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 |
| 4.609 | 0.044 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.715 |
| Model: | OLS | Adj. R-squared: | 0.670 |
| Method: | Least Squares | F-statistic: | 15.87 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 2.07e-05 |
| Time: | 11:45:43 | Log-Likelihood: | -98.677 |
| No. Observations: | 23 | AIC: | 205.4 |
| Df Residuals: | 19 | BIC: | 209.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 204.4872 | 99.478 | 2.056 | 0.054 | -3.723 412.698 |
| C(dose)[T.1] | 37.4740 | 136.282 | 0.275 | 0.786 | -247.767 322.714 |
| expression | -19.1494 | 12.656 | -1.513 | 0.147 | -45.639 7.340 |
| expression:C(dose)[T.1] | 0.6122 | 18.015 | 0.034 | 0.973 | -37.094 38.318 |
| Omnibus: | 0.743 | Durbin-Watson: | 1.702 |
| Prob(Omnibus): | 0.690 | Jarque-Bera (JB): | 0.780 |
| Skew: | 0.340 | Prob(JB): | 0.677 |
| Kurtosis: | 2.407 | Cond. No. | 336. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.715 |
| Model: | OLS | Adj. R-squared: | 0.686 |
| Method: | Least Squares | F-statistic: | 25.06 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 3.56e-06 |
| Time: | 11:45:43 | Log-Likelihood: | -98.678 |
| No. Observations: | 23 | AIC: | 203.4 |
| Df Residuals: | 20 | BIC: | 206.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 202.1163 | 69.112 | 2.924 | 0.008 | 57.952 346.281 |
| C(dose)[T.1] | 42.0930 | 9.484 | 4.439 | 0.000 | 22.311 61.875 |
| expression | -18.8473 | 8.779 | -2.147 | 0.044 | -37.160 -0.535 |
| Omnibus: | 0.749 | Durbin-Watson: | 1.708 |
| Prob(Omnibus): | 0.688 | Jarque-Bera (JB): | 0.784 |
| Skew: | 0.342 | Prob(JB): | 0.676 |
| Kurtosis: | 2.410 | Cond. No. | 136. |
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: | 11:45:43 | 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: | 0.434 |
| Model: | OLS | Adj. R-squared: | 0.407 |
| Method: | Least Squares | F-statistic: | 16.09 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.000632 |
| Time: | 11:45:43 | Log-Likelihood: | -106.56 |
| No. Observations: | 23 | AIC: | 217.1 |
| Df Residuals: | 21 | BIC: | 219.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 384.9889 | 76.293 | 5.046 | 0.000 | 226.328 543.649 |
| expression | -40.3672 | 10.063 | -4.011 | 0.001 | -61.294 -19.440 |
| Omnibus: | 1.723 | Durbin-Watson: | 1.901 |
| Prob(Omnibus): | 0.423 | Jarque-Bera (JB): | 1.348 |
| Skew: | 0.406 | Prob(JB): | 0.510 |
| Kurtosis: | 2.136 | Cond. No. | 108. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.715 | 0.215 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.576 |
| Model: | OLS | Adj. R-squared: | 0.460 |
| Method: | Least Squares | F-statistic: | 4.974 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0202 |
| Time: | 11:45:43 | Log-Likelihood: | -68.871 |
| No. Observations: | 15 | AIC: | 145.7 |
| Df Residuals: | 11 | BIC: | 148.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 116.7389 | 131.840 | 0.885 | 0.395 | -173.439 406.917 |
| C(dose)[T.1] | 316.3922 | 222.049 | 1.425 | 0.182 | -172.334 805.118 |
| expression | -6.4574 | 17.210 | -0.375 | 0.715 | -44.336 31.421 |
| expression:C(dose)[T.1] | -36.2753 | 29.603 | -1.225 | 0.246 | -101.431 28.880 |
| Omnibus: | 0.845 | Durbin-Watson: | 0.857 |
| Prob(Omnibus): | 0.656 | Jarque-Bera (JB): | 0.735 |
| Skew: | -0.260 | Prob(JB): | 0.692 |
| Kurtosis: | 2.049 | Cond. No. | 293. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.518 |
| Model: | OLS | Adj. R-squared: | 0.437 |
| Method: | Least Squares | F-statistic: | 6.440 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0126 |
| Time: | 11:45:44 | Log-Likelihood: | -69.831 |
| No. Observations: | 15 | AIC: | 145.7 |
| Df Residuals: | 12 | BIC: | 147.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 210.3593 | 109.669 | 1.918 | 0.079 | -28.589 449.308 |
| C(dose)[T.1] | 44.8987 | 15.084 | 2.977 | 0.012 | 12.034 77.764 |
| expression | -18.7174 | 14.292 | -1.310 | 0.215 | -49.858 12.423 |
| Omnibus: | 1.999 | Durbin-Watson: | 1.272 |
| Prob(Omnibus): | 0.368 | Jarque-Bera (JB): | 1.521 |
| Skew: | -0.639 | Prob(JB): | 0.467 |
| Kurtosis: | 2.105 | Cond. No. | 115. |
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: | 11:45:44 | 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: | 0.162 |
| Model: | OLS | Adj. R-squared: | 0.097 |
| Method: | Least Squares | F-statistic: | 2.506 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.137 |
| Time: | 11:45:44 | Log-Likelihood: | -73.978 |
| No. Observations: | 15 | AIC: | 152.0 |
| Df Residuals: | 13 | BIC: | 153.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 303.8506 | 133.102 | 2.283 | 0.040 | 16.300 591.401 |
| expression | -27.9730 | 17.671 | -1.583 | 0.137 | -66.149 10.203 |
| Omnibus: | 0.038 | Durbin-Watson: | 1.988 |
| Prob(Omnibus): | 0.981 | Jarque-Bera (JB): | 0.196 |
| Skew: | -0.096 | Prob(JB): | 0.907 |
| Kurtosis: | 2.474 | Cond. No. | 110. |