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 |
| 0.192 | 0.666 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.669 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 12.79 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.34e-05 |
| Time: | 04:56:57 | Log-Likelihood: | -100.40 |
| No. Observations: | 23 | AIC: | 208.8 |
| Df Residuals: | 19 | BIC: | 213.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 132.3178 | 75.346 | 1.756 | 0.095 | -25.384 290.020 |
| C(dose)[T.1] | -37.7412 | 95.126 | -0.397 | 0.696 | -236.843 161.360 |
| expression | -12.4123 | 11.935 | -1.040 | 0.311 | -37.392 12.567 |
| expression:C(dose)[T.1] | 14.3951 | 14.843 | 0.970 | 0.344 | -16.672 45.462 |
| Omnibus: | 0.742 | Durbin-Watson: | 1.667 |
| Prob(Omnibus): | 0.690 | Jarque-Bera (JB): | 0.721 |
| Skew: | -0.165 | Prob(JB): | 0.697 |
| Kurtosis: | 2.198 | Cond. No. | 199. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 18.77 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.58e-05 |
| Time: | 04:56:57 | Log-Likelihood: | -100.95 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 73.7543 | 44.994 | 1.639 | 0.117 | -20.102 167.611 |
| C(dose)[T.1] | 54.1069 | 8.903 | 6.077 | 0.000 | 35.536 72.678 |
| expression | -3.1060 | 7.085 | -0.438 | 0.666 | -17.886 11.674 |
| Omnibus: | 0.355 | Durbin-Watson: | 1.775 |
| Prob(Omnibus): | 0.837 | Jarque-Bera (JB): | 0.504 |
| Skew: | 0.056 | Prob(JB): | 0.777 |
| Kurtosis: | 2.284 | Cond. No. | 68.3 |
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: | 04:56:57 | 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: | 0.010 |
| Model: | OLS | Adj. R-squared: | -0.037 |
| Method: | Least Squares | F-statistic: | 0.2218 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.643 |
| Time: | 04:56:57 | Log-Likelihood: | -112.98 |
| No. Observations: | 23 | AIC: | 230.0 |
| Df Residuals: | 21 | BIC: | 232.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 45.1804 | 73.681 | 0.613 | 0.546 | -108.048 198.408 |
| expression | 5.3868 | 11.437 | 0.471 | 0.643 | -18.399 29.172 |
| Omnibus: | 3.941 | Durbin-Watson: | 2.549 |
| Prob(Omnibus): | 0.139 | Jarque-Bera (JB): | 1.622 |
| Skew: | 0.246 | Prob(JB): | 0.444 |
| Kurtosis: | 1.796 | Cond. No. | 67.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.358 | 0.561 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.474 |
| Model: | OLS | Adj. R-squared: | 0.330 |
| Method: | Least Squares | F-statistic: | 3.298 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0615 |
| Time: | 04:56:58 | Log-Likelihood: | -70.488 |
| No. Observations: | 15 | AIC: | 149.0 |
| Df Residuals: | 11 | BIC: | 151.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 146.3994 | 115.739 | 1.265 | 0.232 | -108.342 401.140 |
| C(dose)[T.1] | -11.6969 | 142.792 | -0.082 | 0.936 | -325.980 302.586 |
| expression | -10.9574 | 15.976 | -0.686 | 0.507 | -46.121 24.207 |
| expression:C(dose)[T.1] | 8.4530 | 19.677 | 0.430 | 0.676 | -34.855 51.761 |
| Omnibus: | 2.239 | Durbin-Watson: | 0.984 |
| Prob(Omnibus): | 0.326 | Jarque-Bera (JB): | 1.584 |
| Skew: | -0.764 | Prob(JB): | 0.453 |
| Kurtosis: | 2.554 | Cond. No. | 188. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.465 |
| Model: | OLS | Adj. R-squared: | 0.376 |
| Method: | Least Squares | F-statistic: | 5.209 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0235 |
| Time: | 04:56:58 | Log-Likelihood: | -70.613 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 106.2365 | 65.870 | 1.613 | 0.133 | -37.281 249.754 |
| C(dose)[T.1] | 49.2560 | 15.511 | 3.176 | 0.008 | 15.462 83.050 |
| expression | -5.3847 | 9.003 | -0.598 | 0.561 | -25.002 14.232 |
| Omnibus: | 2.668 | Durbin-Watson: | 0.863 |
| Prob(Omnibus): | 0.263 | Jarque-Bera (JB): | 1.880 |
| Skew: | -0.840 | Prob(JB): | 0.391 |
| Kurtosis: | 2.569 | Cond. No. | 63.2 |
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: | 04:56:58 | 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: | 0.015 |
| Model: | OLS | Adj. R-squared: | -0.061 |
| Method: | Least Squares | F-statistic: | 0.1964 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.665 |
| Time: | 04:56:58 | Log-Likelihood: | -75.188 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 131.1817 | 85.241 | 1.539 | 0.148 | -52.971 315.335 |
| expression | -5.2011 | 11.735 | -0.443 | 0.665 | -30.553 20.150 |
| Omnibus: | 1.417 | Durbin-Watson: | 1.750 |
| Prob(Omnibus): | 0.492 | Jarque-Bera (JB): | 0.831 |
| Skew: | 0.099 | Prob(JB): | 0.660 |
| Kurtosis: | 1.864 | Cond. No. | 62.5 |