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 |
| 0.390 | 0.539 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.677 |
| Model: | OLS | Adj. R-squared: | 0.625 |
| Method: | Least Squares | F-statistic: | 13.25 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.70e-05 |
| Time: | 03:38:21 | Log-Likelihood: | -100.13 |
| No. Observations: | 23 | AIC: | 208.3 |
| Df Residuals: | 19 | BIC: | 212.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 133.0759 | 63.775 | 2.087 | 0.051 | -0.408 266.559 |
| C(dose)[T.1] | -40.6585 | 85.553 | -0.475 | 0.640 | -219.722 138.405 |
| expression | -11.9240 | 9.600 | -1.242 | 0.229 | -32.017 8.169 |
| expression:C(dose)[T.1] | 14.2109 | 12.868 | 1.104 | 0.283 | -12.722 41.144 |
| Omnibus: | 0.569 | Durbin-Watson: | 2.008 |
| Prob(Omnibus): | 0.752 | Jarque-Bera (JB): | 0.610 |
| Skew: | -0.043 | Prob(JB): | 0.737 |
| Kurtosis: | 2.207 | Cond. No. | 179. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 19.05 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.34e-05 |
| Time: | 03:38:21 | Log-Likelihood: | -100.84 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 80.7631 | 42.936 | 1.881 | 0.075 | -8.800 170.326 |
| C(dose)[T.1] | 53.3404 | 8.686 | 6.141 | 0.000 | 35.223 71.458 |
| expression | -4.0148 | 6.428 | -0.625 | 0.539 | -17.423 9.393 |
| Omnibus: | 0.095 | Durbin-Watson: | 1.921 |
| Prob(Omnibus): | 0.953 | Jarque-Bera (JB): | 0.316 |
| Skew: | 0.053 | Prob(JB): | 0.854 |
| Kurtosis: | 2.435 | Cond. No. | 67.4 |
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: | 03:38:21 | 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: | 0.007 |
| Model: | OLS | Adj. R-squared: | -0.041 |
| Method: | Least Squares | F-statistic: | 0.1403 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.712 |
| Time: | 03:38:21 | Log-Likelihood: | -113.03 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 106.1165 | 70.850 | 1.498 | 0.149 | -41.225 253.458 |
| expression | -3.9910 | 10.656 | -0.375 | 0.712 | -26.151 18.169 |
| Omnibus: | 3.015 | Durbin-Watson: | 2.535 |
| Prob(Omnibus): | 0.221 | Jarque-Bera (JB): | 1.604 |
| Skew: | 0.345 | Prob(JB): | 0.448 |
| Kurtosis: | 1.905 | Cond. No. | 67.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.326 | 0.579 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.495 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 3.600 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0496 |
| Time: | 03:38:21 | Log-Likelihood: | -70.170 |
| No. Observations: | 15 | AIC: | 148.3 |
| Df Residuals: | 11 | BIC: | 151.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 2.2917 | 116.342 | 0.020 | 0.985 | -253.775 258.358 |
| C(dose)[T.1] | 155.6905 | 126.870 | 1.227 | 0.245 | -123.548 434.929 |
| expression | 12.1176 | 21.538 | 0.563 | 0.585 | -35.286 59.522 |
| expression:C(dose)[T.1] | -19.4298 | 23.244 | -0.836 | 0.421 | -70.590 31.731 |
| Omnibus: | 4.465 | Durbin-Watson: | 0.678 |
| Prob(Omnibus): | 0.107 | Jarque-Bera (JB): | 2.382 |
| Skew: | -0.961 | Prob(JB): | 0.304 |
| Kurtosis: | 3.340 | Cond. No. | 147. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.463 |
| Model: | OLS | Adj. R-squared: | 0.374 |
| Method: | Least Squares | F-statistic: | 5.180 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0239 |
| Time: | 03:38:21 | Log-Likelihood: | -70.632 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 12 | BIC: | 149.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 91.9593 | 44.465 | 2.068 | 0.061 | -4.923 188.841 |
| C(dose)[T.1] | 50.4766 | 15.692 | 3.217 | 0.007 | 16.287 84.666 |
| expression | -4.5635 | 7.998 | -0.571 | 0.579 | -21.991 12.864 |
| Omnibus: | 4.033 | Durbin-Watson: | 0.874 |
| Prob(Omnibus): | 0.133 | Jarque-Bera (JB): | 2.380 |
| Skew: | -0.975 | Prob(JB): | 0.304 |
| Kurtosis: | 3.055 | Cond. No. | 33.3 |
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: | 03:38:21 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.076 |
| Method: | Least Squares | F-statistic: | 0.007262 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.933 |
| Time: | 03:38:21 | Log-Likelihood: | -75.296 |
| 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 | 98.5536 | 58.238 | 1.692 | 0.114 | -27.262 224.369 |
| expression | -0.8845 | 10.379 | -0.085 | 0.933 | -23.307 21.538 |
| Omnibus: | 0.508 | Durbin-Watson: | 1.624 |
| Prob(Omnibus): | 0.776 | Jarque-Bera (JB): | 0.543 |
| Skew: | 0.023 | Prob(JB): | 0.762 |
| Kurtosis: | 2.069 | Cond. No. | 33.2 |