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 |
| 1.615 | 0.218 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.676 |
| Model: | OLS | Adj. R-squared: | 0.625 |
| Method: | Least Squares | F-statistic: | 13.22 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.77e-05 |
| Time: | 06:23:27 | Log-Likelihood: | -100.14 |
| No. Observations: | 23 | AIC: | 208.3 |
| Df Residuals: | 19 | BIC: | 212.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -68.9304 | 127.061 | -0.542 | 0.594 | -334.872 197.011 |
| C(dose)[T.1] | 93.2399 | 163.871 | 0.569 | 0.576 | -249.746 436.226 |
| expression | 17.5433 | 18.082 | 0.970 | 0.344 | -20.303 55.389 |
| expression:C(dose)[T.1] | -5.3849 | 23.551 | -0.229 | 0.822 | -54.677 43.907 |
| Omnibus: | 0.223 | Durbin-Watson: | 2.087 |
| Prob(Omnibus): | 0.894 | Jarque-Bera (JB): | 0.042 |
| Skew: | 0.087 | Prob(JB): | 0.979 |
| Kurtosis: | 2.886 | Cond. No. | 363. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.675 |
| Model: | OLS | Adj. R-squared: | 0.643 |
| Method: | Least Squares | F-statistic: | 20.80 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.30e-05 |
| Time: | 06:23:27 | Log-Likelihood: | -100.17 |
| No. Observations: | 23 | AIC: | 206.3 |
| Df Residuals: | 20 | BIC: | 209.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -46.6485 | 79.581 | -0.586 | 0.564 | -212.652 119.355 |
| C(dose)[T.1] | 55.8251 | 8.660 | 6.446 | 0.000 | 37.760 73.890 |
| expression | 14.3688 | 11.307 | 1.271 | 0.218 | -9.218 37.955 |
| Omnibus: | 0.208 | Durbin-Watson: | 2.052 |
| Prob(Omnibus): | 0.901 | Jarque-Bera (JB): | 0.023 |
| Skew: | 0.057 | Prob(JB): | 0.989 |
| Kurtosis: | 2.895 | Cond. No. | 134. |
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: | 06:23:27 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.01252 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.912 |
| Time: | 06:23:27 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 94.3536 | 131.000 | 0.720 | 0.479 | -178.076 366.783 |
| expression | -2.1101 | 18.857 | -0.112 | 0.912 | -41.326 37.106 |
| Omnibus: | 3.105 | Durbin-Watson: | 2.478 |
| Prob(Omnibus): | 0.212 | Jarque-Bera (JB): | 1.528 |
| Skew: | 0.289 | Prob(JB): | 0.466 |
| Kurtosis: | 1.877 | Cond. No. | 129. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.001 | 0.977 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.453 |
| Model: | OLS | Adj. R-squared: | 0.304 |
| Method: | Least Squares | F-statistic: | 3.041 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0745 |
| Time: | 06:23:27 | Log-Likelihood: | -70.771 |
| No. Observations: | 15 | AIC: | 149.5 |
| Df Residuals: | 11 | BIC: | 152.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 39.4917 | 185.501 | 0.213 | 0.835 | -368.794 447.778 |
| C(dose)[T.1] | 148.1360 | 328.030 | 0.452 | 0.660 | -573.854 870.126 |
| expression | 3.7545 | 24.878 | 0.151 | 0.883 | -51.002 58.511 |
| expression:C(dose)[T.1] | -12.9702 | 43.010 | -0.302 | 0.769 | -107.634 81.694 |
| Omnibus: | 3.058 | Durbin-Watson: | 0.831 |
| Prob(Omnibus): | 0.217 | Jarque-Bera (JB): | 2.029 |
| Skew: | -0.889 | Prob(JB): | 0.363 |
| Kurtosis: | 2.715 | Cond. No. | 385. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.886 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0280 |
| Time: | 06:23:27 | Log-Likelihood: | -70.832 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 71.7819 | 145.627 | 0.493 | 0.631 | -245.512 389.076 |
| C(dose)[T.1] | 49.3507 | 16.558 | 2.980 | 0.011 | 13.274 85.428 |
| expression | -0.5851 | 19.510 | -0.030 | 0.977 | -43.094 41.924 |
| Omnibus: | 2.686 | Durbin-Watson: | 0.812 |
| Prob(Omnibus): | 0.261 | Jarque-Bera (JB): | 1.852 |
| Skew: | -0.839 | Prob(JB): | 0.396 |
| Kurtosis: | 2.615 | Cond. No. | 144. |
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: | 06:23:27 | 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: | 0.041 |
| Model: | OLS | Adj. R-squared: | -0.033 |
| Method: | Least Squares | F-statistic: | 0.5529 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.470 |
| Time: | 06:23:27 | Log-Likelihood: | -74.988 |
| No. Observations: | 15 | AIC: | 154.0 |
| Df Residuals: | 13 | BIC: | 155.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -38.8407 | 178.477 | -0.218 | 0.831 | -424.417 346.736 |
| expression | 17.4777 | 23.505 | 0.744 | 0.470 | -33.301 68.256 |
| Omnibus: | 0.038 | Durbin-Watson: | 1.559 |
| Prob(Omnibus): | 0.981 | Jarque-Bera (JB): | 0.266 |
| Skew: | 0.006 | Prob(JB): | 0.875 |
| Kurtosis: | 2.348 | Cond. No. | 139. |