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 |
| 3.122 | 0.093 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.700 |
| Model: | OLS | Adj. R-squared: | 0.652 |
| Method: | Least Squares | F-statistic: | 14.76 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.35e-05 |
| Time: | 05:12:22 | Log-Likelihood: | -99.268 |
| No. Observations: | 23 | AIC: | 206.5 |
| Df Residuals: | 19 | BIC: | 211.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -11.4717 | 74.063 | -0.155 | 0.879 | -166.487 143.544 |
| C(dose)[T.1] | 4.0980 | 104.806 | 0.039 | 0.969 | -215.263 223.459 |
| expression | 9.6845 | 10.888 | 0.890 | 0.385 | -13.103 32.472 |
| expression:C(dose)[T.1] | 7.0103 | 15.291 | 0.458 | 0.652 | -24.995 39.016 |
| Omnibus: | 1.259 | Durbin-Watson: | 1.783 |
| Prob(Omnibus): | 0.533 | Jarque-Bera (JB): | 0.862 |
| Skew: | -0.024 | Prob(JB): | 0.650 |
| Kurtosis: | 2.053 | Cond. No. | 229. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.696 |
| Model: | OLS | Adj. R-squared: | 0.666 |
| Method: | Least Squares | F-statistic: | 22.94 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.64e-06 |
| Time: | 05:12:22 | Log-Likelihood: | -99.395 |
| No. Observations: | 23 | AIC: | 204.8 |
| Df Residuals: | 20 | BIC: | 208.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -35.5739 | 51.126 | -0.696 | 0.495 | -142.221 71.073 |
| C(dose)[T.1] | 51.9930 | 8.192 | 6.347 | 0.000 | 34.905 69.081 |
| expression | 13.2383 | 7.493 | 1.767 | 0.093 | -2.391 28.867 |
| Omnibus: | 1.499 | Durbin-Watson: | 1.825 |
| Prob(Omnibus): | 0.473 | Jarque-Bera (JB): | 0.932 |
| Skew: | 0.004 | Prob(JB): | 0.627 |
| Kurtosis: | 2.014 | Cond. No. | 88.0 |
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: | 05:12:22 | 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: | 0.085 |
| Model: | OLS | Adj. R-squared: | 0.041 |
| Method: | Least Squares | F-statistic: | 1.951 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.177 |
| Time: | 05:12:22 | Log-Likelihood: | -112.08 |
| No. Observations: | 23 | AIC: | 228.2 |
| Df Residuals: | 21 | BIC: | 230.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -40.8727 | 86.612 | -0.472 | 0.642 | -220.992 139.247 |
| expression | 17.6545 | 12.640 | 1.397 | 0.177 | -8.631 43.940 |
| Omnibus: | 4.254 | Durbin-Watson: | 2.675 |
| Prob(Omnibus): | 0.119 | Jarque-Bera (JB): | 1.621 |
| Skew: | 0.206 | Prob(JB): | 0.445 |
| Kurtosis: | 1.766 | Cond. No. | 87.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.156 | 0.168 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.571 |
| Model: | OLS | Adj. R-squared: | 0.454 |
| Method: | Least Squares | F-statistic: | 4.885 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0214 |
| Time: | 05:12:22 | Log-Likelihood: | -68.949 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 11 | BIC: | 148.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 330.5787 | 152.095 | 2.173 | 0.052 | -4.181 665.338 |
| C(dose)[T.1] | -341.6515 | 381.932 | -0.895 | 0.390 | -1182.279 498.976 |
| expression | -36.8964 | 21.274 | -1.734 | 0.111 | -83.719 9.927 |
| expression:C(dose)[T.1] | 55.8724 | 56.221 | 0.994 | 0.342 | -67.868 179.613 |
| Omnibus: | 2.381 | Durbin-Watson: | 1.278 |
| Prob(Omnibus): | 0.304 | Jarque-Bera (JB): | 1.330 |
| Skew: | -0.728 | Prob(JB): | 0.514 |
| Kurtosis: | 2.910 | Cond. No. | 434. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.533 |
| Model: | OLS | Adj. R-squared: | 0.455 |
| Method: | Least Squares | F-statistic: | 6.840 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0104 |
| Time: | 05:12:22 | Log-Likelihood: | -69.594 |
| No. Observations: | 15 | AIC: | 145.2 |
| Df Residuals: | 12 | BIC: | 147.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 273.5216 | 140.771 | 1.943 | 0.076 | -33.191 580.235 |
| C(dose)[T.1] | 37.5599 | 16.518 | 2.274 | 0.042 | 1.571 73.549 |
| expression | -28.8964 | 19.682 | -1.468 | 0.168 | -71.779 13.986 |
| Omnibus: | 5.763 | Durbin-Watson: | 1.240 |
| Prob(Omnibus): | 0.056 | Jarque-Bera (JB): | 2.865 |
| Skew: | -0.962 | Prob(JB): | 0.239 |
| Kurtosis: | 3.938 | Cond. No. | 138. |
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: | 05:12:22 | 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: | 0.331 |
| Model: | OLS | Adj. R-squared: | 0.280 |
| Method: | Least Squares | F-statistic: | 6.442 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0247 |
| Time: | 05:12:22 | Log-Likelihood: | -72.281 |
| No. Observations: | 15 | AIC: | 148.6 |
| Df Residuals: | 13 | BIC: | 150.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 442.1051 | 137.530 | 3.215 | 0.007 | 144.990 739.220 |
| expression | -50.3715 | 19.845 | -2.538 | 0.025 | -93.245 -7.498 |
| Omnibus: | 0.201 | Durbin-Watson: | 1.833 |
| Prob(Omnibus): | 0.904 | Jarque-Bera (JB): | 0.376 |
| Skew: | 0.185 | Prob(JB): | 0.828 |
| Kurtosis: | 2.318 | Cond. No. | 117. |