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 |
| 0.705 | 0.411 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.683 |
| Model: | OLS | Adj. R-squared: | 0.633 |
| Method: | Least Squares | F-statistic: | 13.65 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 5.55e-05 |
| Time: | 23:12:23 | Log-Likelihood: | -99.892 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 19 | BIC: | 212.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 79.1690 | 82.133 | 0.964 | 0.347 | -92.738 251.076 |
| C(dose)[T.1] | 305.7692 | 215.357 | 1.420 | 0.172 | -144.978 756.516 |
| expression | -3.0987 | 10.170 | -0.305 | 0.764 | -24.384 18.187 |
| expression:C(dose)[T.1] | -28.6577 | 24.947 | -1.149 | 0.265 | -80.872 23.557 |
| Omnibus: | 0.564 | Durbin-Watson: | 1.929 |
| Prob(Omnibus): | 0.754 | Jarque-Bera (JB): | 0.655 |
| Skew: | 0.280 | Prob(JB): | 0.721 |
| Kurtosis: | 2.392 | Cond. No. | 501. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.627 |
| Method: | Least Squares | F-statistic: | 19.50 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 2.00e-05 |
| Time: | 23:12:23 | Log-Likelihood: | -100.66 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 117.5315 | 75.635 | 1.554 | 0.136 | -40.240 275.303 |
| C(dose)[T.1] | 58.6805 | 10.713 | 5.477 | 0.000 | 36.333 81.028 |
| expression | -7.8611 | 9.360 | -0.840 | 0.411 | -27.386 11.664 |
| Omnibus: | 0.944 | Durbin-Watson: | 1.975 |
| Prob(Omnibus): | 0.624 | Jarque-Bera (JB): | 0.758 |
| Skew: | 0.029 | Prob(JB): | 0.684 |
| Kurtosis: | 2.112 | Cond. No. | 150. |
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: | 23:12:23 | 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: | 0.152 |
| Model: | OLS | Adj. R-squared: | 0.112 |
| Method: | Least Squares | F-statistic: | 3.778 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0654 |
| Time: | 23:12:23 | Log-Likelihood: | -111.20 |
| No. Observations: | 23 | AIC: | 226.4 |
| Df Residuals: | 21 | BIC: | 228.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -109.5791 | 97.610 | -1.123 | 0.274 | -312.571 93.412 |
| expression | 22.5880 | 11.620 | 1.944 | 0.065 | -1.578 46.754 |
| Omnibus: | 2.209 | Durbin-Watson: | 2.242 |
| Prob(Omnibus): | 0.331 | Jarque-Bera (JB): | 1.211 |
| Skew: | 0.197 | Prob(JB): | 0.546 |
| Kurtosis: | 1.947 | Cond. No. | 125. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.380 | 0.091 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.634 |
| Model: | OLS | Adj. R-squared: | 0.535 |
| Method: | Least Squares | F-statistic: | 6.361 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.00926 |
| Time: | 23:12:23 | Log-Likelihood: | -67.754 |
| No. Observations: | 15 | AIC: | 143.5 |
| Df Residuals: | 11 | BIC: | 146.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 123.4098 | 77.260 | 1.597 | 0.138 | -46.638 293.458 |
| C(dose)[T.1] | 239.0083 | 134.230 | 1.781 | 0.103 | -56.429 534.446 |
| expression | -9.0185 | 12.346 | -0.730 | 0.480 | -36.193 18.156 |
| expression:C(dose)[T.1] | -29.3266 | 21.063 | -1.392 | 0.191 | -75.685 17.032 |
| Omnibus: | 2.257 | Durbin-Watson: | 1.434 |
| Prob(Omnibus): | 0.324 | Jarque-Bera (JB): | 1.325 |
| Skew: | -0.723 | Prob(JB): | 0.516 |
| Kurtosis: | 2.834 | Cond. No. | 163. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.570 |
| Model: | OLS | Adj. R-squared: | 0.498 |
| Method: | Least Squares | F-statistic: | 7.951 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.00633 |
| Time: | 23:12:23 | Log-Likelihood: | -68.972 |
| No. Observations: | 15 | AIC: | 143.9 |
| Df Residuals: | 12 | BIC: | 146.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 185.9597 | 65.269 | 2.849 | 0.015 | 43.752 328.168 |
| C(dose)[T.1] | 53.0663 | 14.061 | 3.774 | 0.003 | 22.429 83.704 |
| expression | -19.0953 | 10.387 | -1.838 | 0.091 | -41.726 3.535 |
| Omnibus: | 1.908 | Durbin-Watson: | 0.930 |
| Prob(Omnibus): | 0.385 | Jarque-Bera (JB): | 1.158 |
| Skew: | -0.670 | Prob(JB): | 0.561 |
| Kurtosis: | 2.763 | Cond. No. | 61.5 |
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: | 23:12:23 | 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: | 0.059 |
| Model: | OLS | Adj. R-squared: | -0.013 |
| Method: | Least Squares | F-statistic: | 0.8218 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.381 |
| Time: | 23:12:23 | Log-Likelihood: | -74.840 |
| No. Observations: | 15 | AIC: | 153.7 |
| Df Residuals: | 13 | BIC: | 155.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 177.2034 | 92.674 | 1.912 | 0.078 | -23.006 377.413 |
| expression | -13.2274 | 14.591 | -0.907 | 0.381 | -44.749 18.294 |
| Omnibus: | 0.297 | Durbin-Watson: | 1.942 |
| Prob(Omnibus): | 0.862 | Jarque-Bera (JB): | 0.449 |
| Skew: | -0.003 | Prob(JB): | 0.799 |
| Kurtosis: | 2.152 | Cond. No. | 61.2 |