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 |
| 5.816 | 0.026 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.737 |
| Model: | OLS | Adj. R-squared: | 0.696 |
| Method: | Least Squares | F-statistic: | 17.75 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.71e-06 |
| Time: | 05:11:58 | Log-Likelihood: | -97.742 |
| No. Observations: | 23 | AIC: | 203.5 |
| Df Residuals: | 19 | BIC: | 208.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 194.3072 | 168.929 | 1.150 | 0.264 | -159.265 547.879 |
| C(dose)[T.1] | 205.0662 | 208.769 | 0.982 | 0.338 | -231.893 642.026 |
| expression | -15.4468 | 18.616 | -0.830 | 0.417 | -54.411 23.517 |
| expression:C(dose)[T.1] | -18.9484 | 23.562 | -0.804 | 0.431 | -68.264 30.367 |
| Omnibus: | 0.755 | Durbin-Watson: | 2.139 |
| Prob(Omnibus): | 0.685 | Jarque-Bera (JB): | 0.554 |
| Skew: | 0.359 | Prob(JB): | 0.758 |
| Kurtosis: | 2.749 | Cond. No. | 655. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.728 |
| Model: | OLS | Adj. R-squared: | 0.701 |
| Method: | Least Squares | F-statistic: | 26.78 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.21e-06 |
| Time: | 05:11:58 | Log-Likelihood: | -98.127 |
| No. Observations: | 23 | AIC: | 202.3 |
| Df Residuals: | 20 | BIC: | 205.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 301.5907 | 102.718 | 2.936 | 0.008 | 87.325 515.856 |
| C(dose)[T.1] | 37.3752 | 10.168 | 3.676 | 0.001 | 16.165 58.586 |
| expression | -27.2756 | 11.310 | -2.412 | 0.026 | -50.868 -3.683 |
| Omnibus: | 1.400 | Durbin-Watson: | 2.196 |
| Prob(Omnibus): | 0.497 | Jarque-Bera (JB): | 0.700 |
| Skew: | 0.426 | Prob(JB): | 0.705 |
| Kurtosis: | 3.063 | Cond. No. | 238. |
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: | 05:11:58 | 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: | 0.544 |
| Model: | OLS | Adj. R-squared: | 0.523 |
| Method: | Least Squares | F-statistic: | 25.10 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.86e-05 |
| Time: | 05:11:58 | Log-Likelihood: | -104.06 |
| No. Observations: | 23 | AIC: | 212.1 |
| Df Residuals: | 21 | BIC: | 214.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 557.3251 | 95.459 | 5.838 | 0.000 | 358.808 755.842 |
| expression | -54.3362 | 10.846 | -5.010 | 0.000 | -76.892 -31.781 |
| Omnibus: | 0.972 | Durbin-Watson: | 2.309 |
| Prob(Omnibus): | 0.615 | Jarque-Bera (JB): | 0.949 |
| Skew: | 0.391 | Prob(JB): | 0.622 |
| Kurtosis: | 2.383 | Cond. No. | 175. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 4.365 | 0.059 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.597 |
| Model: | OLS | Adj. R-squared: | 0.487 |
| Method: | Least Squares | F-statistic: | 5.432 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0154 |
| Time: | 05:11:58 | Log-Likelihood: | -68.484 |
| No. Observations: | 15 | AIC: | 145.0 |
| Df Residuals: | 11 | BIC: | 147.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 334.6031 | 150.271 | 2.227 | 0.048 | 3.858 665.348 |
| C(dose)[T.1] | -0.0803 | 277.853 | -0.000 | 1.000 | -611.630 611.470 |
| expression | -29.0244 | 16.287 | -1.782 | 0.102 | -64.871 6.822 |
| expression:C(dose)[T.1] | 5.4682 | 30.042 | 0.182 | 0.859 | -60.654 71.590 |
| Omnibus: | 1.591 | Durbin-Watson: | 1.434 |
| Prob(Omnibus): | 0.451 | Jarque-Bera (JB): | 0.923 |
| Skew: | -0.598 | Prob(JB): | 0.630 |
| Kurtosis: | 2.782 | Cond. No. | 453. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.596 |
| Model: | OLS | Adj. R-squared: | 0.528 |
| Method: | Least Squares | F-statistic: | 8.844 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00436 |
| Time: | 05:11:58 | Log-Likelihood: | -68.506 |
| No. Observations: | 15 | AIC: | 143.0 |
| Df Residuals: | 12 | BIC: | 145.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 319.8095 | 121.196 | 2.639 | 0.022 | 55.745 583.874 |
| C(dose)[T.1] | 50.4294 | 13.491 | 3.738 | 0.003 | 21.035 79.823 |
| expression | -27.4173 | 13.123 | -2.089 | 0.059 | -56.009 1.174 |
| Omnibus: | 1.401 | Durbin-Watson: | 1.417 |
| Prob(Omnibus): | 0.496 | Jarque-Bera (JB): | 0.852 |
| Skew: | -0.568 | Prob(JB): | 0.653 |
| Kurtosis: | 2.726 | Cond. No. | 169. |
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: | 05:11:58 | 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: | 0.125 |
| Model: | OLS | Adj. R-squared: | 0.058 |
| Method: | Least Squares | F-statistic: | 1.860 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.196 |
| Time: | 05:11:58 | Log-Likelihood: | -74.297 |
| No. Observations: | 15 | AIC: | 152.6 |
| Df Residuals: | 13 | BIC: | 154.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 326.9024 | 171.287 | 1.909 | 0.079 | -43.141 696.946 |
| expression | -25.2716 | 18.531 | -1.364 | 0.196 | -65.305 14.762 |
| Omnibus: | 3.240 | Durbin-Watson: | 2.133 |
| Prob(Omnibus): | 0.198 | Jarque-Bera (JB): | 1.669 |
| Skew: | 0.529 | Prob(JB): | 0.434 |
| Kurtosis: | 1.754 | Cond. No. | 169. |