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 |
| 3.745 | 0.067 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.726 |
| Model: | OLS | Adj. R-squared: | 0.683 |
| Method: | Least Squares | F-statistic: | 16.79 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.42e-05 |
| Time: | 04:42:52 | Log-Likelihood: | -98.210 |
| No. Observations: | 23 | AIC: | 204.4 |
| Df Residuals: | 19 | BIC: | 209.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 90.0206 | 50.040 | 1.799 | 0.088 | -14.714 194.756 |
| C(dose)[T.1] | 142.3845 | 75.925 | 1.875 | 0.076 | -16.527 301.296 |
| expression | -4.9833 | 6.921 | -0.720 | 0.480 | -19.469 9.502 |
| expression:C(dose)[T.1] | -13.2827 | 10.815 | -1.228 | 0.234 | -35.920 9.354 |
| Omnibus: | 0.724 | Durbin-Watson: | 1.760 |
| Prob(Omnibus): | 0.696 | Jarque-Bera (JB): | 0.663 |
| Skew: | 0.365 | Prob(JB): | 0.718 |
| Kurtosis: | 2.601 | Cond. No. | 172. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.704 |
| Model: | OLS | Adj. R-squared: | 0.675 |
| Method: | Least Squares | F-statistic: | 23.83 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.09e-06 |
| Time: | 04:42:52 | Log-Likelihood: | -99.089 |
| No. Observations: | 23 | AIC: | 204.2 |
| Df Residuals: | 20 | BIC: | 207.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 129.1087 | 39.101 | 3.302 | 0.004 | 47.545 210.673 |
| C(dose)[T.1] | 49.6805 | 8.267 | 6.009 | 0.000 | 32.435 66.926 |
| expression | -10.4224 | 5.386 | -1.935 | 0.067 | -21.656 0.812 |
| Omnibus: | 0.134 | Durbin-Watson: | 1.984 |
| Prob(Omnibus): | 0.935 | Jarque-Bera (JB): | 0.253 |
| Skew: | 0.155 | Prob(JB): | 0.881 |
| Kurtosis: | 2.591 | Cond. No. | 70.3 |
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: | 04:42:52 | 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: | 0.171 |
| Model: | OLS | Adj. R-squared: | 0.131 |
| Method: | Least Squares | F-statistic: | 4.323 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0500 |
| Time: | 04:42:52 | Log-Likelihood: | -110.95 |
| No. Observations: | 23 | AIC: | 225.9 |
| Df Residuals: | 21 | BIC: | 228.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 204.7819 | 60.510 | 3.384 | 0.003 | 78.945 330.619 |
| expression | -17.8188 | 8.570 | -2.079 | 0.050 | -35.642 0.004 |
| Omnibus: | 1.320 | Durbin-Watson: | 2.488 |
| Prob(Omnibus): | 0.517 | Jarque-Bera (JB): | 0.881 |
| Skew: | 0.028 | Prob(JB): | 0.644 |
| Kurtosis: | 2.043 | Cond. No. | 66.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.272 | 0.281 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.679 |
| Model: | OLS | Adj. R-squared: | 0.591 |
| Method: | Least Squares | F-statistic: | 7.752 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00466 |
| Time: | 04:42:52 | Log-Likelihood: | -66.781 |
| No. Observations: | 15 | AIC: | 141.6 |
| Df Residuals: | 11 | BIC: | 144.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 138.2517 | 105.038 | 1.316 | 0.215 | -92.935 369.438 |
| C(dose)[T.1] | -308.6598 | 149.009 | -2.071 | 0.063 | -636.627 19.307 |
| expression | -10.0989 | 14.921 | -0.677 | 0.512 | -42.939 22.741 |
| expression:C(dose)[T.1] | 54.3374 | 22.051 | 2.464 | 0.031 | 5.804 102.870 |
| Omnibus: | 1.245 | Durbin-Watson: | 1.433 |
| Prob(Omnibus): | 0.537 | Jarque-Bera (JB): | 0.969 |
| Skew: | -0.561 | Prob(JB): | 0.616 |
| Kurtosis: | 2.461 | Cond. No. | 214. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.502 |
| Model: | OLS | Adj. R-squared: | 0.419 |
| Method: | Least Squares | F-statistic: | 6.039 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0153 |
| Time: | 04:42:52 | Log-Likelihood: | -70.077 |
| No. Observations: | 15 | AIC: | 146.2 |
| Df Residuals: | 12 | BIC: | 148.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -36.2213 | 92.544 | -0.391 | 0.702 | -237.858 165.416 |
| C(dose)[T.1] | 56.9509 | 16.470 | 3.458 | 0.005 | 21.066 92.836 |
| expression | 14.7797 | 13.104 | 1.128 | 0.281 | -13.771 43.331 |
| Omnibus: | 2.518 | Durbin-Watson: | 0.940 |
| Prob(Omnibus): | 0.284 | Jarque-Bera (JB): | 1.650 |
| Skew: | -0.798 | Prob(JB): | 0.438 |
| Kurtosis: | 2.699 | Cond. No. | 86.1 |
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: | 04:42:52 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.072 |
| Method: | Least Squares | F-statistic: | 0.06543 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.802 |
| Time: | 04:42:52 | Log-Likelihood: | -75.262 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 121.5073 | 109.310 | 1.112 | 0.286 | -114.642 357.657 |
| expression | -4.1349 | 16.165 | -0.256 | 0.802 | -39.056 30.787 |
| Omnibus: | 0.772 | Durbin-Watson: | 1.515 |
| Prob(Omnibus): | 0.680 | Jarque-Bera (JB): | 0.652 |
| Skew: | 0.114 | Prob(JB): | 0.722 |
| Kurtosis: | 2.005 | Cond. No. | 74.5 |