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 |
| 0.430 | 0.520 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.672 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 12.96 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.70e-05 |
| Time: | 04:56:58 | Log-Likelihood: | -100.30 |
| No. Observations: | 23 | AIC: | 208.6 |
| Df Residuals: | 19 | BIC: | 213.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 23.0807 | 138.168 | 0.167 | 0.869 | -266.108 312.269 |
| C(dose)[T.1] | 232.6302 | 191.286 | 1.216 | 0.239 | -167.736 632.996 |
| expression | 4.0284 | 17.864 | 0.226 | 0.824 | -33.362 41.419 |
| expression:C(dose)[T.1] | -23.2298 | 24.746 | -0.939 | 0.360 | -75.024 28.565 |
| Omnibus: | 0.178 | Durbin-Watson: | 1.821 |
| Prob(Omnibus): | 0.915 | Jarque-Bera (JB): | 0.191 |
| Skew: | -0.165 | Prob(JB): | 0.909 |
| Kurtosis: | 2.700 | Cond. No. | 453. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 19.11 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.29e-05 |
| Time: | 04:56:58 | Log-Likelihood: | -100.82 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 116.6232 | 95.426 | 1.222 | 0.236 | -82.431 315.678 |
| C(dose)[T.1] | 53.2519 | 8.678 | 6.136 | 0.000 | 35.150 71.354 |
| expression | -8.0775 | 12.325 | -0.655 | 0.520 | -33.788 17.632 |
| Omnibus: | 0.207 | Durbin-Watson: | 1.921 |
| Prob(Omnibus): | 0.902 | Jarque-Bera (JB): | 0.411 |
| Skew: | -0.027 | Prob(JB): | 0.814 |
| Kurtosis: | 2.348 | Cond. No. | 173. |
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: | 04:56:58 | 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: | -0.038 |
| Method: | Least Squares | F-statistic: | 0.2034 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.657 |
| Time: | 04:56:58 | Log-Likelihood: | -112.99 |
| No. Observations: | 23 | AIC: | 230.0 |
| Df Residuals: | 21 | BIC: | 232.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 150.8389 | 157.844 | 0.956 | 0.350 | -177.417 479.094 |
| expression | -9.2103 | 20.420 | -0.451 | 0.657 | -51.676 33.255 |
| Omnibus: | 4.501 | Durbin-Watson: | 2.524 |
| Prob(Omnibus): | 0.105 | Jarque-Bera (JB): | 1.668 |
| Skew: | 0.212 | Prob(JB): | 0.434 |
| Kurtosis: | 1.751 | Cond. No. | 173. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.418 | 0.530 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.503 |
| Model: | OLS | Adj. R-squared: | 0.367 |
| Method: | Least Squares | F-statistic: | 3.708 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0459 |
| Time: | 04:56:58 | Log-Likelihood: | -70.059 |
| No. Observations: | 15 | AIC: | 148.1 |
| Df Residuals: | 11 | BIC: | 151.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 0.2131 | 68.198 | 0.003 | 0.998 | -149.889 150.315 |
| C(dose)[T.1] | 160.3631 | 120.904 | 1.326 | 0.212 | -105.744 426.471 |
| expression | 12.1356 | 12.140 | 1.000 | 0.339 | -14.583 38.855 |
| expression:C(dose)[T.1] | -21.3599 | 24.112 | -0.886 | 0.395 | -74.430 31.710 |
| Omnibus: | 3.043 | Durbin-Watson: | 0.792 |
| Prob(Omnibus): | 0.218 | Jarque-Bera (JB): | 1.708 |
| Skew: | -0.827 | Prob(JB): | 0.426 |
| Kurtosis: | 2.995 | Cond. No. | 99.5 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.467 |
| Model: | OLS | Adj. R-squared: | 0.379 |
| Method: | Least Squares | F-statistic: | 5.264 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0228 |
| Time: | 04:56:58 | Log-Likelihood: | -70.576 |
| 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 | 30.2017 | 58.669 | 0.515 | 0.616 | -97.628 158.031 |
| C(dose)[T.1] | 54.3988 | 17.439 | 3.119 | 0.009 | 16.402 92.395 |
| expression | 6.7212 | 10.394 | 0.647 | 0.530 | -15.926 29.368 |
| Omnibus: | 1.884 | Durbin-Watson: | 0.866 |
| Prob(Omnibus): | 0.390 | Jarque-Bera (JB): | 1.380 |
| Skew: | -0.699 | Prob(JB): | 0.502 |
| Kurtosis: | 2.495 | Cond. No. | 41.6 |
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: | 0.035 |
| Model: | OLS | Adj. R-squared: | -0.039 |
| Method: | Least Squares | F-statistic: | 0.4773 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.502 |
| Time: | 04:56:58 | Log-Likelihood: | -75.030 |
| No. Observations: | 15 | AIC: | 154.1 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 135.8885 | 61.926 | 2.194 | 0.047 | 2.105 269.672 |
| expression | -8.2370 | 11.923 | -0.691 | 0.502 | -33.995 17.521 |
| Omnibus: | 0.431 | Durbin-Watson: | 1.505 |
| Prob(Omnibus): | 0.806 | Jarque-Bera (JB): | 0.450 |
| Skew: | -0.326 | Prob(JB): | 0.799 |
| Kurtosis: | 2.456 | Cond. No. | 33.4 |