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 |
| 1.733 | 0.203 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.724 |
| Model: | OLS | Adj. R-squared: | 0.680 |
| Method: | Least Squares | F-statistic: | 16.58 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.55e-05 |
| Time: | 04:27:02 | Log-Likelihood: | -98.316 |
| No. Observations: | 23 | AIC: | 204.6 |
| Df Residuals: | 19 | BIC: | 209.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 25.1507 | 265.329 | 0.095 | 0.925 | -530.190 580.492 |
| C(dose)[T.1] | 808.9937 | 416.727 | 1.941 | 0.067 | -63.225 1681.212 |
| expression | 2.7587 | 25.185 | 0.110 | 0.914 | -49.953 55.470 |
| expression:C(dose)[T.1] | -69.3307 | 38.740 | -1.790 | 0.089 | -150.415 11.754 |
| Omnibus: | 0.235 | Durbin-Watson: | 1.639 |
| Prob(Omnibus): | 0.889 | Jarque-Bera (JB): | 0.422 |
| Skew: | 0.145 | Prob(JB): | 0.810 |
| Kurtosis: | 2.403 | Cond. No. | 1.42e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.677 |
| Model: | OLS | Adj. R-squared: | 0.645 |
| Method: | Least Squares | F-statistic: | 20.96 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.23e-05 |
| Time: | 04:27:02 | Log-Likelihood: | -100.11 |
| No. Observations: | 23 | AIC: | 206.2 |
| Df Residuals: | 20 | BIC: | 209.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 333.7721 | 212.460 | 1.571 | 0.132 | -109.411 776.955 |
| C(dose)[T.1] | 63.4588 | 11.397 | 5.568 | 0.000 | 39.684 87.233 |
| expression | -26.5414 | 20.163 | -1.316 | 0.203 | -68.601 15.518 |
| Omnibus: | 2.426 | Durbin-Watson: | 1.762 |
| Prob(Omnibus): | 0.297 | Jarque-Bera (JB): | 1.295 |
| Skew: | 0.228 | Prob(JB): | 0.523 |
| Kurtosis: | 1.930 | Cond. No. | 548. |
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: | 04:27:02 | 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: | 0.176 |
| Model: | OLS | Adj. R-squared: | 0.137 |
| Method: | Least Squares | F-statistic: | 4.499 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0460 |
| Time: | 04:27:02 | Log-Likelihood: | -110.87 |
| No. Observations: | 23 | AIC: | 225.7 |
| Df Residuals: | 21 | BIC: | 228.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -447.4482 | 248.621 | -1.800 | 0.086 | -964.483 69.587 |
| expression | 49.1965 | 23.194 | 2.121 | 0.046 | 0.962 97.431 |
| Omnibus: | 0.536 | Durbin-Watson: | 2.336 |
| Prob(Omnibus): | 0.765 | Jarque-Bera (JB): | 0.451 |
| Skew: | 0.306 | Prob(JB): | 0.798 |
| Kurtosis: | 2.691 | Cond. No. | 410. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.529 | 0.240 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.511 |
| Model: | OLS | Adj. R-squared: | 0.378 |
| Method: | Least Squares | F-statistic: | 3.833 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0422 |
| Time: | 04:27:02 | Log-Likelihood: | -69.934 |
| No. Observations: | 15 | AIC: | 147.9 |
| Df Residuals: | 11 | BIC: | 150.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -523.3024 | 606.443 | -0.863 | 0.407 | -1858.074 811.469 |
| C(dose)[T.1] | 47.5616 | 1069.418 | 0.044 | 0.965 | -2306.213 2401.336 |
| expression | 56.1455 | 57.629 | 0.974 | 0.351 | -70.695 182.985 |
| expression:C(dose)[T.1] | -0.5264 | 100.798 | -0.005 | 0.996 | -222.381 221.328 |
| Omnibus: | 2.079 | Durbin-Watson: | 0.710 |
| Prob(Omnibus): | 0.354 | Jarque-Bera (JB): | 1.358 |
| Skew: | -0.502 | Prob(JB): | 0.507 |
| Kurtosis: | 1.921 | Cond. No. | 1.83e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.511 |
| Model: | OLS | Adj. R-squared: | 0.430 |
| Method: | Least Squares | F-statistic: | 6.272 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0137 |
| Time: | 04:27:02 | Log-Likelihood: | -69.934 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 12 | BIC: | 148.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -521.4921 | 476.411 | -1.095 | 0.295 | -1559.503 516.519 |
| C(dose)[T.1] | 41.9776 | 15.932 | 2.635 | 0.022 | 7.265 76.690 |
| expression | 55.9734 | 45.268 | 1.236 | 0.240 | -42.658 154.605 |
| Omnibus: | 2.087 | Durbin-Watson: | 0.710 |
| Prob(Omnibus): | 0.352 | Jarque-Bera (JB): | 1.358 |
| Skew: | -0.501 | Prob(JB): | 0.507 |
| Kurtosis: | 1.918 | Cond. No. | 689. |
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: | 04:27:02 | 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: | 0.228 |
| Model: | OLS | Adj. R-squared: | 0.169 |
| Method: | Least Squares | F-statistic: | 3.844 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0717 |
| Time: | 04:27:02 | Log-Likelihood: | -73.357 |
| No. Observations: | 15 | AIC: | 150.7 |
| Df Residuals: | 13 | BIC: | 152.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -961.9799 | 538.506 | -1.786 | 0.097 | -2125.352 201.392 |
| expression | 99.6813 | 50.842 | 1.961 | 0.072 | -10.157 209.520 |
| Omnibus: | 0.791 | Durbin-Watson: | 1.447 |
| Prob(Omnibus): | 0.673 | Jarque-Bera (JB): | 0.700 |
| Skew: | 0.229 | Prob(JB): | 0.705 |
| Kurtosis: | 2.046 | Cond. No. | 645. |