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 |
| 1.814 | 0.193 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.733 |
| Model: | OLS | Adj. R-squared: | 0.691 |
| Method: | Least Squares | F-statistic: | 17.38 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.12e-05 |
| Time: | 22:46:36 | Log-Likelihood: | -97.921 |
| No. Observations: | 23 | AIC: | 203.8 |
| Df Residuals: | 19 | BIC: | 208.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 90.9075 | 115.722 | 0.786 | 0.442 | -151.301 333.116 |
| C(dose)[T.1] | -281.7716 | 169.117 | -1.666 | 0.112 | -635.737 72.194 |
| expression | -5.7840 | 18.218 | -0.317 | 0.754 | -43.916 32.348 |
| expression:C(dose)[T.1] | 52.1041 | 26.412 | 1.973 | 0.063 | -3.177 107.385 |
| Omnibus: | 1.433 | Durbin-Watson: | 1.716 |
| Prob(Omnibus): | 0.488 | Jarque-Bera (JB): | 1.255 |
| Skew: | 0.516 | Prob(JB): | 0.534 |
| Kurtosis: | 2.503 | Cond. No. | 363. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.678 |
| Model: | OLS | Adj. R-squared: | 0.646 |
| Method: | Least Squares | F-statistic: | 21.08 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.19e-05 |
| Time: | 22:46:37 | Log-Likelihood: | -100.06 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 20 | BIC: | 209.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -66.3904 | 89.726 | -0.740 | 0.468 | -253.557 120.776 |
| C(dose)[T.1] | 51.4851 | 8.509 | 6.051 | 0.000 | 33.735 69.235 |
| expression | 19.0072 | 14.112 | 1.347 | 0.193 | -10.430 48.444 |
| Omnibus: | 0.768 | Durbin-Watson: | 1.972 |
| Prob(Omnibus): | 0.681 | Jarque-Bera (JB): | 0.728 |
| Skew: | -0.154 | Prob(JB): | 0.695 |
| Kurtosis: | 2.185 | Cond. No. | 141. |
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: | 22:46:37 | 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: | 0.089 |
| Model: | OLS | Adj. R-squared: | 0.046 |
| Method: | Least Squares | F-statistic: | 2.058 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.166 |
| Time: | 22:46:37 | Log-Likelihood: | -112.03 |
| No. Observations: | 23 | AIC: | 228.1 |
| Df Residuals: | 21 | BIC: | 230.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -129.9550 | 146.305 | -0.888 | 0.384 | -434.213 174.303 |
| expression | 32.8049 | 22.865 | 1.435 | 0.166 | -14.746 80.356 |
| Omnibus: | 0.331 | Durbin-Watson: | 2.240 |
| Prob(Omnibus): | 0.848 | Jarque-Bera (JB): | 0.496 |
| Skew: | -0.144 | Prob(JB): | 0.780 |
| Kurtosis: | 2.340 | Cond. No. | 139. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.026 | 0.875 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.588 |
| Model: | OLS | Adj. R-squared: | 0.476 |
| Method: | Least Squares | F-statistic: | 5.241 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0173 |
| Time: | 22:46:37 | Log-Likelihood: | -68.643 |
| No. Observations: | 15 | AIC: | 145.3 |
| Df Residuals: | 11 | BIC: | 148.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -148.5209 | 188.609 | -0.787 | 0.448 | -563.646 266.605 |
| C(dose)[T.1] | 576.6194 | 275.374 | 2.094 | 0.060 | -29.475 1182.714 |
| expression | 33.7820 | 29.460 | 1.147 | 0.276 | -31.060 98.624 |
| expression:C(dose)[T.1] | -85.1337 | 44.267 | -1.923 | 0.081 | -182.565 12.298 |
| Omnibus: | 2.503 | Durbin-Watson: | 1.283 |
| Prob(Omnibus): | 0.286 | Jarque-Bera (JB): | 1.198 |
| Skew: | -0.690 | Prob(JB): | 0.549 |
| Kurtosis: | 3.112 | Cond. No. | 322. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.450 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.908 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0277 |
| Time: | 22:46:37 | Log-Likelihood: | -70.817 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 92.5124 | 155.990 | 0.593 | 0.564 | -247.360 432.385 |
| C(dose)[T.1] | 47.9135 | 17.621 | 2.719 | 0.019 | 9.520 86.307 |
| expression | -3.9240 | 24.336 | -0.161 | 0.875 | -56.948 49.100 |
| Omnibus: | 2.819 | Durbin-Watson: | 0.777 |
| Prob(Omnibus): | 0.244 | Jarque-Bera (JB): | 1.918 |
| Skew: | -0.858 | Prob(JB): | 0.383 |
| Kurtosis: | 2.648 | Cond. No. | 128. |
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: | 22:46:37 | 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: | 0.111 |
| Model: | OLS | Adj. R-squared: | 0.043 |
| Method: | Least Squares | F-statistic: | 1.624 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.225 |
| Time: | 22:46:37 | Log-Likelihood: | -74.417 |
| No. Observations: | 15 | AIC: | 152.8 |
| Df Residuals: | 13 | BIC: | 154.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 303.8509 | 165.191 | 1.839 | 0.089 | -53.022 660.723 |
| expression | -33.8022 | 26.521 | -1.275 | 0.225 | -91.098 23.494 |
| Omnibus: | 1.506 | Durbin-Watson: | 1.269 |
| Prob(Omnibus): | 0.471 | Jarque-Bera (JB): | 0.788 |
| Skew: | -0.557 | Prob(JB): | 0.674 |
| Kurtosis: | 2.860 | Cond. No. | 110. |