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 |
| 0.058 | 0.813 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.655 |
| Model: | OLS | Adj. R-squared: | 0.600 |
| Method: | Least Squares | F-statistic: | 12.02 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000122 |
| Time: | 03:49:57 | Log-Likelihood: | -100.87 |
| No. Observations: | 23 | AIC: | 209.7 |
| Df Residuals: | 19 | BIC: | 214.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 79.0740 | 100.809 | 0.784 | 0.442 | -131.922 290.070 |
| C(dose)[T.1] | -12.6424 | 129.040 | -0.098 | 0.923 | -282.727 257.442 |
| expression | -3.7239 | 15.069 | -0.247 | 0.807 | -35.263 27.816 |
| expression:C(dose)[T.1] | 10.1389 | 19.597 | 0.517 | 0.611 | -30.878 51.156 |
| Omnibus: | 0.108 | Durbin-Watson: | 1.921 |
| Prob(Omnibus): | 0.947 | Jarque-Bera (JB): | 0.327 |
| Skew: | 0.064 | Prob(JB): | 0.849 |
| Kurtosis: | 2.430 | Cond. No. | 263. |
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: | 18.58 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.75e-05 |
| Time: | 03:49:57 | 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 | 39.0441 | 63.429 | 0.616 | 0.545 | -93.267 171.355 |
| C(dose)[T.1] | 53.9465 | 9.117 | 5.917 | 0.000 | 34.928 72.965 |
| expression | 2.2710 | 9.456 | 0.240 | 0.813 | -17.453 21.995 |
| Omnibus: | 0.228 | Durbin-Watson: | 1.859 |
| Prob(Omnibus): | 0.892 | Jarque-Bera (JB): | 0.425 |
| Skew: | 0.046 | Prob(JB): | 0.808 |
| Kurtosis: | 2.340 | Cond. No. | 97.7 |
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: | 03:49:57 | 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: | 0.038 |
| Model: | OLS | Adj. R-squared: | -0.008 |
| Method: | Least Squares | F-statistic: | 0.8185 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.376 |
| Time: | 03:49:57 | Log-Likelihood: | -112.66 |
| No. Observations: | 23 | AIC: | 229.3 |
| Df Residuals: | 21 | BIC: | 231.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 166.8108 | 96.527 | 1.728 | 0.099 | -33.928 367.550 |
| expression | -13.2987 | 14.699 | -0.905 | 0.376 | -43.868 17.271 |
| Omnibus: | 2.524 | Durbin-Watson: | 2.521 |
| Prob(Omnibus): | 0.283 | Jarque-Bera (JB): | 1.529 |
| Skew: | 0.368 | Prob(JB): | 0.465 |
| Kurtosis: | 1.973 | Cond. No. | 91.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 4.734 | 0.050 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.608 |
| Model: | OLS | Adj. R-squared: | 0.501 |
| Method: | Least Squares | F-statistic: | 5.676 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0134 |
| Time: | 03:49:57 | Log-Likelihood: | -68.285 |
| No. Observations: | 15 | AIC: | 144.6 |
| Df Residuals: | 11 | BIC: | 147.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 222.8603 | 88.610 | 2.515 | 0.029 | 27.831 417.889 |
| C(dose)[T.1] | 27.4088 | 146.090 | 0.188 | 0.855 | -294.132 348.950 |
| expression | -26.6155 | 15.074 | -1.766 | 0.105 | -59.793 6.562 |
| expression:C(dose)[T.1] | 6.4900 | 23.046 | 0.282 | 0.783 | -44.234 57.214 |
| Omnibus: | 4.052 | Durbin-Watson: | 0.990 |
| Prob(Omnibus): | 0.132 | Jarque-Bera (JB): | 1.622 |
| Skew: | -0.651 | Prob(JB): | 0.444 |
| Kurtosis: | 3.949 | Cond. No. | 176. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.605 |
| Model: | OLS | Adj. R-squared: | 0.539 |
| Method: | Least Squares | F-statistic: | 9.179 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00381 |
| Time: | 03:49:57 | Log-Likelihood: | -68.339 |
| No. Observations: | 15 | AIC: | 142.7 |
| Df Residuals: | 12 | BIC: | 144.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 206.6456 | 64.718 | 3.193 | 0.008 | 65.636 347.655 |
| C(dose)[T.1] | 68.2830 | 15.956 | 4.279 | 0.001 | 33.518 103.048 |
| expression | -23.8390 | 10.956 | -2.176 | 0.050 | -47.710 0.032 |
| Omnibus: | 3.779 | Durbin-Watson: | 0.961 |
| Prob(Omnibus): | 0.151 | Jarque-Bera (JB): | 1.443 |
| Skew: | -0.599 | Prob(JB): | 0.486 |
| Kurtosis: | 3.935 | Cond. No. | 63.7 |
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: | 03:49:57 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.075 |
| Method: | Least Squares | F-statistic: | 0.01921 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.892 |
| Time: | 03:49:57 | Log-Likelihood: | -75.289 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 81.5289 | 88.168 | 0.925 | 0.372 | -108.946 272.004 |
| expression | 1.9368 | 13.975 | 0.139 | 0.892 | -28.255 32.128 |
| Omnibus: | 0.450 | Durbin-Watson: | 1.576 |
| Prob(Omnibus): | 0.799 | Jarque-Bera (JB): | 0.519 |
| Skew: | -0.005 | Prob(JB): | 0.771 |
| Kurtosis: | 2.089 | Cond. No. | 56.1 |