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 |
| 0.088 | 0.770 | 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: | 0.606 |
| Method: | Least Squares | F-statistic: | 12.26 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000108 |
| Time: | 04:56:28 | Log-Likelihood: | -100.72 |
| No. Observations: | 23 | AIC: | 209.4 |
| Df Residuals: | 19 | BIC: | 214.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 78.4506 | 66.782 | 1.175 | 0.255 | -61.326 218.227 |
| C(dose)[T.1] | -4.9545 | 84.344 | -0.059 | 0.954 | -181.488 171.579 |
| expression | -3.9214 | 10.757 | -0.365 | 0.719 | -26.436 18.593 |
| expression:C(dose)[T.1] | 9.6509 | 13.773 | 0.701 | 0.492 | -19.177 38.478 |
| Omnibus: | 0.656 | Durbin-Watson: | 2.063 |
| Prob(Omnibus): | 0.720 | Jarque-Bera (JB): | 0.651 |
| Skew: | 0.061 | Prob(JB): | 0.722 |
| Kurtosis: | 2.185 | Cond. No. | 163. |
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: | 18.62 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.71e-05 |
| Time: | 04:56:28 | 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 | 42.0583 | 41.443 | 1.015 | 0.322 | -44.391 128.508 |
| C(dose)[T.1] | 53.8074 | 8.893 | 6.050 | 0.000 | 35.256 72.359 |
| expression | 1.9653 | 6.632 | 0.296 | 0.770 | -11.869 15.799 |
| Omnibus: | 0.479 | Durbin-Watson: | 1.820 |
| Prob(Omnibus): | 0.787 | Jarque-Bera (JB): | 0.569 |
| Skew: | 0.052 | Prob(JB): | 0.752 |
| Kurtosis: | 2.237 | Cond. No. | 59.6 |
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: | 04:56:28 | 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: | 0.011 |
| Model: | OLS | Adj. R-squared: | -0.036 |
| Method: | Least Squares | F-statistic: | 0.2351 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.633 |
| Time: | 04:56:28 | Log-Likelihood: | -112.98 |
| No. Observations: | 23 | AIC: | 230.0 |
| Df Residuals: | 21 | BIC: | 232.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 111.2394 | 65.401 | 1.701 | 0.104 | -24.769 247.248 |
| expression | -5.1951 | 10.713 | -0.485 | 0.633 | -27.475 17.085 |
| Omnibus: | 2.660 | Durbin-Watson: | 2.602 |
| Prob(Omnibus): | 0.264 | Jarque-Bera (JB): | 1.513 |
| Skew: | 0.340 | Prob(JB): | 0.469 |
| Kurtosis: | 1.943 | Cond. No. | 57.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.083 | 0.779 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.456 |
| Model: | OLS | Adj. R-squared: | 0.308 |
| Method: | Least Squares | F-statistic: | 3.074 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0727 |
| Time: | 04:56:28 | Log-Likelihood: | -70.733 |
| No. Observations: | 15 | AIC: | 149.5 |
| Df Residuals: | 11 | BIC: | 152.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 116.3571 | 129.037 | 0.902 | 0.387 | -167.652 400.366 |
| C(dose)[T.1] | -7.4924 | 210.355 | -0.036 | 0.972 | -470.480 455.495 |
| expression | -6.3467 | 16.666 | -0.381 | 0.711 | -43.029 30.336 |
| expression:C(dose)[T.1] | 7.3817 | 27.685 | 0.267 | 0.795 | -53.553 68.316 |
| Omnibus: | 2.867 | Durbin-Watson: | 0.701 |
| Prob(Omnibus): | 0.238 | Jarque-Bera (JB): | 1.958 |
| Skew: | -0.867 | Prob(JB): | 0.376 |
| Kurtosis: | 2.644 | Cond. No. | 251. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.453 |
| Model: | OLS | Adj. R-squared: | 0.361 |
| Method: | Least Squares | F-statistic: | 4.960 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0269 |
| Time: | 04:56:28 | Log-Likelihood: | -70.782 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 95.7339 | 99.208 | 0.965 | 0.354 | -120.422 311.889 |
| C(dose)[T.1] | 48.4204 | 15.917 | 3.042 | 0.010 | 13.741 83.100 |
| expression | -3.6716 | 12.783 | -0.287 | 0.779 | -31.522 24.179 |
| Omnibus: | 2.668 | Durbin-Watson: | 0.701 |
| Prob(Omnibus): | 0.263 | Jarque-Bera (JB): | 1.935 |
| Skew: | -0.843 | Prob(JB): | 0.380 |
| Kurtosis: | 2.498 | Cond. No. | 98.6 |
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: | 04:56:28 | 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: | 0.030 |
| Model: | OLS | Adj. R-squared: | -0.044 |
| Method: | Least Squares | F-statistic: | 0.4067 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.535 |
| Time: | 04:56:28 | Log-Likelihood: | -75.069 |
| No. Observations: | 15 | AIC: | 154.1 |
| Df Residuals: | 13 | BIC: | 155.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 171.7012 | 122.768 | 1.399 | 0.185 | -93.523 436.925 |
| expression | -10.2724 | 16.107 | -0.638 | 0.535 | -45.070 24.525 |
| Omnibus: | 0.938 | Durbin-Watson: | 1.495 |
| Prob(Omnibus): | 0.626 | Jarque-Bera (JB): | 0.694 |
| Skew: | 0.065 | Prob(JB): | 0.707 |
| Kurtosis: | 1.954 | Cond. No. | 95.1 |