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 |
| 0.017 | 0.898 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.596 |
| Method: | Least Squares | F-statistic: | 11.83 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000134 |
| Time: | 04:49:44 | Log-Likelihood: | -100.99 |
| No. Observations: | 23 | AIC: | 210.0 |
| Df Residuals: | 19 | BIC: | 214.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 72.9500 | 55.861 | 1.306 | 0.207 | -43.968 189.868 |
| C(dose)[T.1] | 30.9274 | 67.944 | 0.455 | 0.654 | -111.281 173.136 |
| expression | -2.9053 | 8.606 | -0.338 | 0.739 | -20.918 15.107 |
| expression:C(dose)[T.1] | 3.5331 | 10.801 | 0.327 | 0.747 | -19.073 26.139 |
| Omnibus: | 0.317 | Durbin-Watson: | 1.882 |
| Prob(Omnibus): | 0.853 | Jarque-Bera (JB): | 0.481 |
| Skew: | -0.041 | Prob(JB): | 0.786 |
| Kurtosis: | 2.296 | Cond. No. | 133. |
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: | 04:49:44 | 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 | 58.4803 | 33.344 | 1.754 | 0.095 | -11.074 128.034 |
| C(dose)[T.1] | 52.9344 | 9.295 | 5.695 | 0.000 | 33.545 72.324 |
| expression | -0.6622 | 5.083 | -0.130 | 0.898 | -11.265 9.940 |
| Omnibus: | 0.334 | Durbin-Watson: | 1.909 |
| Prob(Omnibus): | 0.846 | Jarque-Bera (JB): | 0.490 |
| Skew: | 0.016 | Prob(JB): | 0.783 |
| Kurtosis: | 2.285 | Cond. No. | 49.1 |
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: | 04:49:44 | 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: | 0.081 |
| Model: | OLS | Adj. R-squared: | 0.037 |
| Method: | Least Squares | F-statistic: | 1.845 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.189 |
| Time: | 04:49:44 | Log-Likelihood: | -112.14 |
| No. Observations: | 23 | AIC: | 228.3 |
| Df Residuals: | 21 | BIC: | 230.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 143.0946 | 47.166 | 3.034 | 0.006 | 45.006 241.183 |
| expression | -10.2885 | 7.574 | -1.358 | 0.189 | -26.040 5.463 |
| Omnibus: | 1.461 | Durbin-Watson: | 2.479 |
| Prob(Omnibus): | 0.482 | Jarque-Bera (JB): | 0.999 |
| Skew: | 0.189 | Prob(JB): | 0.607 |
| Kurtosis: | 2.052 | Cond. No. | 43.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 6.032 | 0.030 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.929 |
| Model: | OLS | Adj. R-squared: | 0.909 |
| Method: | Least Squares | F-statistic: | 47.75 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.35e-06 |
| Time: | 04:49:44 | Log-Likelihood: | -55.495 |
| No. Observations: | 15 | AIC: | 119.0 |
| Df Residuals: | 11 | BIC: | 121.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 509.7777 | 107.676 | 4.734 | 0.001 | 272.785 746.771 |
| C(dose)[T.1] | -708.7947 | 115.563 | -6.133 | 0.000 | -963.146 -454.443 |
| expression | -61.5058 | 14.960 | -4.111 | 0.002 | -94.432 -28.580 |
| expression:C(dose)[T.1] | 109.8369 | 16.269 | 6.751 | 0.000 | 74.029 145.645 |
| Omnibus: | 3.056 | Durbin-Watson: | 1.287 |
| Prob(Omnibus): | 0.217 | Jarque-Bera (JB): | 1.546 |
| Skew: | 0.480 | Prob(JB): | 0.462 |
| Kurtosis: | 1.754 | Cond. No. | 420. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.633 |
| Model: | OLS | Adj. R-squared: | 0.572 |
| Method: | Least Squares | F-statistic: | 10.36 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00244 |
| Time: | 04:49:44 | Log-Likelihood: | -67.779 |
| No. Observations: | 15 | AIC: | 141.6 |
| Df Residuals: | 12 | BIC: | 143.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -158.1132 | 92.308 | -1.713 | 0.112 | -359.234 43.008 |
| C(dose)[T.1] | 69.9308 | 15.367 | 4.551 | 0.001 | 36.450 103.412 |
| expression | 31.3601 | 12.768 | 2.456 | 0.030 | 3.540 59.180 |
| Omnibus: | 7.580 | Durbin-Watson: | 0.906 |
| Prob(Omnibus): | 0.023 | Jarque-Bera (JB): | 4.139 |
| Skew: | -1.091 | Prob(JB): | 0.126 |
| Kurtosis: | 4.364 | Cond. No. | 102. |
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: | 04:49:44 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.077 |
| Method: | Least Squares | F-statistic: | 0.001106 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.974 |
| Time: | 04:49:44 | Log-Likelihood: | -75.299 |
| 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 | 97.5156 | 116.191 | 0.839 | 0.416 | -153.500 348.532 |
| expression | -0.5628 | 16.924 | -0.033 | 0.974 | -37.124 35.998 |
| Omnibus: | 0.628 | Durbin-Watson: | 1.612 |
| Prob(Omnibus): | 0.730 | Jarque-Bera (JB): | 0.591 |
| Skew: | 0.057 | Prob(JB): | 0.744 |
| Kurtosis: | 2.034 | Cond. No. | 80.2 |