723 | Durbin-Watson: | 0.704 |
| Prob(Omnibus): | 0.256 | Jarque-Bera (JB): | 2.049 |
| Skew: | -0.798 | Prob(JB): | 0.359 |
| Kurtosis: | 2.144 | Cond. No. | 568. |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:57:33 | 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: | 0.242 |
| Model: | OLS | Adj. R-squared: | 0.184 |
| Method: | Least Squares | F-statistic: | 4.158 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0623 |
| Time: | 22:57:33 | Log-Likelihood: | -73.219 |
| No. Observations: | 15 | AIC: | 150.4 |
| Df Residuals: | 13 | BIC: | 151.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -833.0554 | 454.557 | -1.833 | 0.090 | -1815.067 148.956 |
| expression | 97.6430 | 47.885 | 2.039 | 0.062 | -5.806 201.092 |
| Omnibus: | 0.242 | Durbin-Watson: | 1.548 |
| Prob(Omnibus): | 0.886 | Jarque-Bera (JB): | 0.421 |
| Skew: | -0.081 | Prob(JB): | 0.810 |
| Kurtosis: | 2.195 | Cond. No. | 493. |