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 |
| 2.951 | 0.101 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.698 |
| Model: | OLS | Adj. R-squared: | 0.650 |
| Method: | Least Squares | F-statistic: | 14.64 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.53e-05 |
| Time: | 04:19:26 | Log-Likelihood: | -99.335 |
| No. Observations: | 23 | AIC: | 206.7 |
| Df Residuals: | 19 | BIC: | 211.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 161.3992 | 137.544 | 1.173 | 0.255 | -126.484 449.283 |
| C(dose)[T.1] | 147.6507 | 188.095 | 0.785 | 0.442 | -246.036 541.337 |
| expression | -16.3455 | 20.956 | -0.780 | 0.445 | -60.206 27.515 |
| expression:C(dose)[T.1] | -13.9832 | 28.482 | -0.491 | 0.629 | -73.597 45.630 |
| Omnibus: | 2.275 | Durbin-Watson: | 1.954 |
| Prob(Omnibus): | 0.321 | Jarque-Bera (JB): | 1.233 |
| Skew: | 0.203 | Prob(JB): | 0.540 |
| Kurtosis: | 1.941 | Cond. No. | 403. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.694 |
| Model: | OLS | Adj. R-squared: | 0.664 |
| Method: | Least Squares | F-statistic: | 22.70 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.15e-06 |
| Time: | 04:19:26 | Log-Likelihood: | -99.480 |
| No. Observations: | 23 | AIC: | 205.0 |
| Df Residuals: | 20 | BIC: | 208.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 211.0387 | 91.462 | 2.307 | 0.032 | 20.251 401.826 |
| C(dose)[T.1] | 55.3986 | 8.274 | 6.695 | 0.000 | 38.139 72.658 |
| expression | -23.9151 | 13.920 | -1.718 | 0.101 | -52.952 5.122 |
| Omnibus: | 3.731 | Durbin-Watson: | 2.032 |
| Prob(Omnibus): | 0.155 | Jarque-Bera (JB): | 1.530 |
| Skew: | 0.202 | Prob(JB): | 0.465 |
| Kurtosis: | 1.803 | Cond. No. | 152. |
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: | 04:19:26 | 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: | 0.009 |
| Model: | OLS | Adj. R-squared: | -0.038 |
| Method: | Least Squares | F-statistic: | 0.1846 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.672 |
| Time: | 04:19:26 | Log-Likelihood: | -113.00 |
| 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 | 148.3354 | 159.857 | 0.928 | 0.364 | -184.105 480.775 |
| expression | -10.3982 | 24.200 | -0.430 | 0.672 | -60.724 39.928 |
| Omnibus: | 3.028 | Durbin-Watson: | 2.551 |
| Prob(Omnibus): | 0.220 | Jarque-Bera (JB): | 1.498 |
| Skew: | 0.280 | Prob(JB): | 0.473 |
| Kurtosis: | 1.882 | Cond. No. | 150. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.260 | 0.619 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.471 |
| Model: | OLS | Adj. R-squared: | 0.327 |
| Method: | Least Squares | F-statistic: | 3.264 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0631 |
| Time: | 04:19:26 | Log-Likelihood: | -70.526 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 151.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 90.3748 | 177.026 | 0.511 | 0.620 | -299.256 480.006 |
| C(dose)[T.1] | 197.2427 | 312.084 | 0.632 | 0.540 | -489.650 884.136 |
| expression | -3.5182 | 27.083 | -0.130 | 0.899 | -63.127 56.090 |
| expression:C(dose)[T.1] | -21.7017 | 46.560 | -0.466 | 0.650 | -124.180 80.777 |
| Omnibus: | 2.447 | Durbin-Watson: | 0.954 |
| Prob(Omnibus): | 0.294 | Jarque-Bera (JB): | 1.653 |
| Skew: | -0.793 | Prob(JB): | 0.438 |
| Kurtosis: | 2.643 | Cond. No. | 330. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.460 |
| Model: | OLS | Adj. R-squared: | 0.371 |
| Method: | Least Squares | F-statistic: | 5.121 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0247 |
| Time: | 04:19:26 | Log-Likelihood: | -70.672 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 12 | BIC: | 149.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 138.2632 | 139.379 | 0.992 | 0.341 | -165.417 441.943 |
| C(dose)[T.1] | 51.9984 | 16.513 | 3.149 | 0.008 | 16.020 87.977 |
| expression | -10.8608 | 21.299 | -0.510 | 0.619 | -57.267 35.546 |
| Omnibus: | 2.671 | Durbin-Watson: | 0.878 |
| Prob(Omnibus): | 0.263 | Jarque-Bera (JB): | 1.874 |
| Skew: | -0.840 | Prob(JB): | 0.392 |
| Kurtosis: | 2.578 | Cond. No. | 123. |
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: | 04:19:26 | 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: | 0.1930 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.668 |
| Time: | 04:19:26 | Log-Likelihood: | -75.190 |
| 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 | 17.3616 | 173.968 | 0.100 | 0.922 | -358.473 393.196 |
| expression | 11.4578 | 26.079 | 0.439 | 0.668 | -44.882 67.797 |
| Omnibus: | 0.546 | Durbin-Watson: | 1.548 |
| Prob(Omnibus): | 0.761 | Jarque-Bera (JB): | 0.561 |
| Skew: | 0.058 | Prob(JB): | 0.756 |
| Kurtosis: | 2.060 | Cond. No. | 118. |