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 |
| 0.091 | 0.766 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.609 |
| Method: | Least Squares | F-statistic: | 12.42 |
| Date: | Thu, 16 Jan 2025 | Prob (F-statistic): | 0.000100 |
| Time: | 03:59:16 | Log-Likelihood: | -100.62 |
| No. Observations: | 23 | AIC: | 209.2 |
| Df Residuals: | 19 | BIC: | 213.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 71.0550 | 30.876 | 2.301 | 0.033 | 6.432 135.678 |
| C(dose)[T.1] | -20.6733 | 92.603 | -0.223 | 0.826 | -214.494 173.147 |
| expression | -6.6794 | 12.000 | -0.557 | 0.584 | -31.795 18.437 |
| expression:C(dose)[T.1] | 28.4183 | 35.220 | 0.807 | 0.430 | -45.298 102.134 |
| Omnibus: | 0.275 | Durbin-Watson: | 1.738 |
| Prob(Omnibus): | 0.871 | Jarque-Bera (JB): | 0.456 |
| Skew: | 0.031 | Prob(JB): | 0.796 |
| Kurtosis: | 2.313 | Cond. No. | 71.7 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.62 |
| Date: | Thu, 16 Jan 2025 | Prob (F-statistic): | 2.71e-05 |
| Time: | 03:59:16 | Log-Likelihood: | -101.01 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 62.7343 | 28.848 | 2.175 | 0.042 | 2.559 122.909 |
| C(dose)[T.1] | 53.7001 | 8.832 | 6.080 | 0.000 | 35.277 72.123 |
| expression | -3.3804 | 11.183 | -0.302 | 0.766 | -26.708 19.947 |
| Omnibus: | 0.208 | Durbin-Watson: | 1.816 |
| Prob(Omnibus): | 0.901 | Jarque-Bera (JB): | 0.411 |
| Skew: | 0.027 | Prob(JB): | 0.814 |
| Kurtosis: | 2.347 | Cond. No. | 20.0 |
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, 16 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 03:59:16 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.042 |
| Method: | Least Squares | F-statistic: | 0.1032 |
| Date: | Thu, 16 Jan 2025 | Prob (F-statistic): | 0.751 |
| Time: | 03:59:16 | Log-Likelihood: | -113.05 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 64.6278 | 47.511 | 1.360 | 0.188 | -34.177 163.433 |
| expression | 5.8633 | 18.248 | 0.321 | 0.751 | -32.086 43.813 |
| Omnibus: | 3.004 | Durbin-Watson: | 2.554 |
| Prob(Omnibus): | 0.223 | Jarque-Bera (JB): | 1.448 |
| Skew: | 0.248 | Prob(JB): | 0.485 |
| Kurtosis: | 1.875 | Cond. No. | 19.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 79.985 | 0.000 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.931 |
| Model: | OLS | Adj. R-squared: | 0.912 |
| Method: | Least Squares | F-statistic: | 49.18 |
| Date: | Thu, 16 Jan 2025 | Prob (F-statistic): | 1.16e-06 |
| Time: | 03:59:16 | Log-Likelihood: | -55.289 |
| No. Observations: | 15 | AIC: | 118.6 |
| Df Residuals: | 11 | BIC: | 121.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 170.1288 | 22.965 | 7.408 | 0.000 | 119.584 220.674 |
| C(dose)[T.1] | 45.2753 | 26.804 | 1.689 | 0.119 | -13.721 104.271 |
| expression | -37.4864 | 8.237 | -4.551 | 0.001 | -55.616 -19.357 |
| expression:C(dose)[T.1] | 5.8691 | 9.263 | 0.634 | 0.539 | -14.518 26.256 |
| Omnibus: | 0.523 | Durbin-Watson: | 1.854 |
| Prob(Omnibus): | 0.770 | Jarque-Bera (JB): | 0.563 |
| Skew: | -0.132 | Prob(JB): | 0.755 |
| Kurtosis: | 2.088 | Cond. No. | 47.5 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.928 |
| Model: | OLS | Adj. R-squared: | 0.916 |
| Method: | Least Squares | F-statistic: | 77.44 |
| Date: | Thu, 16 Jan 2025 | Prob (F-statistic): | 1.38e-07 |
| Time: | 03:59:16 | Log-Likelihood: | -55.558 |
| No. Observations: | 15 | AIC: | 117.1 |
| Df Residuals: | 12 | BIC: | 119.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 157.4137 | 10.884 | 14.462 | 0.000 | 133.698 181.129 |
| C(dose)[T.1] | 61.8269 | 5.858 | 10.555 | 0.000 | 49.064 74.590 |
| expression | -32.8453 | 3.673 | -8.943 | 0.000 | -40.847 -24.844 |
| Omnibus: | 1.000 | Durbin-Watson: | 2.009 |
| Prob(Omnibus): | 0.607 | Jarque-Bera (JB): | 0.707 |
| Skew: | -0.011 | Prob(JB): | 0.702 |
| Kurtosis: | 1.937 | Cond. No. | 13.1 |
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, 16 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 03:59:16 | 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: | 0.261 |
| Model: | OLS | Adj. R-squared: | 0.204 |
| Method: | Least Squares | F-statistic: | 4.580 |
| Date: | Thu, 16 Jan 2025 | Prob (F-statistic): | 0.0519 |
| Time: | 03:59:16 | Log-Likelihood: | -73.037 |
| No. Observations: | 15 | AIC: | 150.1 |
| Df Residuals: | 13 | BIC: | 151.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 162.8679 | 33.497 | 4.862 | 0.000 | 90.502 235.234 |
| expression | -23.4998 | 10.981 | -2.140 | 0.052 | -47.224 0.224 |
| Omnibus: | 5.445 | Durbin-Watson: | 2.400 |
| Prob(Omnibus): | 0.066 | Jarque-Bera (JB): | 1.443 |
| Skew: | -0.046 | Prob(JB): | 0.486 |
| Kurtosis: | 1.483 | Cond. No. | 12.9 |