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 |
| 1.832 | 0.191 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.679 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 13.40 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 6.23e-05 |
| Time: | 22:54:32 | Log-Likelihood: | -100.03 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 19 | BIC: | 212.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -26.4289 | 82.377 | -0.321 | 0.752 | -198.847 145.989 |
| C(dose)[T.1] | 22.2673 | 148.911 | 0.150 | 0.883 | -289.406 333.941 |
| expression | 11.2330 | 11.445 | 0.981 | 0.339 | -12.723 35.188 |
| expression:C(dose)[T.1] | 3.6337 | 20.072 | 0.181 | 0.858 | -38.377 45.644 |
| Omnibus: | 0.441 | Durbin-Watson: | 2.068 |
| Prob(Omnibus): | 0.802 | Jarque-Bera (JB): | 0.365 |
| Skew: | -0.273 | Prob(JB): | 0.833 |
| Kurtosis: | 2.712 | Cond. No. | 314. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.679 |
| Model: | OLS | Adj. R-squared: | 0.646 |
| Method: | Least Squares | F-statistic: | 21.10 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.18e-05 |
| Time: | 22:54:32 | Log-Likelihood: | -100.05 |
| 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 | -34.9106 | 66.098 | -0.528 | 0.603 | -172.789 102.968 |
| C(dose)[T.1] | 49.1743 | 8.940 | 5.501 | 0.000 | 30.527 67.822 |
| expression | 12.4145 | 9.172 | 1.354 | 0.191 | -6.718 31.547 |
| Omnibus: | 0.405 | Durbin-Watson: | 2.090 |
| Prob(Omnibus): | 0.817 | Jarque-Bera (JB): | 0.387 |
| Skew: | -0.268 | Prob(JB): | 0.824 |
| Kurtosis: | 2.658 | Cond. No. | 118. |
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: | 22:54:32 | 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: | 0.192 |
| Model: | OLS | Adj. R-squared: | 0.154 |
| Method: | Least Squares | F-statistic: | 4.994 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0364 |
| Time: | 22:54:33 | Log-Likelihood: | -110.65 |
| No. Observations: | 23 | AIC: | 225.3 |
| Df Residuals: | 21 | BIC: | 227.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -138.7836 | 97.993 | -1.416 | 0.171 | -342.571 65.004 |
| expression | 29.7727 | 13.323 | 2.235 | 0.036 | 2.066 57.480 |
| Omnibus: | 0.669 | Durbin-Watson: | 2.451 |
| Prob(Omnibus): | 0.716 | Jarque-Bera (JB): | 0.654 |
| Skew: | 0.043 | Prob(JB): | 0.721 |
| Kurtosis: | 2.179 | Cond. No. | 113. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.608 | 0.229 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.524 |
| Model: | OLS | Adj. R-squared: | 0.394 |
| Method: | Least Squares | F-statistic: | 4.038 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0367 |
| Time: | 22:54:33 | Log-Likelihood: | -69.731 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 11 | BIC: | 150.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 114.2735 | 133.791 | 0.854 | 0.411 | -180.199 408.746 |
| C(dose)[T.1] | 136.9431 | 170.916 | 0.801 | 0.440 | -239.240 513.126 |
| expression | -9.2238 | 26.252 | -0.351 | 0.732 | -67.004 48.556 |
| expression:C(dose)[T.1] | -15.9249 | 32.868 | -0.485 | 0.638 | -88.267 56.417 |
| Omnibus: | 1.234 | Durbin-Watson: | 0.564 |
| Prob(Omnibus): | 0.539 | Jarque-Bera (JB): | 0.831 |
| Skew: | -0.546 | Prob(JB): | 0.660 |
| Kurtosis: | 2.630 | Cond. No. | 173. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.514 |
| Model: | OLS | Adj. R-squared: | 0.433 |
| Method: | Least Squares | F-statistic: | 6.344 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0132 |
| Time: | 22:54:33 | Log-Likelihood: | -69.890 |
| No. Observations: | 15 | AIC: | 145.8 |
| Df Residuals: | 12 | BIC: | 147.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 165.8680 | 78.372 | 2.116 | 0.056 | -4.889 336.625 |
| C(dose)[T.1] | 54.4904 | 15.359 | 3.548 | 0.004 | 21.027 87.954 |
| expression | -19.3828 | 15.284 | -1.268 | 0.229 | -52.685 13.919 |
| Omnibus: | 0.986 | Durbin-Watson: | 0.542 |
| Prob(Omnibus): | 0.611 | Jarque-Bera (JB): | 0.752 |
| Skew: | -0.495 | Prob(JB): | 0.687 |
| Kurtosis: | 2.529 | Cond. No. | 58.2 |
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: | 22:54:33 | 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: | 0.05269 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.822 |
| Time: | 22:54:33 | Log-Likelihood: | -75.270 |
| 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 | 117.9256 | 106.167 | 1.111 | 0.287 | -111.434 347.286 |
| expression | -4.6434 | 20.229 | -0.230 | 0.822 | -48.345 39.058 |
| Omnibus: | 1.025 | Durbin-Watson: | 1.669 |
| Prob(Omnibus): | 0.599 | Jarque-Bera (JB): | 0.717 |
| Skew: | 0.049 | Prob(JB): | 0.699 |
| Kurtosis: | 1.933 | Cond. No. | 56.9 |