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 |
| 1.224 | 0.282 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.669 |
| Model: | OLS | Adj. R-squared: | 0.617 |
| Method: | Least Squares | F-statistic: | 12.82 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.20e-05 |
| Time: | 04:14:13 | Log-Likelihood: | -100.38 |
| No. Observations: | 23 | AIC: | 208.8 |
| Df Residuals: | 19 | BIC: | 213.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -578.4325 | 598.756 | -0.966 | 0.346 | -1831.644 674.779 |
| C(dose)[T.1] | 216.2602 | 2133.274 | 0.101 | 0.920 | -4248.733 4681.254 |
| expression | 51.9712 | 49.185 | 1.057 | 0.304 | -50.974 154.917 |
| expression:C(dose)[T.1] | -13.8128 | 173.453 | -0.080 | 0.937 | -376.855 349.229 |
| Omnibus: | 0.061 | Durbin-Watson: | 2.007 |
| Prob(Omnibus): | 0.970 | Jarque-Bera (JB): | 0.142 |
| Skew: | 0.095 | Prob(JB): | 0.931 |
| Kurtosis: | 2.665 | Cond. No. | 6.87e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.669 |
| Model: | OLS | Adj. R-squared: | 0.636 |
| Method: | Least Squares | F-statistic: | 20.24 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.56e-05 |
| Time: | 04:14:13 | Log-Likelihood: | -100.38 |
| No. Observations: | 23 | AIC: | 206.8 |
| Df Residuals: | 20 | BIC: | 210.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -564.9124 | 559.737 | -1.009 | 0.325 | -1732.503 602.678 |
| C(dose)[T.1] | 46.3812 | 10.584 | 4.382 | 0.000 | 24.304 68.459 |
| expression | 50.8605 | 45.980 | 1.106 | 0.282 | -45.051 146.772 |
| Omnibus: | 0.073 | Durbin-Watson: | 2.016 |
| Prob(Omnibus): | 0.964 | Jarque-Bera (JB): | 0.106 |
| Skew: | 0.085 | Prob(JB): | 0.948 |
| Kurtosis: | 2.714 | Cond. No. | 1.62e+03 |
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, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 04:14:13 | 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: | 0.352 |
| Model: | OLS | Adj. R-squared: | 0.321 |
| Method: | Least Squares | F-statistic: | 11.39 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00286 |
| Time: | 04:14:14 | Log-Likelihood: | -108.12 |
| No. Observations: | 23 | AIC: | 220.2 |
| Df Residuals: | 21 | BIC: | 222.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -2007.8473 | 618.461 | -3.247 | 0.004 | -3294.008 -721.687 |
| expression | 170.5761 | 50.533 | 3.376 | 0.003 | 65.488 275.664 |
| Omnibus: | 0.270 | Durbin-Watson: | 2.236 |
| Prob(Omnibus): | 0.874 | Jarque-Bera (JB): | 0.116 |
| Skew: | -0.155 | Prob(JB): | 0.944 |
| Kurtosis: | 2.844 | Cond. No. | 1.31e+03 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 4.846 | 0.048 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.613 |
| Model: | OLS | Adj. R-squared: | 0.508 |
| Method: | Least Squares | F-statistic: | 5.814 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0124 |
| Time: | 04:14:14 | Log-Likelihood: | -68.175 |
| No. Observations: | 15 | AIC: | 144.4 |
| Df Residuals: | 11 | BIC: | 147.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -1085.3006 | 2312.837 | -0.469 | 0.648 | -6175.820 4005.218 |
| C(dose)[T.1] | -978.4102 | 2534.255 | -0.386 | 0.707 | -6556.268 4599.447 |
| expression | 96.5158 | 193.647 | 0.498 | 0.628 | -329.699 522.731 |
| expression:C(dose)[T.1] | 87.0876 | 212.389 | 0.410 | 0.690 | -380.378 554.553 |
| Omnibus: | 1.779 | Durbin-Watson: | 0.991 |
| Prob(Omnibus): | 0.411 | Jarque-Bera (JB): | 1.388 |
| Skew: | -0.606 | Prob(JB): | 0.499 |
| Kurtosis: | 2.134 | Cond. No. | 6.79e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.607 |
| Model: | OLS | Adj. R-squared: | 0.542 |
| Method: | Least Squares | F-statistic: | 9.281 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00367 |
| Time: | 04:14:14 | Log-Likelihood: | -68.289 |
| No. Observations: | 15 | AIC: | 142.6 |
| Df Residuals: | 12 | BIC: | 144.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -1949.9586 | 916.472 | -2.128 | 0.055 | -3946.780 46.863 |
| C(dose)[T.1] | 60.7127 | 14.277 | 4.252 | 0.001 | 29.605 91.820 |
| expression | 168.9119 | 76.730 | 2.201 | 0.048 | 1.731 336.093 |
| Omnibus: | 2.231 | Durbin-Watson: | 1.043 |
| Prob(Omnibus): | 0.328 | Jarque-Bera (JB): | 1.308 |
| Skew: | -0.439 | Prob(JB): | 0.520 |
| Kurtosis: | 1.850 | Cond. No. | 1.66e+03 |
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, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 04:14:14 | 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: | 0.016 |
| Model: | OLS | Adj. R-squared: | -0.060 |
| Method: | Least Squares | F-statistic: | 0.2065 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.657 |
| Time: | 04:14:14 | Log-Likelihood: | -75.182 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -493.9986 | 1293.205 | -0.382 | 0.709 | -3287.797 2299.800 |
| expression | 49.3543 | 108.605 | 0.454 | 0.657 | -185.272 283.981 |
| Omnibus: | 0.651 | Durbin-Watson: | 1.756 |
| Prob(Omnibus): | 0.722 | Jarque-Bera (JB): | 0.603 |
| Skew: | 0.083 | Prob(JB): | 0.740 |
| Kurtosis: | 2.032 | Cond. No. | 1.54e+03 |