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 |
| 1.348 | 0.259 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.687 |
| Model: | OLS | Adj. R-squared: | 0.637 |
| Method: | Least Squares | F-statistic: | 13.89 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.97e-05 |
| Time: | 03:47:52 | Log-Likelihood: | -99.756 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 19 | BIC: | 212.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -40.8095 | 66.177 | -0.617 | 0.545 | -179.319 97.700 |
| C(dose)[T.1] | 125.3409 | 83.400 | 1.503 | 0.149 | -49.218 299.900 |
| expression | 17.9320 | 12.440 | 1.442 | 0.166 | -8.105 43.968 |
| expression:C(dose)[T.1] | -14.3157 | 14.745 | -0.971 | 0.344 | -45.178 16.546 |
| Omnibus: | 0.574 | Durbin-Watson: | 1.612 |
| Prob(Omnibus): | 0.750 | Jarque-Bera (JB): | 0.603 |
| Skew: | -0.320 | Prob(JB): | 0.740 |
| Kurtosis: | 2.532 | Cond. No. | 170. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.671 |
| Model: | OLS | Adj. R-squared: | 0.638 |
| Method: | Least Squares | F-statistic: | 20.41 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.48e-05 |
| Time: | 03:47:52 | Log-Likelihood: | -100.31 |
| No. Observations: | 23 | AIC: | 206.6 |
| Df Residuals: | 20 | BIC: | 210.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 13.1796 | 35.824 | 0.368 | 0.717 | -61.548 87.907 |
| C(dose)[T.1] | 45.0881 | 11.070 | 4.073 | 0.001 | 21.997 68.179 |
| expression | 7.7430 | 6.669 | 1.161 | 0.259 | -6.169 21.655 |
| Omnibus: | 0.658 | Durbin-Watson: | 1.709 |
| Prob(Omnibus): | 0.720 | Jarque-Bera (JB): | 0.446 |
| Skew: | -0.324 | Prob(JB): | 0.800 |
| Kurtosis: | 2.788 | Cond. No. | 52.1 |
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: | 03:47:52 | 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: | 0.398 |
| Model: | OLS | Adj. R-squared: | 0.370 |
| Method: | Least Squares | F-statistic: | 13.91 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00124 |
| Time: | 03:47:52 | Log-Likelihood: | -107.26 |
| No. Observations: | 23 | AIC: | 218.5 |
| Df Residuals: | 21 | BIC: | 220.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -66.5313 | 39.608 | -1.680 | 0.108 | -148.901 15.838 |
| expression | 25.1792 | 6.751 | 3.730 | 0.001 | 11.140 39.218 |
| Omnibus: | 0.310 | Durbin-Watson: | 1.602 |
| Prob(Omnibus): | 0.856 | Jarque-Bera (JB): | 0.451 |
| Skew: | 0.217 | Prob(JB): | 0.798 |
| Kurtosis: | 2.470 | Cond. No. | 42.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.151 | 0.101 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.636 |
| Model: | OLS | Adj. R-squared: | 0.537 |
| Method: | Least Squares | F-statistic: | 6.409 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00903 |
| Time: | 03:47:52 | Log-Likelihood: | -67.718 |
| No. Observations: | 15 | AIC: | 143.4 |
| Df Residuals: | 11 | BIC: | 146.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 72.0067 | 102.200 | 0.705 | 0.496 | -152.933 296.946 |
| C(dose)[T.1] | -141.0284 | 128.905 | -1.094 | 0.297 | -424.746 142.689 |
| expression | -0.7568 | 16.817 | -0.045 | 0.965 | -37.770 36.257 |
| expression:C(dose)[T.1] | 31.4026 | 21.183 | 1.482 | 0.166 | -15.220 78.026 |
| Omnibus: | 0.416 | Durbin-Watson: | 1.204 |
| Prob(Omnibus): | 0.812 | Jarque-Bera (JB): | 0.317 |
| Skew: | -0.301 | Prob(JB): | 0.853 |
| Kurtosis: | 2.619 | Cond. No. | 170. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.563 |
| Model: | OLS | Adj. R-squared: | 0.491 |
| Method: | Least Squares | F-statistic: | 7.743 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00693 |
| Time: | 03:47:52 | Log-Likelihood: | -69.085 |
| No. Observations: | 15 | AIC: | 144.2 |
| Df Residuals: | 12 | BIC: | 146.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -47.7226 | 65.676 | -0.727 | 0.481 | -190.818 95.373 |
| C(dose)[T.1] | 49.0387 | 14.008 | 3.501 | 0.004 | 18.518 79.560 |
| expression | 19.0348 | 10.724 | 1.775 | 0.101 | -4.331 42.400 |
| Omnibus: | 0.982 | Durbin-Watson: | 1.009 |
| Prob(Omnibus): | 0.612 | Jarque-Bera (JB): | 0.842 |
| Skew: | -0.354 | Prob(JB): | 0.656 |
| Kurtosis: | 2.080 | Cond. No. | 59.0 |
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: | 03:47:52 | 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: | 0.118 |
| Model: | OLS | Adj. R-squared: | 0.050 |
| Method: | Least Squares | F-statistic: | 1.731 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.211 |
| Time: | 03:47:52 | Log-Likelihood: | -74.362 |
| No. Observations: | 15 | AIC: | 152.7 |
| Df Residuals: | 13 | BIC: | 154.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -23.0099 | 89.190 | -0.258 | 0.800 | -215.692 169.672 |
| expression | 19.2728 | 14.648 | 1.316 | 0.211 | -12.372 50.918 |
| Omnibus: | 0.100 | Durbin-Watson: | 1.529 |
| Prob(Omnibus): | 0.951 | Jarque-Bera (JB): | 0.292 |
| Skew: | -0.137 | Prob(JB): | 0.864 |
| Kurtosis: | 2.374 | Cond. No. | 58.4 |