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 |
| 0.571 | 0.459 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.665 |
| Model: | OLS | Adj. R-squared: | 0.612 |
| Method: | Least Squares | F-statistic: | 12.57 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.27e-05 |
| Time: | 05:01:10 | Log-Likelihood: | -100.53 |
| No. Observations: | 23 | AIC: | 209.1 |
| Df Residuals: | 19 | BIC: | 213.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -56.2518 | 116.639 | -0.482 | 0.635 | -300.381 187.877 |
| C(dose)[T.1] | 154.7457 | 169.888 | 0.911 | 0.374 | -200.834 510.325 |
| expression | 16.6228 | 17.529 | 0.948 | 0.355 | -20.065 53.311 |
| expression:C(dose)[T.1] | -15.2459 | 25.678 | -0.594 | 0.560 | -68.990 38.498 |
| Omnibus: | 0.373 | Durbin-Watson: | 2.277 |
| Prob(Omnibus): | 0.830 | Jarque-Bera (JB): | 0.511 |
| Skew: | 0.004 | Prob(JB): | 0.774 |
| Kurtosis: | 2.270 | Cond. No. | 335. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.625 |
| Method: | Least Squares | F-statistic: | 19.31 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.14e-05 |
| Time: | 05:01:10 | Log-Likelihood: | -100.74 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -9.0402 | 83.942 | -0.108 | 0.915 | -184.141 166.060 |
| C(dose)[T.1] | 54.0127 | 8.693 | 6.213 | 0.000 | 35.878 72.147 |
| expression | 9.5181 | 12.600 | 0.755 | 0.459 | -16.765 35.801 |
| Omnibus: | 0.136 | Durbin-Watson: | 2.048 |
| Prob(Omnibus): | 0.934 | Jarque-Bera (JB): | 0.341 |
| Skew: | -0.106 | Prob(JB): | 0.843 |
| Kurtosis: | 2.442 | Cond. No. | 132. |
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: | 05:01:10 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.004896 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.945 |
| Time: | 05:01:10 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 70.0321 | 138.604 | 0.505 | 0.619 | -218.211 358.275 |
| expression | 1.4650 | 20.937 | 0.070 | 0.945 | -42.075 45.005 |
| Omnibus: | 3.529 | Durbin-Watson: | 2.500 |
| Prob(Omnibus): | 0.171 | Jarque-Bera (JB): | 1.616 |
| Skew: | 0.291 | Prob(JB): | 0.446 |
| Kurtosis: | 1.839 | Cond. No. | 130. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.647 | 0.437 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.482 |
| Model: | OLS | Adj. R-squared: | 0.341 |
| Method: | Least Squares | F-statistic: | 3.411 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0567 |
| Time: | 05:01:10 | Log-Likelihood: | -70.368 |
| No. Observations: | 15 | AIC: | 148.7 |
| Df Residuals: | 11 | BIC: | 151.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 135.4388 | 220.034 | 0.616 | 0.551 | -348.852 619.729 |
| C(dose)[T.1] | 152.1381 | 310.757 | 0.490 | 0.634 | -531.834 836.111 |
| expression | -10.3535 | 33.450 | -0.310 | 0.763 | -83.976 63.269 |
| expression:C(dose)[T.1] | -15.1927 | 46.814 | -0.325 | 0.752 | -118.231 87.845 |
| Omnibus: | 1.774 | Durbin-Watson: | 0.816 |
| Prob(Omnibus): | 0.412 | Jarque-Bera (JB): | 1.340 |
| Skew: | -0.676 | Prob(JB): | 0.512 |
| Kurtosis: | 2.440 | Cond. No. | 354. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.477 |
| Model: | OLS | Adj. R-squared: | 0.390 |
| Method: | Least Squares | F-statistic: | 5.472 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0205 |
| Time: | 05:01:10 | Log-Likelihood: | -70.439 |
| No. Observations: | 15 | AIC: | 146.9 |
| Df Residuals: | 12 | BIC: | 149.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 186.3895 | 148.305 | 1.257 | 0.233 | -136.740 509.519 |
| C(dose)[T.1] | 51.4248 | 15.580 | 3.301 | 0.006 | 17.479 85.371 |
| expression | -18.1099 | 22.513 | -0.804 | 0.437 | -67.161 30.941 |
| Omnibus: | 1.907 | Durbin-Watson: | 0.819 |
| Prob(Omnibus): | 0.385 | Jarque-Bera (JB): | 1.447 |
| Skew: | -0.702 | Prob(JB): | 0.485 |
| Kurtosis: | 2.413 | Cond. No. | 132. |
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: | 05:01:10 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.075 |
| Method: | Least Squares | F-statistic: | 0.02775 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.870 |
| Time: | 05:01:10 | Log-Likelihood: | -75.284 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 126.1617 | 195.317 | 0.646 | 0.530 | -295.795 548.119 |
| expression | -4.8979 | 29.400 | -0.167 | 0.870 | -68.413 58.617 |
| Omnibus: | 0.700 | Durbin-Watson: | 1.696 |
| Prob(Omnibus): | 0.705 | Jarque-Bera (JB): | 0.616 |
| Skew: | 0.054 | Prob(JB): | 0.735 |
| Kurtosis: | 2.013 | Cond. No. | 131. |