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 |
| 0.772 | 0.390 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.673 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 13.02 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.47e-05 |
| Time: | 03:41:20 | Log-Likelihood: | -100.26 |
| No. Observations: | 23 | AIC: | 208.5 |
| Df Residuals: | 19 | BIC: | 213.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 55.8700 | 63.446 | 0.881 | 0.390 | -76.924 188.664 |
| C(dose)[T.1] | 120.1087 | 86.475 | 1.389 | 0.181 | -60.886 301.104 |
| expression | -0.2660 | 10.111 | -0.026 | 0.979 | -21.429 20.897 |
| expression:C(dose)[T.1] | -10.9079 | 13.901 | -0.785 | 0.442 | -40.003 18.187 |
| Omnibus: | 0.664 | Durbin-Watson: | 1.803 |
| Prob(Omnibus): | 0.717 | Jarque-Bera (JB): | 0.683 |
| Skew: | 0.157 | Prob(JB): | 0.711 |
| Kurtosis: | 2.216 | Cond. No. | 167. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 19.59 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.94e-05 |
| Time: | 03:41:20 | Log-Likelihood: | -100.63 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 91.9199 | 43.335 | 2.121 | 0.047 | 1.524 182.316 |
| C(dose)[T.1] | 52.5996 | 8.646 | 6.084 | 0.000 | 34.564 70.635 |
| expression | -6.0372 | 6.872 | -0.879 | 0.390 | -20.371 8.297 |
| Omnibus: | 0.588 | Durbin-Watson: | 1.844 |
| Prob(Omnibus): | 0.745 | Jarque-Bera (JB): | 0.647 |
| Skew: | 0.159 | Prob(JB): | 0.724 |
| Kurtosis: | 2.242 | Cond. No. | 64.5 |
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: | 03:41:20 | 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: | 0.037 |
| Model: | OLS | Adj. R-squared: | -0.009 |
| Method: | Least Squares | F-statistic: | 0.8027 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.380 |
| Time: | 03:41:20 | Log-Likelihood: | -112.67 |
| 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 | 142.1922 | 70.091 | 2.029 | 0.055 | -3.570 287.954 |
| expression | -10.0960 | 11.269 | -0.896 | 0.380 | -33.531 13.339 |
| Omnibus: | 3.325 | Durbin-Watson: | 2.295 |
| Prob(Omnibus): | 0.190 | Jarque-Bera (JB): | 1.486 |
| Skew: | 0.228 | Prob(JB): | 0.476 |
| Kurtosis: | 1.841 | Cond. No. | 63.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.000 | 0.992 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.463 |
| Model: | OLS | Adj. R-squared: | 0.317 |
| Method: | Least Squares | F-statistic: | 3.163 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0680 |
| Time: | 03:41:20 | Log-Likelihood: | -70.635 |
| No. Observations: | 15 | AIC: | 149.3 |
| Df Residuals: | 11 | BIC: | 152.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 95.3094 | 107.740 | 0.885 | 0.395 | -141.825 332.444 |
| C(dose)[T.1] | -64.6905 | 210.880 | -0.307 | 0.765 | -528.834 399.453 |
| expression | -4.2795 | 16.437 | -0.260 | 0.799 | -40.457 31.898 |
| expression:C(dose)[T.1] | 17.9425 | 33.112 | 0.542 | 0.599 | -54.937 90.822 |
| Omnibus: | 2.428 | Durbin-Watson: | 0.833 |
| Prob(Omnibus): | 0.297 | Jarque-Bera (JB): | 1.616 |
| Skew: | -0.787 | Prob(JB): | 0.446 |
| Kurtosis: | 2.668 | Cond. No. | 205. |
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: | 4.885 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0281 |
| Time: | 03:41:20 | Log-Likelihood: | -70.833 |
| 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 | 66.5044 | 90.913 | 0.732 | 0.479 | -131.578 264.587 |
| C(dose)[T.1] | 49.2277 | 16.032 | 3.071 | 0.010 | 14.297 84.158 |
| expression | 0.1418 | 13.842 | 0.010 | 0.992 | -30.018 30.302 |
| Omnibus: | 2.729 | Durbin-Watson: | 0.808 |
| Prob(Omnibus): | 0.256 | Jarque-Bera (JB): | 1.877 |
| Skew: | -0.846 | Prob(JB): | 0.391 |
| Kurtosis: | 2.623 | Cond. No. | 76.5 |
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: | 03:41:20 | 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: | 0.016 |
| Model: | OLS | Adj. R-squared: | -0.060 |
| Method: | Least Squares | F-statistic: | 0.2069 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.657 |
| Time: | 03:41:20 | Log-Likelihood: | -75.182 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 144.4443 | 112.080 | 1.289 | 0.220 | -97.691 386.579 |
| expression | -7.9370 | 17.448 | -0.455 | 0.657 | -45.632 29.758 |
| Omnibus: | 1.224 | Durbin-Watson: | 1.598 |
| Prob(Omnibus): | 0.542 | Jarque-Bera (JB): | 0.789 |
| Skew: | 0.121 | Prob(JB): | 0.674 |
| Kurtosis: | 1.903 | Cond. No. | 73.1 |