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 |
| 2.128 | 0.160 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.691 |
| Model: | OLS | Adj. R-squared: | 0.643 |
| Method: | Least Squares | F-statistic: | 14.20 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 4.31e-05 |
| Time: | 22:00:26 | Log-Likelihood: | -99.581 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 19 | BIC: | 211.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 84.0916 | 59.561 | 1.412 | 0.174 | -40.571 208.754 |
| C(dose)[T.1] | 97.2319 | 76.760 | 1.267 | 0.221 | -63.429 257.893 |
| expression | -4.2702 | 8.470 | -0.504 | 0.620 | -21.999 13.458 |
| expression:C(dose)[T.1] | -8.7731 | 11.994 | -0.731 | 0.473 | -33.878 16.331 |
| Omnibus: | 2.588 | Durbin-Watson: | 2.473 |
| Prob(Omnibus): | 0.274 | Jarque-Bera (JB): | 1.976 |
| Skew: | 0.707 | Prob(JB): | 0.372 |
| Kurtosis: | 2.752 | Cond. No. | 156. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.683 |
| Model: | OLS | Adj. R-squared: | 0.651 |
| Method: | Least Squares | F-statistic: | 21.53 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 1.03e-05 |
| Time: | 22:00:26 | Log-Likelihood: | -99.900 |
| No. Observations: | 23 | AIC: | 205.8 |
| Df Residuals: | 20 | BIC: | 209.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 114.7083 | 41.876 | 2.739 | 0.013 | 27.355 202.061 |
| C(dose)[T.1] | 41.7381 | 11.522 | 3.623 | 0.002 | 17.704 65.772 |
| expression | -8.6453 | 5.927 | -1.459 | 0.160 | -21.009 3.718 |
| Omnibus: | 2.557 | Durbin-Watson: | 2.540 |
| Prob(Omnibus): | 0.278 | Jarque-Bera (JB): | 1.752 |
| Skew: | 0.675 | Prob(JB): | 0.416 |
| Kurtosis: | 2.928 | Cond. No. | 67.6 |
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, 28 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:00:26 | 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: | 0.475 |
| Model: | OLS | Adj. R-squared: | 0.450 |
| Method: | Least Squares | F-statistic: | 18.97 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.000277 |
| Time: | 22:00:26 | Log-Likelihood: | -105.70 |
| No. Observations: | 23 | AIC: | 215.4 |
| Df Residuals: | 21 | BIC: | 217.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 228.8640 | 34.636 | 6.608 | 0.000 | 156.834 300.894 |
| expression | -23.4641 | 5.387 | -4.356 | 0.000 | -34.666 -12.262 |
| Omnibus: | 3.315 | Durbin-Watson: | 3.307 |
| Prob(Omnibus): | 0.191 | Jarque-Bera (JB): | 2.353 |
| Skew: | 0.783 | Prob(JB): | 0.308 |
| Kurtosis: | 2.959 | Cond. No. | 43.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.892 | 0.364 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.490 |
| Model: | OLS | Adj. R-squared: | 0.350 |
| Method: | Least Squares | F-statistic: | 3.518 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0525 |
| Time: | 22:00:26 | Log-Likelihood: | -70.255 |
| No. Observations: | 15 | AIC: | 148.5 |
| Df Residuals: | 11 | BIC: | 151.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 159.7072 | 112.467 | 1.420 | 0.183 | -87.832 407.246 |
| C(dose)[T.1] | 10.7526 | 164.996 | 0.065 | 0.949 | -352.401 373.906 |
| expression | -12.2096 | 14.802 | -0.825 | 0.427 | -44.789 20.370 |
| expression:C(dose)[T.1] | 5.2218 | 21.510 | 0.243 | 0.813 | -42.122 52.566 |
| Omnibus: | 1.145 | Durbin-Watson: | 0.975 |
| Prob(Omnibus): | 0.564 | Jarque-Bera (JB): | 0.965 |
| Skew: | -0.437 | Prob(JB): | 0.617 |
| Kurtosis: | 2.117 | Cond. No. | 214. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.487 |
| Model: | OLS | Adj. R-squared: | 0.401 |
| Method: | Least Squares | F-statistic: | 5.694 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0182 |
| Time: | 22:00:26 | Log-Likelihood: | -70.295 |
| No. Observations: | 15 | AIC: | 146.6 |
| Df Residuals: | 12 | BIC: | 148.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 141.0188 | 78.710 | 1.792 | 0.098 | -30.475 312.512 |
| C(dose)[T.1] | 50.6203 | 15.260 | 3.317 | 0.006 | 17.371 83.869 |
| expression | -9.7369 | 10.310 | -0.944 | 0.364 | -32.201 12.727 |
| Omnibus: | 1.493 | Durbin-Watson: | 0.955 |
| Prob(Omnibus): | 0.474 | Jarque-Bera (JB): | 1.115 |
| Skew: | -0.451 | Prob(JB): | 0.573 |
| Kurtosis: | 2.016 | Cond. No. | 81.2 |
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, 28 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:00:26 | 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: | -0.059 |
| Method: | Least Squares | F-statistic: | 0.2170 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.649 |
| Time: | 22:00:26 | Log-Likelihood: | -75.176 |
| 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 | 142.2142 | 104.701 | 1.358 | 0.197 | -83.978 368.406 |
| expression | -6.3578 | 13.648 | -0.466 | 0.649 | -35.843 23.127 |
| Omnibus: | 1.910 | Durbin-Watson: | 1.701 |
| Prob(Omnibus): | 0.385 | Jarque-Bera (JB): | 1.008 |
| Skew: | 0.225 | Prob(JB): | 0.604 |
| Kurtosis: | 1.812 | Cond. No. | 81.0 |