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 |
| 0.149 | 0.703 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.670 |
| Model: | OLS | Adj. R-squared: | 0.617 |
| Method: | Least Squares | F-statistic: | 12.83 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 8.17e-05 |
| Time: | 22:49:23 | Log-Likelihood: | -100.37 |
| No. Observations: | 23 | AIC: | 208.7 |
| Df Residuals: | 19 | BIC: | 213.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 16.2548 | 89.117 | 0.182 | 0.857 | -170.270 202.780 |
| C(dose)[T.1] | 182.3377 | 127.685 | 1.428 | 0.170 | -84.909 449.585 |
| expression | 5.4299 | 12.720 | 0.427 | 0.674 | -21.194 32.054 |
| expression:C(dose)[T.1] | -18.5653 | 18.304 | -1.014 | 0.323 | -56.875 19.744 |
| Omnibus: | 0.178 | Durbin-Watson: | 1.825 |
| Prob(Omnibus): | 0.915 | Jarque-Bera (JB): | 0.089 |
| Skew: | -0.118 | Prob(JB): | 0.956 |
| Kurtosis: | 2.807 | Cond. No. | 268. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.617 |
| Method: | Least Squares | F-statistic: | 18.71 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.63e-05 |
| Time: | 22:49:23 | Log-Likelihood: | -100.98 |
| 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 | 78.9308 | 64.262 | 1.228 | 0.234 | -55.117 212.979 |
| C(dose)[T.1] | 53.1309 | 8.754 | 6.070 | 0.000 | 34.871 71.391 |
| expression | -3.5370 | 9.153 | -0.386 | 0.703 | -22.630 15.556 |
| Omnibus: | 0.063 | Durbin-Watson: | 1.920 |
| Prob(Omnibus): | 0.969 | Jarque-Bera (JB): | 0.284 |
| Skew: | 0.031 | Prob(JB): | 0.868 |
| Kurtosis: | 2.459 | Cond. No. | 105. |
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: | 22:49:24 | 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: | -0.037 |
| Method: | Least Squares | F-statistic: | 0.2122 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.650 |
| Time: | 22:49:24 | Log-Likelihood: | -112.99 |
| 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 | 127.9194 | 104.886 | 1.220 | 0.236 | -90.204 346.043 |
| expression | -6.9238 | 15.031 | -0.461 | 0.650 | -38.182 24.334 |
| Omnibus: | 3.394 | Durbin-Watson: | 2.545 |
| Prob(Omnibus): | 0.183 | Jarque-Bera (JB): | 1.479 |
| Skew: | 0.212 | Prob(JB): | 0.477 |
| Kurtosis: | 1.832 | Cond. No. | 104. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.000 | 0.983 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.543 |
| Model: | OLS | Adj. R-squared: | 0.418 |
| Method: | Least Squares | F-statistic: | 4.356 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0298 |
| Time: | 22:49:24 | Log-Likelihood: | -69.428 |
| No. Observations: | 15 | AIC: | 146.9 |
| Df Residuals: | 11 | BIC: | 149.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -166.5217 | 175.456 | -0.949 | 0.363 | -552.697 219.653 |
| C(dose)[T.1] | 348.6814 | 199.531 | 1.748 | 0.108 | -90.483 787.845 |
| expression | 37.6114 | 28.153 | 1.336 | 0.209 | -24.352 99.575 |
| expression:C(dose)[T.1] | -47.3810 | 31.478 | -1.505 | 0.160 | -116.664 21.902 |
| Omnibus: | 1.416 | Durbin-Watson: | 1.092 |
| Prob(Omnibus): | 0.493 | Jarque-Bera (JB): | 1.143 |
| Skew: | -0.592 | Prob(JB): | 0.565 |
| Kurtosis: | 2.347 | Cond. No. | 272. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.885 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0280 |
| Time: | 22:49:24 | Log-Likelihood: | -70.833 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 69.2147 | 83.165 | 0.832 | 0.422 | -111.987 250.417 |
| C(dose)[T.1] | 49.3365 | 17.013 | 2.900 | 0.013 | 12.268 86.405 |
| expression | -0.2871 | 13.242 | -0.022 | 0.983 | -29.139 28.564 |
| Omnibus: | 2.690 | Durbin-Watson: | 0.804 |
| Prob(Omnibus): | 0.260 | Jarque-Bera (JB): | 1.867 |
| Skew: | -0.841 | Prob(JB): | 0.393 |
| Kurtosis: | 2.602 | Cond. No. | 71.0 |
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: | 22:49:24 | 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: | 0.063 |
| Model: | OLS | Adj. R-squared: | -0.010 |
| Method: | Least Squares | F-statistic: | 0.8670 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.369 |
| Time: | 22:49:24 | Log-Likelihood: | -74.816 |
| No. Observations: | 15 | AIC: | 153.6 |
| Df Residuals: | 13 | BIC: | 155.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 1.0464 | 99.955 | 0.010 | 0.992 | -214.893 216.986 |
| expression | 14.2924 | 15.349 | 0.931 | 0.369 | -18.868 47.453 |
| Omnibus: | 0.313 | Durbin-Watson: | 1.597 |
| Prob(Omnibus): | 0.855 | Jarque-Bera (JB): | 0.452 |
| Skew: | 0.238 | Prob(JB): | 0.798 |
| Kurtosis: | 2.295 | Cond. No. | 67.7 |