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 |
| 0.077 | 0.784 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.734 |
| Model: | OLS | Adj. R-squared: | 0.693 |
| Method: | Least Squares | F-statistic: | 17.52 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 1.07e-05 |
| Time: | 21:34:49 | Log-Likelihood: | -97.856 |
| No. Observations: | 23 | AIC: | 203.7 |
| Df Residuals: | 19 | BIC: | 208.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 2.3175 | 31.340 | 0.074 | 0.942 | -63.277 67.912 |
| C(dose)[T.1] | 179.1970 | 51.769 | 3.461 | 0.003 | 70.844 287.551 |
| expression | 17.8208 | 10.601 | 1.681 | 0.109 | -4.368 40.009 |
| expression:C(dose)[T.1] | -44.6400 | 18.204 | -2.452 | 0.024 | -82.741 -6.539 |
| Omnibus: | 0.973 | Durbin-Watson: | 2.522 |
| Prob(Omnibus): | 0.615 | Jarque-Bera (JB): | 0.175 |
| Skew: | -0.151 | Prob(JB): | 0.916 |
| Kurtosis: | 3.303 | Cond. No. | 51.5 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.60 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 2.73e-05 |
| Time: | 21:34:49 | Log-Likelihood: | -101.02 |
| 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 | 46.4003 | 28.709 | 1.616 | 0.122 | -13.486 106.286 |
| C(dose)[T.1] | 53.7494 | 8.877 | 6.055 | 0.000 | 35.231 72.267 |
| expression | 2.6815 | 9.638 | 0.278 | 0.784 | -17.423 22.786 |
| Omnibus: | 0.255 | Durbin-Watson: | 1.913 |
| Prob(Omnibus): | 0.880 | Jarque-Bera (JB): | 0.444 |
| Skew: | 0.098 | Prob(JB): | 0.801 |
| Kurtosis: | 2.349 | Cond. No. | 21.3 |
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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 21:34:49 | 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: | 0.010 |
| Model: | OLS | Adj. R-squared: | -0.038 |
| Method: | Least Squares | F-statistic: | 0.2045 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.656 |
| Time: | 21:34:49 | Log-Likelihood: | -112.99 |
| 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 | 99.7499 | 44.880 | 2.223 | 0.037 | 6.416 193.083 |
| expression | -7.0580 | 15.609 | -0.452 | 0.656 | -39.518 25.402 |
| Omnibus: | 2.371 | Durbin-Watson: | 2.480 |
| Prob(Omnibus): | 0.306 | Jarque-Bera (JB): | 1.184 |
| Skew: | 0.115 | Prob(JB): | 0.553 |
| Kurtosis: | 1.913 | Cond. No. | 20.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.081 | 0.319 | 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: | 0.358 |
| Method: | Least Squares | F-statistic: | 3.599 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0496 |
| Time: | 21:34:49 | Log-Likelihood: | -70.171 |
| No. Observations: | 15 | AIC: | 148.3 |
| Df Residuals: | 11 | BIC: | 151.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 11.0170 | 84.280 | 0.131 | 0.898 | -174.483 196.517 |
| C(dose)[T.1] | 13.3058 | 149.906 | 0.089 | 0.931 | -316.635 343.247 |
| expression | 17.0091 | 25.175 | 0.676 | 0.513 | -38.400 72.419 |
| expression:C(dose)[T.1] | 5.8813 | 39.647 | 0.148 | 0.885 | -81.380 93.143 |
| Omnibus: | 1.000 | Durbin-Watson: | 1.162 |
| Prob(Omnibus): | 0.606 | Jarque-Bera (JB): | 0.833 |
| Skew: | -0.500 | Prob(JB): | 0.659 |
| Kurtosis: | 2.422 | Cond. No. | 97.9 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.494 |
| Model: | OLS | Adj. R-squared: | 0.410 |
| Method: | Least Squares | F-statistic: | 5.865 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0167 |
| Time: | 21:34:49 | Log-Likelihood: | -70.186 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 12 | BIC: | 148.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 3.1523 | 62.790 | 0.050 | 0.961 | -133.655 139.960 |
| C(dose)[T.1] | 35.3238 | 20.131 | 1.755 | 0.105 | -8.538 79.186 |
| expression | 19.3804 | 18.639 | 1.040 | 0.319 | -21.230 59.991 |
| Omnibus: | 1.150 | Durbin-Watson: | 1.126 |
| Prob(Omnibus): | 0.563 | Jarque-Bera (JB): | 0.931 |
| Skew: | -0.538 | Prob(JB): | 0.628 |
| Kurtosis: | 2.423 | Cond. No. | 34.6 |
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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 21:34:49 | 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: | 0.365 |
| Model: | OLS | Adj. R-squared: | 0.316 |
| Method: | Least Squares | F-statistic: | 7.459 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0171 |
| Time: | 21:34:49 | Log-Likelihood: | -71.899 |
| No. Observations: | 15 | AIC: | 147.8 |
| Df Residuals: | 13 | BIC: | 149.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -58.1704 | 56.181 | -1.035 | 0.319 | -179.543 63.202 |
| expression | 41.0557 | 15.032 | 2.731 | 0.017 | 8.580 73.531 |
| Omnibus: | 0.666 | Durbin-Watson: | 1.629 |
| Prob(Omnibus): | 0.717 | Jarque-Bera (JB): | 0.674 |
| Skew: | 0.379 | Prob(JB): | 0.714 |
| Kurtosis: | 2.289 | Cond. No. | 27.7 |