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 |
| 0.016 | 0.902 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.597 |
| Method: | Least Squares | F-statistic: | 11.87 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000132 |
| Time: | 04:53:06 | Log-Likelihood: | -100.96 |
| No. Observations: | 23 | AIC: | 209.9 |
| Df Residuals: | 19 | BIC: | 214.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 37.6367 | 126.442 | 0.298 | 0.769 | -227.009 302.282 |
| C(dose)[T.1] | 135.5390 | 213.611 | 0.635 | 0.533 | -311.555 582.633 |
| expression | 2.0551 | 15.662 | 0.131 | 0.897 | -30.725 34.835 |
| expression:C(dose)[T.1] | -10.0867 | 26.241 | -0.384 | 0.705 | -65.009 44.836 |
| Omnibus: | 0.569 | Durbin-Watson: | 1.831 |
| Prob(Omnibus): | 0.753 | Jarque-Bera (JB): | 0.623 |
| Skew: | 0.112 | Prob(JB): | 0.732 |
| Kurtosis: | 2.225 | Cond. No. | 480. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.52 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.81e-05 |
| Time: | 04:53:06 | Log-Likelihood: | -101.05 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 66.6100 | 99.333 | 0.671 | 0.510 | -140.595 273.815 |
| C(dose)[T.1] | 53.5029 | 8.866 | 6.035 | 0.000 | 35.009 71.997 |
| expression | -1.5380 | 12.296 | -0.125 | 0.902 | -27.186 24.110 |
| Omnibus: | 0.332 | Durbin-Watson: | 1.904 |
| Prob(Omnibus): | 0.847 | Jarque-Bera (JB): | 0.494 |
| Skew: | 0.090 | Prob(JB): | 0.781 |
| Kurtosis: | 2.305 | Cond. No. | 187. |
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: | 04:53:06 | 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: | 0.011 |
| Model: | OLS | Adj. R-squared: | -0.036 |
| Method: | Least Squares | F-statistic: | 0.2300 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.637 |
| Time: | 04:53:06 | Log-Likelihood: | -112.98 |
| No. Observations: | 23 | AIC: | 230.0 |
| Df Residuals: | 21 | BIC: | 232.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 2.1717 | 161.868 | 0.013 | 0.989 | -334.451 338.794 |
| expression | 9.5555 | 19.926 | 0.480 | 0.637 | -31.884 50.995 |
| Omnibus: | 2.985 | Durbin-Watson: | 2.460 |
| Prob(Omnibus): | 0.225 | Jarque-Bera (JB): | 1.470 |
| Skew: | 0.267 | Prob(JB): | 0.479 |
| Kurtosis: | 1.882 | Cond. No. | 186. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.614 | 0.228 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.521 |
| Model: | OLS | Adj. R-squared: | 0.390 |
| Method: | Least Squares | F-statistic: | 3.985 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0380 |
| Time: | 04:53:06 | Log-Likelihood: | -69.783 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 11 | BIC: | 150.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 268.9176 | 157.106 | 1.712 | 0.115 | -76.870 614.706 |
| C(dose)[T.1] | -157.2624 | 507.102 | -0.310 | 0.762 | -1273.387 958.862 |
| expression | -20.6565 | 16.065 | -1.286 | 0.225 | -56.016 14.703 |
| expression:C(dose)[T.1] | 21.1888 | 54.074 | 0.392 | 0.703 | -97.826 140.204 |
| Omnibus: | 2.442 | Durbin-Watson: | 1.041 |
| Prob(Omnibus): | 0.295 | Jarque-Bera (JB): | 1.458 |
| Skew: | -0.759 | Prob(JB): | 0.482 |
| Kurtosis: | 2.832 | Cond. No. | 740. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.514 |
| Model: | OLS | Adj. R-squared: | 0.433 |
| Method: | Least Squares | F-statistic: | 6.348 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0132 |
| Time: | 04:53:06 | Log-Likelihood: | -69.887 |
| No. Observations: | 15 | AIC: | 145.8 |
| Df Residuals: | 12 | BIC: | 147.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 250.6738 | 144.660 | 1.733 | 0.109 | -64.513 565.861 |
| C(dose)[T.1] | 41.3391 | 16.020 | 2.581 | 0.024 | 6.435 76.243 |
| expression | -18.7861 | 14.789 | -1.270 | 0.228 | -51.009 13.437 |
| Omnibus: | 2.957 | Durbin-Watson: | 0.925 |
| Prob(Omnibus): | 0.228 | Jarque-Bera (JB): | 1.792 |
| Skew: | -0.844 | Prob(JB): | 0.408 |
| Kurtosis: | 2.865 | Cond. No. | 190. |
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: | 04:53:06 | 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: | 0.244 |
| Model: | OLS | Adj. R-squared: | 0.186 |
| Method: | Least Squares | F-statistic: | 4.207 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0610 |
| Time: | 04:53:06 | Log-Likelihood: | -73.197 |
| No. Observations: | 15 | AIC: | 150.4 |
| Df Residuals: | 13 | BIC: | 151.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 413.1690 | 156.028 | 2.648 | 0.020 | 76.092 750.246 |
| expression | -33.5216 | 16.344 | -2.051 | 0.061 | -68.831 1.787 |
| Omnibus: | 4.923 | Durbin-Watson: | 1.882 |
| Prob(Omnibus): | 0.085 | Jarque-Bera (JB): | 1.489 |
| Skew: | 0.232 | Prob(JB): | 0.475 |
| Kurtosis: | 1.528 | Cond. No. | 170. |