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 |
| 7.028 | 0.015 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.746 |
| Model: | OLS | Adj. R-squared: | 0.705 |
| Method: | Least Squares | F-statistic: | 18.57 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 7.13e-06 |
| Time: | 11:44:21 | Log-Likelihood: | -97.361 |
| No. Observations: | 23 | AIC: | 202.7 |
| Df Residuals: | 19 | BIC: | 207.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 152.5342 | 55.882 | 2.730 | 0.013 | 35.572 269.497 |
| C(dose)[T.1] | 103.6126 | 92.475 | 1.120 | 0.276 | -89.940 297.165 |
| expression | -13.7877 | 7.801 | -1.767 | 0.093 | -30.115 2.539 |
| expression:C(dose)[T.1] | -8.5285 | 13.513 | -0.631 | 0.535 | -36.811 19.754 |
| Omnibus: | 0.523 | Durbin-Watson: | 2.070 |
| Prob(Omnibus): | 0.770 | Jarque-Bera (JB): | 0.016 |
| Skew: | -0.019 | Prob(JB): | 0.992 |
| Kurtosis: | 3.124 | Cond. No. | 206. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.740 |
| Model: | OLS | Adj. R-squared: | 0.714 |
| Method: | Least Squares | F-statistic: | 28.51 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 1.40e-06 |
| Time: | 11:44:21 | Log-Likelihood: | -97.600 |
| No. Observations: | 23 | AIC: | 201.2 |
| Df Residuals: | 20 | BIC: | 204.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 172.8032 | 45.039 | 3.837 | 0.001 | 78.853 266.753 |
| C(dose)[T.1] | 45.4793 | 8.105 | 5.611 | 0.000 | 28.572 62.387 |
| expression | -16.6299 | 6.273 | -2.651 | 0.015 | -29.715 -3.544 |
| Omnibus: | 0.218 | Durbin-Watson: | 2.244 |
| Prob(Omnibus): | 0.897 | Jarque-Bera (JB): | 0.078 |
| Skew: | -0.120 | Prob(JB): | 0.962 |
| Kurtosis: | 2.846 | Cond. No. | 85.0 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 11:44:21 | 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: | 0.332 |
| Model: | OLS | Adj. R-squared: | 0.300 |
| Method: | Least Squares | F-statistic: | 10.41 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00404 |
| Time: | 11:44:21 | Log-Likelihood: | -108.47 |
| No. Observations: | 23 | AIC: | 220.9 |
| Df Residuals: | 21 | BIC: | 223.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 283.4392 | 63.403 | 4.470 | 0.000 | 151.586 415.293 |
| expression | -29.5016 | 9.142 | -3.227 | 0.004 | -48.513 -10.490 |
| Omnibus: | 0.375 | Durbin-Watson: | 2.499 |
| Prob(Omnibus): | 0.829 | Jarque-Bera (JB): | 0.526 |
| Skew: | 0.170 | Prob(JB): | 0.769 |
| Kurtosis: | 2.342 | Cond. No. | 76.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.054 | 0.819 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.768 |
| Model: | OLS | Adj. R-squared: | 0.705 |
| Method: | Least Squares | F-statistic: | 12.16 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.000812 |
| Time: | 11:44:21 | Log-Likelihood: | -64.331 |
| No. Observations: | 15 | AIC: | 136.7 |
| Df Residuals: | 11 | BIC: | 139.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -140.2335 | 83.436 | -1.681 | 0.121 | -323.875 43.408 |
| C(dose)[T.1] | 615.9040 | 146.471 | 4.205 | 0.001 | 293.523 938.285 |
| expression | 30.8149 | 12.327 | 2.500 | 0.030 | 3.683 57.947 |
| expression:C(dose)[T.1] | -83.7065 | 21.571 | -3.881 | 0.003 | -131.184 -36.230 |
| Omnibus: | 1.105 | Durbin-Watson: | 1.297 |
| Prob(Omnibus): | 0.576 | Jarque-Bera (JB): | 0.881 |
| Skew: | -0.528 | Prob(JB): | 0.644 |
| Kurtosis: | 2.455 | Cond. No. | 236. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 4.934 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0273 |
| Time: | 11:44:21 | Log-Likelihood: | -70.799 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 43.9890 | 101.111 | 0.435 | 0.671 | -176.312 264.290 |
| C(dose)[T.1] | 49.0249 | 15.721 | 3.118 | 0.009 | 14.771 83.279 |
| expression | 3.4782 | 14.907 | 0.233 | 0.819 | -29.001 35.958 |
| Omnibus: | 2.815 | Durbin-Watson: | 0.824 |
| Prob(Omnibus): | 0.245 | Jarque-Bera (JB): | 1.849 |
| Skew: | -0.848 | Prob(JB): | 0.397 |
| Kurtosis: | 2.715 | Cond. No. | 89.7 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 11:44:21 | 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: | 0.007 |
| Model: | OLS | Adj. R-squared: | -0.070 |
| Method: | Least Squares | F-statistic: | 0.08620 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.774 |
| Time: | 11:44:21 | Log-Likelihood: | -75.250 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 55.4312 | 130.621 | 0.424 | 0.678 | -226.758 337.620 |
| expression | 5.6517 | 19.249 | 0.294 | 0.774 | -35.934 47.237 |
| Omnibus: | 1.741 | Durbin-Watson: | 1.639 |
| Prob(Omnibus): | 0.419 | Jarque-Bera (JB): | 0.964 |
| Skew: | 0.215 | Prob(JB): | 0.618 |
| Kurtosis: | 1.835 | Cond. No. | 89.4 |