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 |
| 0.051 | 0.824 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.680 |
| Model: | OLS | Adj. R-squared: | 0.629 |
| Method: | Least Squares | F-statistic: | 13.43 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.15e-05 |
| Time: | 05:14:01 | Log-Likelihood: | -100.02 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 19 | BIC: | 212.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -178.9679 | 221.724 | -0.807 | 0.430 | -643.041 285.105 |
| C(dose)[T.1] | 490.1603 | 329.480 | 1.488 | 0.153 | -199.450 1179.771 |
| expression | 21.4057 | 20.347 | 1.052 | 0.306 | -21.181 63.992 |
| expression:C(dose)[T.1] | -40.5159 | 30.606 | -1.324 | 0.201 | -104.574 23.542 |
| Omnibus: | 1.170 | Durbin-Watson: | 1.580 |
| Prob(Omnibus): | 0.557 | Jarque-Bera (JB): | 0.994 |
| Skew: | -0.468 | Prob(JB): | 0.608 |
| Kurtosis: | 2.598 | Cond. No. | 1.05e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.57 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.76e-05 |
| Time: | 05:14:01 | Log-Likelihood: | -101.03 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 16.0982 | 168.765 | 0.095 | 0.925 | -335.939 368.135 |
| C(dose)[T.1] | 54.1655 | 9.495 | 5.705 | 0.000 | 34.359 73.972 |
| expression | 3.4985 | 15.483 | 0.226 | 0.824 | -28.798 35.795 |
| Omnibus: | 0.464 | Durbin-Watson: | 1.825 |
| Prob(Omnibus): | 0.793 | Jarque-Bera (JB): | 0.562 |
| Skew: | 0.059 | Prob(JB): | 0.755 |
| Kurtosis: | 2.243 | Cond. No. | 420. |
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: | 05:14:01 | 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: | 0.080 |
| Model: | OLS | Adj. R-squared: | 0.037 |
| Method: | Least Squares | F-statistic: | 1.835 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.190 |
| Time: | 05:14:01 | Log-Likelihood: | -112.14 |
| No. Observations: | 23 | AIC: | 228.3 |
| Df Residuals: | 21 | BIC: | 230.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 409.6174 | 243.632 | 1.681 | 0.108 | -97.043 916.277 |
| expression | -30.6031 | 22.591 | -1.355 | 0.190 | -77.584 16.378 |
| Omnibus: | 5.961 | Durbin-Watson: | 2.549 |
| Prob(Omnibus): | 0.051 | Jarque-Bera (JB): | 1.751 |
| Skew: | 0.078 | Prob(JB): | 0.417 |
| Kurtosis: | 1.657 | Cond. No. | 383. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.332 | 0.575 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.555 |
| Model: | OLS | Adj. R-squared: | 0.433 |
| Method: | Least Squares | F-statistic: | 4.569 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0260 |
| Time: | 05:14:01 | Log-Likelihood: | -69.231 |
| No. Observations: | 15 | AIC: | 146.5 |
| Df Residuals: | 11 | BIC: | 149.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 641.0639 | 384.670 | 1.667 | 0.124 | -205.590 1487.717 |
| C(dose)[T.1] | -774.1775 | 553.412 | -1.399 | 0.189 | -1992.229 443.874 |
| expression | -58.7627 | 39.390 | -1.492 | 0.164 | -145.459 27.934 |
| expression:C(dose)[T.1] | 83.3301 | 55.519 | 1.501 | 0.162 | -38.867 205.527 |
| Omnibus: | 1.187 | Durbin-Watson: | 1.462 |
| Prob(Omnibus): | 0.552 | Jarque-Bera (JB): | 0.679 |
| Skew: | -0.507 | Prob(JB): | 0.712 |
| Kurtosis: | 2.760 | Cond. No. | 1.01e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.464 |
| Model: | OLS | Adj. R-squared: | 0.374 |
| Method: | Least Squares | F-statistic: | 5.186 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0238 |
| Time: | 05:14:01 | Log-Likelihood: | -70.628 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 12 | BIC: | 149.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 231.6010 | 285.000 | 0.813 | 0.432 | -389.361 852.563 |
| C(dose)[T.1] | 55.9833 | 19.485 | 2.873 | 0.014 | 13.530 98.437 |
| expression | -16.8177 | 29.172 | -0.576 | 0.575 | -80.378 46.743 |
| Omnibus: | 3.960 | Durbin-Watson: | 0.876 |
| Prob(Omnibus): | 0.138 | Jarque-Bera (JB): | 2.461 |
| Skew: | -0.992 | Prob(JB): | 0.292 |
| Kurtosis: | 2.940 | Cond. No. | 372. |
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: | 05:14:01 | 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: | 0.095 |
| Model: | OLS | Adj. R-squared: | 0.025 |
| Method: | Least Squares | F-statistic: | 1.359 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.265 |
| Time: | 05:14:01 | Log-Likelihood: | -74.554 |
| No. Observations: | 15 | AIC: | 153.1 |
| Df Residuals: | 13 | BIC: | 154.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -243.8015 | 289.655 | -0.842 | 0.415 | -869.562 381.959 |
| expression | 33.8242 | 29.016 | 1.166 | 0.265 | -28.860 96.509 |
| Omnibus: | 0.159 | Durbin-Watson: | 1.316 |
| Prob(Omnibus): | 0.924 | Jarque-Bera (JB): | 0.188 |
| Skew: | -0.178 | Prob(JB): | 0.910 |
| Kurtosis: | 2.584 | Cond. No. | 302. |