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 |
| 0.344 | 0.564 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.601 |
| Method: | Least Squares | F-statistic: | 12.07 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000119 |
| Time: | 03:41:08 | Log-Likelihood: | -100.84 |
| No. Observations: | 23 | AIC: | 209.7 |
| Df Residuals: | 19 | BIC: | 214.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 77.1127 | 71.012 | 1.086 | 0.291 | -71.516 225.742 |
| C(dose)[T.1] | 81.3824 | 121.770 | 0.668 | 0.512 | -173.485 336.250 |
| expression | -3.3939 | 10.483 | -0.324 | 0.750 | -25.334 18.546 |
| expression:C(dose)[T.1] | -3.5467 | 17.050 | -0.208 | 0.837 | -39.233 32.139 |
| Omnibus: | 0.466 | Durbin-Watson: | 1.912 |
| Prob(Omnibus): | 0.792 | Jarque-Bera (JB): | 0.570 |
| Skew: | 0.103 | Prob(JB): | 0.752 |
| Kurtosis: | 2.257 | Cond. No. | 243. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.655 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 18.99 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.39e-05 |
| Time: | 03:41:08 | Log-Likelihood: | -100.87 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 86.1603 | 54.774 | 1.573 | 0.131 | -28.096 200.417 |
| C(dose)[T.1] | 56.1403 | 9.921 | 5.659 | 0.000 | 35.446 76.835 |
| expression | -4.7345 | 8.067 | -0.587 | 0.564 | -21.562 12.093 |
| Omnibus: | 0.301 | Durbin-Watson: | 1.888 |
| Prob(Omnibus): | 0.860 | Jarque-Bera (JB): | 0.472 |
| Skew: | 0.046 | Prob(JB): | 0.790 |
| Kurtosis: | 2.304 | Cond. No. | 91.3 |
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: | 03:41:08 | 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: | 0.103 |
| Model: | OLS | Adj. R-squared: | 0.060 |
| Method: | Least Squares | F-statistic: | 2.401 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.136 |
| Time: | 03:41:08 | Log-Likelihood: | -111.86 |
| No. Observations: | 23 | AIC: | 227.7 |
| Df Residuals: | 21 | BIC: | 230.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -41.5424 | 78.554 | -0.529 | 0.602 | -204.904 121.819 |
| expression | 17.2443 | 11.129 | 1.550 | 0.136 | -5.899 40.388 |
| Omnibus: | 3.515 | Durbin-Watson: | 2.507 |
| Prob(Omnibus): | 0.172 | Jarque-Bera (JB): | 1.453 |
| Skew: | 0.166 | Prob(JB): | 0.484 |
| Kurtosis: | 1.814 | Cond. No. | 82.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.411 | 0.534 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.571 |
| Model: | OLS | Adj. R-squared: | 0.454 |
| Method: | Least Squares | F-statistic: | 4.873 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0215 |
| Time: | 03:41:09 | Log-Likelihood: | -68.959 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 11 | BIC: | 148.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 171.6946 | 178.183 | 0.964 | 0.356 | -220.483 563.872 |
| C(dose)[T.1] | -412.2946 | 278.896 | -1.478 | 0.167 | -1026.141 201.552 |
| expression | -17.0996 | 29.170 | -0.586 | 0.570 | -81.303 47.104 |
| expression:C(dose)[T.1] | 70.8280 | 43.475 | 1.629 | 0.132 | -24.859 166.515 |
| Omnibus: | 0.950 | Durbin-Watson: | 1.629 |
| Prob(Omnibus): | 0.622 | Jarque-Bera (JB): | 0.731 |
| Skew: | -0.486 | Prob(JB): | 0.694 |
| Kurtosis: | 2.525 | Cond. No. | 329. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.467 |
| Model: | OLS | Adj. R-squared: | 0.378 |
| Method: | Least Squares | F-statistic: | 5.257 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0229 |
| Time: | 03:41:09 | Log-Likelihood: | -70.581 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -22.7379 | 141.136 | -0.161 | 0.875 | -330.247 284.771 |
| C(dose)[T.1] | 41.0462 | 20.031 | 2.049 | 0.063 | -2.598 84.690 |
| expression | 14.7873 | 23.072 | 0.641 | 0.534 | -35.482 65.057 |
| Omnibus: | 2.003 | Durbin-Watson: | 0.786 |
| Prob(Omnibus): | 0.367 | Jarque-Bera (JB): | 1.564 |
| Skew: | -0.694 | Prob(JB): | 0.458 |
| Kurtosis: | 2.243 | Cond. No. | 121. |
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: | 03:41:09 | 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: | 0.281 |
| Model: | OLS | Adj. R-squared: | 0.225 |
| Method: | Least Squares | F-statistic: | 5.069 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0423 |
| Time: | 03:41:09 | Log-Likelihood: | -72.831 |
| No. Observations: | 15 | AIC: | 149.7 |
| Df Residuals: | 13 | BIC: | 151.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -192.6771 | 127.478 | -1.511 | 0.155 | -468.077 82.723 |
| expression | 44.8006 | 19.899 | 2.251 | 0.042 | 1.811 87.790 |
| Omnibus: | 0.642 | Durbin-Watson: | 1.298 |
| Prob(Omnibus): | 0.726 | Jarque-Bera (JB): | 0.652 |
| Skew: | -0.378 | Prob(JB): | 0.722 |
| Kurtosis: | 2.313 | Cond. No. | 97.0 |