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 |
| 1.379 | 0.254 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.672 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 13.00 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 7.55e-05 |
| Time: | 22:53:52 | Log-Likelihood: | -100.27 |
| No. Observations: | 23 | AIC: | 208.5 |
| Df Residuals: | 19 | BIC: | 213.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 101.4238 | 51.994 | 1.951 | 0.066 | -7.402 210.249 |
| C(dose)[T.1] | 68.5297 | 101.457 | 0.675 | 0.508 | -143.822 280.882 |
| expression | -8.6206 | 9.429 | -0.914 | 0.372 | -28.357 11.115 |
| expression:C(dose)[T.1] | -3.8978 | 19.817 | -0.197 | 0.846 | -45.376 37.580 |
| Omnibus: | 0.157 | Durbin-Watson: | 1.908 |
| Prob(Omnibus): | 0.924 | Jarque-Bera (JB): | 0.022 |
| Skew: | 0.028 | Prob(JB): | 0.989 |
| Kurtosis: | 2.861 | Cond. No. | 148. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.672 |
| Model: | OLS | Adj. R-squared: | 0.639 |
| Method: | Least Squares | F-statistic: | 20.46 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.45e-05 |
| Time: | 22:53:52 | Log-Likelihood: | -100.30 |
| No. Observations: | 23 | AIC: | 206.6 |
| Df Residuals: | 20 | BIC: | 210.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 106.2572 | 44.706 | 2.377 | 0.028 | 13.002 199.512 |
| C(dose)[T.1] | 48.6640 | 9.369 | 5.194 | 0.000 | 29.120 68.208 |
| expression | -9.5030 | 8.092 | -1.174 | 0.254 | -26.382 7.376 |
| Omnibus: | 0.127 | Durbin-Watson: | 1.857 |
| Prob(Omnibus): | 0.938 | Jarque-Bera (JB): | 0.034 |
| Skew: | 0.037 | Prob(JB): | 0.983 |
| Kurtosis: | 2.828 | Cond. No. | 58.1 |
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: | 22:53:52 | 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: | 0.229 |
| Model: | OLS | Adj. R-squared: | 0.192 |
| Method: | Least Squares | F-statistic: | 6.232 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0209 |
| Time: | 22:53:52 | Log-Likelihood: | -110.12 |
| No. Observations: | 23 | AIC: | 224.2 |
| Df Residuals: | 21 | BIC: | 226.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 223.0985 | 57.784 | 3.861 | 0.001 | 102.930 343.267 |
| expression | -27.3529 | 10.957 | -2.496 | 0.021 | -50.139 -4.567 |
| Omnibus: | 1.552 | Durbin-Watson: | 2.021 |
| Prob(Omnibus): | 0.460 | Jarque-Bera (JB): | 1.004 |
| Skew: | 0.159 | Prob(JB): | 0.605 |
| Kurtosis: | 2.027 | Cond. No. | 49.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.092 | 0.317 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.506 |
| Model: | OLS | Adj. R-squared: | 0.371 |
| Method: | Least Squares | F-statistic: | 3.757 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0444 |
| Time: | 22:53:53 | Log-Likelihood: | -70.009 |
| No. Observations: | 15 | AIC: | 148.0 |
| Df Residuals: | 11 | BIC: | 150.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -83.7816 | 134.736 | -0.622 | 0.547 | -380.334 212.770 |
| C(dose)[T.1] | 179.4551 | 268.214 | 0.669 | 0.517 | -410.880 769.791 |
| expression | 27.2658 | 24.209 | 1.126 | 0.284 | -26.017 80.549 |
| expression:C(dose)[T.1] | -23.6219 | 47.006 | -0.503 | 0.625 | -127.082 79.838 |
| Omnibus: | 3.785 | Durbin-Watson: | 1.200 |
| Prob(Omnibus): | 0.151 | Jarque-Bera (JB): | 2.250 |
| Skew: | -0.949 | Prob(JB): | 0.325 |
| Kurtosis: | 3.005 | Cond. No. | 245. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.495 |
| Model: | OLS | Adj. R-squared: | 0.411 |
| Method: | Least Squares | F-statistic: | 5.875 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0166 |
| Time: | 22:53:53 | Log-Likelihood: | -70.180 |
| 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 | -49.0353 | 111.982 | -0.438 | 0.669 | -293.024 194.953 |
| C(dose)[T.1] | 44.9140 | 15.616 | 2.876 | 0.014 | 10.890 78.938 |
| expression | 21.0005 | 20.095 | 1.045 | 0.317 | -22.782 64.783 |
| Omnibus: | 7.093 | Durbin-Watson: | 1.102 |
| Prob(Omnibus): | 0.029 | Jarque-Bera (JB): | 4.056 |
| Skew: | -1.211 | Prob(JB): | 0.132 |
| Kurtosis: | 3.790 | Cond. No. | 87.5 |
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: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:53:53 | 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: | 0.146 |
| Model: | OLS | Adj. R-squared: | 0.081 |
| Method: | Least Squares | F-statistic: | 2.231 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.159 |
| Time: | 22:53:53 | Log-Likelihood: | -74.112 |
| No. Observations: | 15 | AIC: | 152.2 |
| Df Residuals: | 13 | BIC: | 153.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -110.8357 | 137.241 | -0.808 | 0.434 | -407.326 185.655 |
| expression | 36.1661 | 24.214 | 1.494 | 0.159 | -16.145 88.477 |
| Omnibus: | 1.055 | Durbin-Watson: | 2.191 |
| Prob(Omnibus): | 0.590 | Jarque-Bera (JB): | 0.723 |
| Skew: | -0.021 | Prob(JB): | 0.697 |
| Kurtosis: | 1.925 | Cond. No. | 85.4 |