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 |
| 1.811 | 0.193 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.752 |
| Model: | OLS | Adj. R-squared: | 0.713 |
| Method: | Least Squares | F-statistic: | 19.23 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.58e-06 |
| Time: | 03:59:53 | Log-Likelihood: | -97.060 |
| No. Observations: | 23 | AIC: | 202.1 |
| Df Residuals: | 19 | BIC: | 206.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -72.6926 | 238.617 | -0.305 | 0.764 | -572.124 426.739 |
| C(dose)[T.1] | 905.6296 | 354.901 | 2.552 | 0.019 | 162.812 1648.447 |
| expression | 12.6509 | 23.782 | 0.532 | 0.601 | -37.126 62.428 |
| expression:C(dose)[T.1] | -83.0585 | 34.864 | -2.382 | 0.028 | -156.030 -10.087 |
| Omnibus: | 0.175 | Durbin-Watson: | 2.021 |
| Prob(Omnibus): | 0.916 | Jarque-Bera (JB): | 0.047 |
| Skew: | 0.081 | Prob(JB): | 0.977 |
| Kurtosis: | 2.847 | Cond. No. | 1.23e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.678 |
| Model: | OLS | Adj. R-squared: | 0.646 |
| Method: | Least Squares | F-statistic: | 21.08 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.19e-05 |
| Time: | 03:59:53 | Log-Likelihood: | -100.07 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 20 | BIC: | 209.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 314.9870 | 193.848 | 1.625 | 0.120 | -89.372 719.346 |
| C(dose)[T.1] | 60.4016 | 9.903 | 6.099 | 0.000 | 39.744 81.060 |
| expression | -25.9973 | 19.316 | -1.346 | 0.193 | -66.290 14.296 |
| Omnibus: | 0.216 | Durbin-Watson: | 1.918 |
| Prob(Omnibus): | 0.898 | Jarque-Bera (JB): | 0.405 |
| Skew: | -0.144 | Prob(JB): | 0.817 |
| Kurtosis: | 2.418 | Cond. No. | 475. |
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: | 03:59:53 | 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: | 0.080 |
| Model: | OLS | Adj. R-squared: | 0.036 |
| Method: | Least Squares | F-statistic: | 1.818 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.192 |
| Time: | 03:59:53 | Log-Likelihood: | -112.15 |
| No. Observations: | 23 | AIC: | 228.3 |
| Df Residuals: | 21 | BIC: | 230.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -290.6130 | 274.769 | -1.058 | 0.302 | -862.027 280.801 |
| expression | 36.4464 | 27.033 | 1.348 | 0.192 | -19.772 92.665 |
| Omnibus: | 2.631 | Durbin-Watson: | 2.318 |
| Prob(Omnibus): | 0.268 | Jarque-Bera (JB): | 1.891 |
| Skew: | 0.525 | Prob(JB): | 0.388 |
| Kurtosis: | 2.067 | Cond. No. | 407. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.148 | 0.707 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.530 |
| Model: | OLS | Adj. R-squared: | 0.401 |
| Method: | Least Squares | F-statistic: | 4.127 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0346 |
| Time: | 03:59:53 | Log-Likelihood: | -69.645 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 11 | BIC: | 150.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -477.1217 | 561.093 | -0.850 | 0.413 | -1712.079 757.836 |
| C(dose)[T.1] | 1679.1708 | 1248.913 | 1.345 | 0.206 | -1069.668 4428.009 |
| expression | 56.7396 | 58.452 | 0.971 | 0.353 | -71.912 185.391 |
| expression:C(dose)[T.1] | -162.8010 | 123.703 | -1.316 | 0.215 | -435.070 109.468 |
| Omnibus: | 2.662 | Durbin-Watson: | 0.819 |
| Prob(Omnibus): | 0.264 | Jarque-Bera (JB): | 1.278 |
| Skew: | -0.346 | Prob(JB): | 0.528 |
| Kurtosis: | 1.749 | Cond. No. | 2.00e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.365 |
| Method: | Least Squares | F-statistic: | 5.019 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0261 |
| Time: | 03:59:53 | Log-Likelihood: | -70.741 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -128.2694 | 509.392 | -0.252 | 0.805 | -1238.139 981.600 |
| C(dose)[T.1] | 36.2165 | 37.224 | 0.973 | 0.350 | -44.888 117.321 |
| expression | 20.3908 | 53.063 | 0.384 | 0.707 | -95.223 136.005 |
| Omnibus: | 3.221 | Durbin-Watson: | 0.699 |
| Prob(Omnibus): | 0.200 | Jarque-Bera (JB): | 1.961 |
| Skew: | -0.884 | Prob(JB): | 0.375 |
| Kurtosis: | 2.887 | Cond. No. | 658. |
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: | 03:59:53 | 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: | 0.413 |
| Model: | OLS | Adj. R-squared: | 0.367 |
| Method: | Least Squares | F-statistic: | 9.128 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00983 |
| Time: | 03:59:53 | Log-Likelihood: | -71.311 |
| No. Observations: | 15 | AIC: | 146.6 |
| Df Residuals: | 13 | BIC: | 148.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -574.4572 | 221.273 | -2.596 | 0.022 | -1052.489 -96.426 |
| expression | 67.2369 | 22.254 | 3.021 | 0.010 | 19.160 115.314 |
| Omnibus: | 3.962 | Durbin-Watson: | 0.518 |
| Prob(Omnibus): | 0.138 | Jarque-Bera (JB): | 2.084 |
| Skew: | -0.903 | Prob(JB): | 0.353 |
| Kurtosis: | 3.264 | Cond. No. | 285. |