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 |
| 0.398 | 0.535 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.674 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 13.11 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.15e-05 |
| Time: | 04:46:27 | Log-Likelihood: | -100.21 |
| No. Observations: | 23 | AIC: | 208.4 |
| Df Residuals: | 19 | BIC: | 213.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 9.9443 | 39.881 | 0.249 | 0.806 | -73.527 93.416 |
| C(dose)[T.1] | 123.4526 | 69.401 | 1.779 | 0.091 | -21.806 268.711 |
| expression | 9.0912 | 8.098 | 1.123 | 0.276 | -7.858 26.040 |
| expression:C(dose)[T.1] | -14.1218 | 13.648 | -1.035 | 0.314 | -42.687 14.443 |
| Omnibus: | 0.490 | Durbin-Watson: | 1.929 |
| Prob(Omnibus): | 0.783 | Jarque-Bera (JB): | 0.489 |
| Skew: | -0.299 | Prob(JB): | 0.783 |
| Kurtosis: | 2.608 | Cond. No. | 102. |
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: | 19.06 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.33e-05 |
| Time: | 04:46:27 | 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 | 34.1519 | 32.355 | 1.056 | 0.304 | -33.340 101.643 |
| C(dose)[T.1] | 52.2251 | 8.861 | 5.894 | 0.000 | 33.741 70.709 |
| expression | 4.1193 | 6.530 | 0.631 | 0.535 | -9.502 17.740 |
| Omnibus: | 1.696 | Durbin-Watson: | 1.951 |
| Prob(Omnibus): | 0.428 | Jarque-Bera (JB): | 0.996 |
| Skew: | 0.065 | Prob(JB): | 0.608 |
| Kurtosis: | 1.989 | Cond. No. | 39.3 |
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: | 04:46:27 | 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: | 0.058 |
| Model: | OLS | Adj. R-squared: | 0.013 |
| Method: | Least Squares | F-statistic: | 1.299 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.267 |
| Time: | 04:46:27 | Log-Likelihood: | -112.41 |
| No. Observations: | 23 | AIC: | 228.8 |
| Df Residuals: | 21 | BIC: | 231.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 20.8648 | 52.110 | 0.400 | 0.693 | -87.503 129.233 |
| expression | 11.7753 | 10.332 | 1.140 | 0.267 | -9.710 33.261 |
| Omnibus: | 1.870 | Durbin-Watson: | 2.368 |
| Prob(Omnibus): | 0.393 | Jarque-Bera (JB): | 1.426 |
| Skew: | 0.421 | Prob(JB): | 0.490 |
| Kurtosis: | 2.118 | Cond. No. | 39.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.322 | 0.273 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.516 |
| Model: | OLS | Adj. R-squared: | 0.383 |
| Method: | Least Squares | F-statistic: | 3.902 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0402 |
| Time: | 04:46:27 | Log-Likelihood: | -69.865 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 11 | BIC: | 150.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 22.1630 | 76.992 | 0.288 | 0.779 | -147.295 191.621 |
| C(dose)[T.1] | -25.2600 | 135.544 | -0.186 | 0.856 | -323.590 273.071 |
| expression | 8.6621 | 14.575 | 0.594 | 0.564 | -23.417 40.741 |
| expression:C(dose)[T.1] | 12.9807 | 24.809 | 0.523 | 0.611 | -41.624 67.585 |
| Omnibus: | 0.926 | Durbin-Watson: | 0.757 |
| Prob(Omnibus): | 0.629 | Jarque-Bera (JB): | 0.836 |
| Skew: | -0.462 | Prob(JB): | 0.658 |
| Kurtosis: | 2.304 | Cond. No. | 123. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.503 |
| Model: | OLS | Adj. R-squared: | 0.421 |
| Method: | Least Squares | F-statistic: | 6.084 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0150 |
| Time: | 04:46:27 | Log-Likelihood: | -70.049 |
| No. Observations: | 15 | AIC: | 146.1 |
| Df Residuals: | 12 | BIC: | 148.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -1.2491 | 60.729 | -0.021 | 0.984 | -133.566 131.068 |
| C(dose)[T.1] | 45.1747 | 15.343 | 2.944 | 0.012 | 11.746 78.604 |
| expression | 13.1423 | 11.432 | 1.150 | 0.273 | -11.766 38.051 |
| Omnibus: | 1.199 | Durbin-Watson: | 0.862 |
| Prob(Omnibus): | 0.549 | Jarque-Bera (JB): | 0.981 |
| Skew: | -0.547 | Prob(JB): | 0.612 |
| Kurtosis: | 2.388 | Cond. No. | 46.0 |
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: | 04:46:27 | 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: | 0.145 |
| Model: | OLS | Adj. R-squared: | 0.079 |
| Method: | Least Squares | F-statistic: | 2.200 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.162 |
| Time: | 04:46:27 | Log-Likelihood: | -74.128 |
| No. Observations: | 15 | AIC: | 152.3 |
| Df Residuals: | 13 | BIC: | 153.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -18.5163 | 76.217 | -0.243 | 0.812 | -183.173 146.141 |
| expression | 20.8173 | 14.035 | 1.483 | 0.162 | -9.504 51.139 |
| Omnibus: | 1.558 | Durbin-Watson: | 1.421 |
| Prob(Omnibus): | 0.459 | Jarque-Bera (JB): | 0.903 |
| Skew: | 0.187 | Prob(JB): | 0.637 |
| Kurtosis: | 1.858 | Cond. No. | 45.5 |