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 |
| 0.081 | 0.779 | 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: | 13.01 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.49e-05 |
| Time: | 05:24:00 | 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 | 180.1915 | 117.402 | 1.535 | 0.141 | -65.533 425.916 |
| C(dose)[T.1] | -115.5571 | 149.944 | -0.771 | 0.450 | -429.394 198.280 |
| expression | -27.8292 | 25.900 | -1.075 | 0.296 | -82.038 26.379 |
| expression:C(dose)[T.1] | 36.9638 | 32.607 | 1.134 | 0.271 | -31.284 105.212 |
| Omnibus: | 0.047 | Durbin-Watson: | 1.714 |
| Prob(Omnibus): | 0.977 | Jarque-Bera (JB): | 0.255 |
| Skew: | -0.055 | Prob(JB): | 0.881 |
| Kurtosis: | 2.497 | Cond. No. | 230. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.61 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.72e-05 |
| Time: | 05:24:00 | Log-Likelihood: | -101.02 |
| 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 | 74.6212 | 71.994 | 1.036 | 0.312 | -75.556 224.798 |
| C(dose)[T.1] | 54.1065 | 9.160 | 5.907 | 0.000 | 34.998 73.215 |
| expression | -4.5091 | 15.847 | -0.285 | 0.779 | -37.565 28.547 |
| Omnibus: | 0.206 | Durbin-Watson: | 1.911 |
| Prob(Omnibus): | 0.902 | Jarque-Bera (JB): | 0.409 |
| Skew: | 0.071 | Prob(JB): | 0.815 |
| Kurtosis: | 2.362 | Cond. No. | 80.1 |
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: | 05:24:00 | 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: | 0.041 |
| Model: | OLS | Adj. R-squared: | -0.005 |
| Method: | Least Squares | F-statistic: | 0.8922 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.356 |
| Time: | 05:24:00 | Log-Likelihood: | -112.63 |
| No. Observations: | 23 | AIC: | 229.3 |
| Df Residuals: | 21 | BIC: | 231.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -26.8387 | 113.031 | -0.237 | 0.815 | -261.900 208.222 |
| expression | 23.1210 | 24.478 | 0.945 | 0.356 | -27.784 74.026 |
| Omnibus: | 3.014 | Durbin-Watson: | 2.297 |
| Prob(Omnibus): | 0.222 | Jarque-Bera (JB): | 1.353 |
| Skew: | 0.161 | Prob(JB): | 0.508 |
| Kurtosis: | 1.856 | Cond. No. | 77.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.179 | 0.166 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.534 |
| Model: | OLS | Adj. R-squared: | 0.407 |
| Method: | Least Squares | F-statistic: | 4.209 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0328 |
| Time: | 05:24:01 | Log-Likelihood: | -69.567 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 11 | BIC: | 150.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -116.6010 | 193.507 | -0.603 | 0.559 | -542.508 309.306 |
| C(dose)[T.1] | 85.2074 | 239.128 | 0.356 | 0.728 | -441.110 611.525 |
| expression | 34.1016 | 35.800 | 0.953 | 0.361 | -44.693 112.896 |
| expression:C(dose)[T.1] | -6.5406 | 44.297 | -0.148 | 0.885 | -104.037 90.956 |
| Omnibus: | 1.098 | Durbin-Watson: | 1.498 |
| Prob(Omnibus): | 0.578 | Jarque-Bera (JB): | 0.941 |
| Skew: | -0.432 | Prob(JB): | 0.625 |
| Kurtosis: | 2.130 | Cond. No. | 252. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.533 |
| Model: | OLS | Adj. R-squared: | 0.456 |
| Method: | Least Squares | F-statistic: | 6.862 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0103 |
| Time: | 05:24:01 | Log-Likelihood: | -69.581 |
| No. Observations: | 15 | AIC: | 145.2 |
| Df Residuals: | 12 | BIC: | 147.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -93.5474 | 109.555 | -0.854 | 0.410 | -332.247 145.152 |
| C(dose)[T.1] | 49.9701 | 14.489 | 3.449 | 0.005 | 18.401 81.539 |
| expression | 29.8297 | 20.206 | 1.476 | 0.166 | -14.196 73.855 |
| Omnibus: | 0.967 | Durbin-Watson: | 1.466 |
| Prob(Omnibus): | 0.617 | Jarque-Bera (JB): | 0.868 |
| Skew: | -0.417 | Prob(JB): | 0.648 |
| Kurtosis: | 2.168 | Cond. No. | 85.0 |
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: | 05:24:01 | 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: | 0.071 |
| Model: | OLS | Adj. R-squared: | -0.000 |
| Method: | Least Squares | F-statistic: | 0.9951 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.337 |
| Time: | 05:24:01 | Log-Likelihood: | -74.747 |
| No. Observations: | 15 | AIC: | 153.5 |
| Df Residuals: | 13 | BIC: | 154.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -53.3298 | 147.684 | -0.361 | 0.724 | -372.381 265.721 |
| expression | 27.3092 | 27.376 | 0.998 | 0.337 | -31.834 86.453 |
| Omnibus: | 0.436 | Durbin-Watson: | 1.946 |
| Prob(Omnibus): | 0.804 | Jarque-Bera (JB): | 0.538 |
| Skew: | 0.269 | Prob(JB): | 0.764 |
| Kurtosis: | 2.244 | Cond. No. | 84.1 |