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 |
| 0.746 | 0.398 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.693 |
| Model: | OLS | Adj. R-squared: | 0.645 |
| Method: | Least Squares | F-statistic: | 14.30 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.11e-05 |
| Time: | 04:42:05 | Log-Likelihood: | -99.521 |
| No. Observations: | 23 | AIC: | 207.0 |
| Df Residuals: | 19 | BIC: | 211.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 271.5362 | 131.749 | 2.061 | 0.053 | -4.218 547.291 |
| C(dose)[T.1] | -165.4487 | 157.387 | -1.051 | 0.306 | -494.863 163.965 |
| expression | -29.9433 | 18.135 | -1.651 | 0.115 | -67.900 8.013 |
| expression:C(dose)[T.1] | 30.1429 | 21.612 | 1.395 | 0.179 | -15.092 75.378 |
| Omnibus: | 0.120 | Durbin-Watson: | 1.882 |
| Prob(Omnibus): | 0.942 | Jarque-Bera (JB): | 0.337 |
| Skew: | -0.068 | Prob(JB): | 0.845 |
| Kurtosis: | 2.423 | Cond. No. | 395. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 19.56 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.96e-05 |
| Time: | 04:42:05 | Log-Likelihood: | -100.64 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 117.5043 | 73.516 | 1.598 | 0.126 | -35.848 270.856 |
| C(dose)[T.1] | 53.7440 | 8.624 | 6.232 | 0.000 | 35.756 71.732 |
| expression | -8.7209 | 10.096 | -0.864 | 0.398 | -29.780 12.338 |
| Omnibus: | 0.030 | Durbin-Watson: | 1.920 |
| Prob(Omnibus): | 0.985 | Jarque-Bera (JB): | 0.237 |
| Skew: | 0.033 | Prob(JB): | 0.888 |
| Kurtosis: | 2.507 | Cond. No. | 127. |
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: | 04:42:05 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.043 |
| Method: | Least Squares | F-statistic: | 0.09808 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.757 |
| Time: | 04:42:05 | Log-Likelihood: | -113.05 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 118.1908 | 123.058 | 0.960 | 0.348 | -137.723 374.105 |
| expression | -5.2846 | 16.874 | -0.313 | 0.757 | -40.376 29.807 |
| Omnibus: | 3.439 | Durbin-Watson: | 2.477 |
| Prob(Omnibus): | 0.179 | Jarque-Bera (JB): | 1.662 |
| Skew: | 0.328 | Prob(JB): | 0.436 |
| Kurtosis: | 1.858 | Cond. No. | 127. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.526 | 0.240 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.537 |
| Model: | OLS | Adj. R-squared: | 0.411 |
| Method: | Least Squares | F-statistic: | 4.254 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0318 |
| Time: | 04:42:05 | Log-Likelihood: | -69.523 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 11 | BIC: | 149.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 246.1726 | 124.373 | 1.979 | 0.073 | -27.570 519.915 |
| C(dose)[T.1] | -108.7020 | 202.237 | -0.537 | 0.602 | -553.823 336.419 |
| expression | -23.6735 | 16.408 | -1.443 | 0.177 | -59.787 12.440 |
| expression:C(dose)[T.1] | 20.9370 | 26.564 | 0.788 | 0.447 | -37.530 79.404 |
| Omnibus: | 2.652 | Durbin-Watson: | 1.101 |
| Prob(Omnibus): | 0.266 | Jarque-Bera (JB): | 1.497 |
| Skew: | -0.773 | Prob(JB): | 0.473 |
| Kurtosis: | 2.932 | Cond. No. | 263. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.511 |
| Model: | OLS | Adj. R-squared: | 0.429 |
| Method: | Least Squares | F-statistic: | 6.269 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0137 |
| Time: | 04:42:05 | Log-Likelihood: | -69.935 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 12 | BIC: | 148.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 185.8619 | 96.487 | 1.926 | 0.078 | -24.366 396.090 |
| C(dose)[T.1] | 50.2507 | 14.850 | 3.384 | 0.005 | 17.896 82.606 |
| expression | -15.6857 | 12.698 | -1.235 | 0.240 | -43.353 11.982 |
| Omnibus: | 2.494 | Durbin-Watson: | 0.887 |
| Prob(Omnibus): | 0.287 | Jarque-Bera (JB): | 1.790 |
| Skew: | -0.812 | Prob(JB): | 0.409 |
| Kurtosis: | 2.523 | Cond. No. | 101. |
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: | 04:42:05 | 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: | 0.044 |
| Model: | OLS | Adj. R-squared: | -0.029 |
| Method: | Least Squares | F-statistic: | 0.6024 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.452 |
| Time: | 04:42:05 | Log-Likelihood: | -74.960 |
| No. Observations: | 15 | AIC: | 153.9 |
| Df Residuals: | 13 | BIC: | 155.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 193.9257 | 129.553 | 1.497 | 0.158 | -85.956 473.807 |
| expression | -13.2159 | 17.027 | -0.776 | 0.452 | -50.001 23.569 |
| Omnibus: | 3.364 | Durbin-Watson: | 1.827 |
| Prob(Omnibus): | 0.186 | Jarque-Bera (JB): | 1.346 |
| Skew: | 0.300 | Prob(JB): | 0.510 |
| Kurtosis: | 1.661 | Cond. No. | 101. |