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 |
| 1.076 | 0.312 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.667 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 12.69 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 8.74e-05 |
| Time: | 21:39:56 | Log-Likelihood: | -100.45 |
| No. Observations: | 23 | AIC: | 208.9 |
| Df Residuals: | 19 | BIC: | 213.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -0.3723 | 76.196 | -0.005 | 0.996 | -159.852 159.107 |
| C(dose)[T.1] | 26.2244 | 137.027 | 0.191 | 0.850 | -260.577 313.026 |
| expression | 11.0734 | 15.410 | 0.719 | 0.481 | -21.179 43.326 |
| expression:C(dose)[T.1] | 2.4404 | 24.316 | 0.100 | 0.921 | -48.455 53.335 |
| Omnibus: | 0.230 | Durbin-Watson: | 2.257 |
| Prob(Omnibus): | 0.892 | Jarque-Bera (JB): | 0.423 |
| Skew: | -0.117 | Prob(JB): | 0.809 |
| Kurtosis: | 2.378 | Cond. No. | 223. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.667 |
| Model: | OLS | Adj. R-squared: | 0.634 |
| Method: | Least Squares | F-statistic: | 20.03 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 1.68e-05 |
| Time: | 21:39:56 | Log-Likelihood: | -100.46 |
| No. Observations: | 23 | AIC: | 206.9 |
| Df Residuals: | 20 | BIC: | 210.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -5.2029 | 57.587 | -0.090 | 0.929 | -125.327 114.922 |
| C(dose)[T.1] | 39.8830 | 15.533 | 2.568 | 0.018 | 7.482 72.284 |
| expression | 12.0534 | 11.622 | 1.037 | 0.312 | -12.189 36.296 |
| Omnibus: | 0.311 | Durbin-Watson: | 2.271 |
| Prob(Omnibus): | 0.856 | Jarque-Bera (JB): | 0.482 |
| Skew: | -0.128 | Prob(JB): | 0.786 |
| Kurtosis: | 2.339 | Cond. No. | 79.0 |
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: | 21:39:56 | 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: | 0.557 |
| Model: | OLS | Adj. R-squared: | 0.536 |
| Method: | Least Squares | F-statistic: | 26.42 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 4.31e-05 |
| Time: | 21:39:56 | Log-Likelihood: | -103.74 |
| No. Observations: | 23 | AIC: | 211.5 |
| Df Residuals: | 21 | BIC: | 213.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -122.2719 | 39.587 | -3.089 | 0.006 | -204.597 -39.947 |
| expression | 36.9752 | 7.193 | 5.140 | 0.000 | 22.016 51.934 |
| Omnibus: | 0.512 | Durbin-Watson: | 2.901 |
| Prob(Omnibus): | 0.774 | Jarque-Bera (JB): | 0.591 |
| Skew: | 0.091 | Prob(JB): | 0.744 |
| Kurtosis: | 2.236 | Cond. No. | 46.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.065 | 0.803 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.480 |
| Model: | OLS | Adj. R-squared: | 0.338 |
| Method: | Least Squares | F-statistic: | 3.381 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0580 |
| Time: | 21:39:56 | Log-Likelihood: | -70.400 |
| No. Observations: | 15 | AIC: | 148.8 |
| Df Residuals: | 11 | BIC: | 151.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 3.8015 | 139.819 | 0.027 | 0.979 | -303.937 311.540 |
| C(dose)[T.1] | 186.6748 | 178.626 | 1.045 | 0.318 | -206.478 579.828 |
| expression | 15.3015 | 33.507 | 0.457 | 0.657 | -58.448 89.050 |
| expression:C(dose)[T.1] | -32.5048 | 42.271 | -0.769 | 0.458 | -125.543 60.534 |
| Omnibus: | 6.227 | Durbin-Watson: | 0.901 |
| Prob(Omnibus): | 0.044 | Jarque-Bera (JB): | 3.618 |
| Skew: | -1.177 | Prob(JB): | 0.164 |
| Kurtosis: | 3.503 | Cond. No. | 142. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 4.944 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0272 |
| Time: | 21:39:56 | Log-Likelihood: | -70.792 |
| 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 | 88.7278 | 84.268 | 1.053 | 0.313 | -94.875 272.331 |
| C(dose)[T.1] | 49.8859 | 15.928 | 3.132 | 0.009 | 15.182 84.590 |
| expression | -5.1222 | 20.077 | -0.255 | 0.803 | -48.866 38.621 |
| Omnibus: | 3.439 | Durbin-Watson: | 0.798 |
| Prob(Omnibus): | 0.179 | Jarque-Bera (JB): | 2.162 |
| Skew: | -0.927 | Prob(JB): | 0.339 |
| Kurtosis: | 2.855 | Cond. No. | 48.6 |
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: | 21:39:56 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.073 |
| Method: | Least Squares | F-statistic: | 0.04685 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.832 |
| Time: | 21:39:56 | Log-Likelihood: | -75.273 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 70.2023 | 108.877 | 0.645 | 0.530 | -165.011 305.416 |
| expression | 5.5471 | 25.627 | 0.216 | 0.832 | -49.817 60.911 |
| Omnibus: | 0.643 | Durbin-Watson: | 1.595 |
| Prob(Omnibus): | 0.725 | Jarque-Bera (JB): | 0.606 |
| Skew: | 0.113 | Prob(JB): | 0.738 |
| Kurtosis: | 2.041 | Cond. No. | 48.1 |