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 |
| 5.750 | 0.026 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.728 |
| Model: | OLS | Adj. R-squared: | 0.685 |
| Method: | Least Squares | F-statistic: | 16.94 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.34e-05 |
| Time: | 04:11:49 | Log-Likelihood: | -98.136 |
| No. Observations: | 23 | AIC: | 204.3 |
| Df Residuals: | 19 | BIC: | 208.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -42.6144 | 94.856 | -0.449 | 0.658 | -241.150 155.921 |
| C(dose)[T.1] | 33.2458 | 109.978 | 0.302 | 0.766 | -196.940 263.431 |
| expression | 14.9211 | 14.594 | 1.022 | 0.319 | -15.624 45.466 |
| expression:C(dose)[T.1] | 3.0768 | 16.900 | 0.182 | 0.857 | -32.295 38.448 |
| Omnibus: | 1.311 | Durbin-Watson: | 1.986 |
| Prob(Omnibus): | 0.519 | Jarque-Bera (JB): | 0.938 |
| Skew: | 0.170 | Prob(JB): | 0.626 |
| Kurtosis: | 2.071 | Cond. No. | 268. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.727 |
| Model: | OLS | Adj. R-squared: | 0.700 |
| Method: | Least Squares | F-statistic: | 26.69 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.26e-06 |
| Time: | 04:11:49 | Log-Likelihood: | -98.156 |
| No. Observations: | 23 | AIC: | 202.3 |
| Df Residuals: | 20 | BIC: | 205.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -57.5026 | 46.891 | -1.226 | 0.234 | -155.315 40.310 |
| C(dose)[T.1] | 53.2165 | 7.729 | 6.885 | 0.000 | 37.094 69.339 |
| expression | 17.2155 | 7.179 | 2.398 | 0.026 | 2.240 32.191 |
| Omnibus: | 1.519 | Durbin-Watson: | 2.006 |
| Prob(Omnibus): | 0.468 | Jarque-Bera (JB): | 0.977 |
| Skew: | 0.133 | Prob(JB): | 0.614 |
| Kurtosis: | 2.026 | Cond. No. | 81.2 |
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: | 04:11:49 | 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: | 0.038 |
| Method: | Least Squares | F-statistic: | 1.859 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.187 |
| Time: | 04:11:49 | Log-Likelihood: | -112.13 |
| 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 | -34.1397 | 83.790 | -0.407 | 0.688 | -208.390 140.111 |
| expression | 17.5372 | 12.862 | 1.363 | 0.187 | -9.211 44.285 |
| Omnibus: | 3.497 | Durbin-Watson: | 2.451 |
| Prob(Omnibus): | 0.174 | Jarque-Bera (JB): | 1.475 |
| Skew: | 0.191 | Prob(JB): | 0.478 |
| Kurtosis: | 1.820 | Cond. No. | 80.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.248 | 0.286 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.540 |
| Model: | OLS | Adj. R-squared: | 0.414 |
| Method: | Least Squares | F-statistic: | 4.303 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0308 |
| Time: | 04:11:49 | Log-Likelihood: | -69.477 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 11 | BIC: | 149.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 35.8702 | 80.284 | 0.447 | 0.664 | -140.833 212.573 |
| C(dose)[T.1] | -90.8989 | 145.321 | -0.626 | 0.544 | -410.748 228.950 |
| expression | 5.3346 | 13.444 | 0.397 | 0.699 | -24.255 34.924 |
| expression:C(dose)[T.1] | 23.6513 | 24.416 | 0.969 | 0.354 | -30.087 77.390 |
| Omnibus: | 1.799 | Durbin-Watson: | 0.631 |
| Prob(Omnibus): | 0.407 | Jarque-Bera (JB): | 1.394 |
| Skew: | -0.603 | Prob(JB): | 0.498 |
| Kurtosis: | 2.118 | Cond. No. | 145. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.501 |
| Model: | OLS | Adj. R-squared: | 0.417 |
| Method: | Least Squares | F-statistic: | 6.017 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0155 |
| Time: | 04:11:49 | Log-Likelihood: | -70.091 |
| No. Observations: | 15 | AIC: | 146.2 |
| Df Residuals: | 12 | BIC: | 148.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -6.5506 | 67.116 | -0.098 | 0.924 | -152.783 139.682 |
| C(dose)[T.1] | 49.1191 | 14.980 | 3.279 | 0.007 | 16.480 81.758 |
| expression | 12.5054 | 11.193 | 1.117 | 0.286 | -11.883 36.894 |
| Omnibus: | 1.687 | Durbin-Watson: | 0.609 |
| Prob(Omnibus): | 0.430 | Jarque-Bera (JB): | 1.336 |
| Skew: | -0.598 | Prob(JB): | 0.513 |
| Kurtosis: | 2.159 | Cond. No. | 55.2 |
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: | 04:11:49 | 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: | 0.053 |
| Model: | OLS | Adj. R-squared: | -0.019 |
| Method: | Least Squares | F-statistic: | 0.7327 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.408 |
| Time: | 04:11:49 | Log-Likelihood: | -74.889 |
| No. Observations: | 15 | AIC: | 153.8 |
| Df Residuals: | 13 | BIC: | 155.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 18.6432 | 88.205 | 0.211 | 0.836 | -171.912 209.199 |
| expression | 12.6749 | 14.808 | 0.856 | 0.408 | -19.316 44.665 |
| Omnibus: | 2.058 | Durbin-Watson: | 1.500 |
| Prob(Omnibus): | 0.357 | Jarque-Bera (JB): | 0.963 |
| Skew: | 0.066 | Prob(JB): | 0.618 |
| Kurtosis: | 1.766 | Cond. No. | 54.6 |