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 |
| 1.936 | 0.179 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.709 |
| Model: | OLS | Adj. R-squared: | 0.664 |
| Method: | Least Squares | F-statistic: | 15.46 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 2.47e-05 |
| Time: | 11:46:25 | Log-Likelihood: | -98.891 |
| No. Observations: | 23 | AIC: | 205.8 |
| Df Residuals: | 19 | BIC: | 210.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 147.3610 | 220.313 | 0.669 | 0.512 | -313.760 608.482 |
| C(dose)[T.1] | -316.2292 | 262.366 | -1.205 | 0.243 | -865.368 232.909 |
| expression | -12.9200 | 30.547 | -0.423 | 0.677 | -76.855 51.015 |
| expression:C(dose)[T.1] | 49.9473 | 36.010 | 1.387 | 0.181 | -25.422 125.316 |
| Omnibus: | 1.122 | Durbin-Watson: | 2.171 |
| Prob(Omnibus): | 0.571 | Jarque-Bera (JB): | 0.469 |
| Skew: | 0.347 | Prob(JB): | 0.791 |
| Kurtosis: | 3.090 | Cond. No. | 687. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.680 |
| Model: | OLS | Adj. R-squared: | 0.648 |
| Method: | Least Squares | F-statistic: | 21.25 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 1.12e-05 |
| Time: | 11:46:25 | Log-Likelihood: | -100.00 |
| No. Observations: | 23 | AIC: | 206.0 |
| Df Residuals: | 20 | BIC: | 209.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -111.7773 | 119.429 | -0.936 | 0.360 | -360.903 137.348 |
| C(dose)[T.1] | 47.4635 | 9.378 | 5.061 | 0.000 | 27.902 67.025 |
| expression | 23.0216 | 16.545 | 1.391 | 0.179 | -11.491 57.534 |
| Omnibus: | 0.278 | Durbin-Watson: | 2.007 |
| Prob(Omnibus): | 0.870 | Jarque-Bera (JB): | 0.457 |
| Skew: | -0.151 | Prob(JB): | 0.796 |
| Kurtosis: | 2.380 | Cond. No. | 214. |
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: | 11:46:25 | 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: | 0.270 |
| Model: | OLS | Adj. R-squared: | 0.235 |
| Method: | Least Squares | F-statistic: | 7.775 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0110 |
| Time: | 11:46:25 | Log-Likelihood: | -109.48 |
| No. Observations: | 23 | AIC: | 223.0 |
| Df Residuals: | 21 | BIC: | 225.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -365.4508 | 159.771 | -2.287 | 0.033 | -697.713 -33.189 |
| expression | 60.7157 | 21.775 | 2.788 | 0.011 | 15.433 105.999 |
| Omnibus: | 0.095 | Durbin-Watson: | 2.678 |
| Prob(Omnibus): | 0.953 | Jarque-Bera (JB): | 0.075 |
| Skew: | -0.075 | Prob(JB): | 0.963 |
| Kurtosis: | 2.763 | Cond. No. | 194. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.076 | 0.788 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.303 |
| Method: | Least Squares | F-statistic: | 3.027 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0753 |
| Time: | 11:46:25 | Log-Likelihood: | -70.786 |
| No. Observations: | 15 | AIC: | 149.6 |
| Df Residuals: | 11 | BIC: | 152.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 15.4927 | 448.534 | 0.035 | 0.973 | -971.723 1002.709 |
| C(dose)[T.1] | 53.9594 | 490.833 | 0.110 | 0.914 | -1026.357 1134.276 |
| expression | 7.0517 | 60.879 | 0.116 | 0.910 | -126.941 141.044 |
| expression:C(dose)[T.1] | -0.1969 | 67.399 | -0.003 | 0.998 | -148.540 148.146 |
| Omnibus: | 2.119 | Durbin-Watson: | 0.862 |
| Prob(Omnibus): | 0.347 | Jarque-Bera (JB): | 1.594 |
| Skew: | -0.743 | Prob(JB): | 0.451 |
| Kurtosis: | 2.416 | Cond. No. | 661. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.361 |
| Method: | Least Squares | F-statistic: | 4.954 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0270 |
| Time: | 11:46:25 | Log-Likelihood: | -70.786 |
| 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 | 16.6757 | 184.559 | 0.090 | 0.929 | -385.443 418.794 |
| C(dose)[T.1] | 52.5269 | 19.806 | 2.652 | 0.021 | 9.373 95.681 |
| expression | 6.8910 | 25.010 | 0.276 | 0.788 | -47.602 61.384 |
| Omnibus: | 2.120 | Durbin-Watson: | 0.862 |
| Prob(Omnibus): | 0.346 | Jarque-Bera (JB): | 1.594 |
| Skew: | -0.743 | Prob(JB): | 0.451 |
| Kurtosis: | 2.416 | Cond. No. | 172. |
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: | 11:46:25 | 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: | 0.131 |
| Model: | OLS | Adj. R-squared: | 0.064 |
| Method: | Least Squares | F-statistic: | 1.963 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.185 |
| Time: | 11:46:25 | Log-Likelihood: | -74.245 |
| No. Observations: | 15 | AIC: | 152.5 |
| Df Residuals: | 13 | BIC: | 153.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 332.3907 | 170.647 | 1.948 | 0.073 | -36.270 701.051 |
| expression | -33.5886 | 23.973 | -1.401 | 0.185 | -85.379 18.202 |
| Omnibus: | 0.308 | Durbin-Watson: | 1.208 |
| Prob(Omnibus): | 0.857 | Jarque-Bera (JB): | 0.350 |
| Skew: | -0.274 | Prob(JB): | 0.839 |
| Kurtosis: | 2.490 | Cond. No. | 131. |