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 |
| 5.523 | 0.029 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.795 |
| Model: | OLS | Adj. R-squared: | 0.762 |
| Method: | Least Squares | F-statistic: | 24.50 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.62e-07 |
| Time: | 04:46:28 | Log-Likelihood: | -94.903 |
| No. Observations: | 23 | AIC: | 197.8 |
| Df Residuals: | 19 | BIC: | 202.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 46.7840 | 25.367 | 1.844 | 0.081 | -6.310 99.878 |
| C(dose)[T.1] | -47.8061 | 39.366 | -1.214 | 0.239 | -130.200 34.588 |
| expression | 1.5253 | 5.119 | 0.298 | 0.769 | -9.189 12.239 |
| expression:C(dose)[T.1] | 19.5649 | 7.711 | 2.537 | 0.020 | 3.425 35.705 |
| Omnibus: | 1.048 | Durbin-Watson: | 2.220 |
| Prob(Omnibus): | 0.592 | Jarque-Bera (JB): | 0.495 |
| Skew: | 0.359 | Prob(JB): | 0.781 |
| Kurtosis: | 3.013 | Cond. No. | 76.3 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.725 |
| Model: | OLS | Adj. R-squared: | 0.697 |
| Method: | Least Squares | F-statistic: | 26.36 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.47e-06 |
| Time: | 04:46:29 | Log-Likelihood: | -98.259 |
| No. Observations: | 23 | AIC: | 202.5 |
| Df Residuals: | 20 | BIC: | 205.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 4.8203 | 21.691 | 0.222 | 0.826 | -40.426 50.067 |
| C(dose)[T.1] | 50.4937 | 7.857 | 6.427 | 0.000 | 34.104 66.883 |
| expression | 10.1464 | 4.318 | 2.350 | 0.029 | 1.140 19.153 |
| Omnibus: | 0.633 | Durbin-Watson: | 1.719 |
| Prob(Omnibus): | 0.729 | Jarque-Bera (JB): | 0.707 |
| Skew: | -0.292 | Prob(JB): | 0.702 |
| Kurtosis: | 2.370 | Cond. No. | 29.6 |
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: | 04:46:29 | 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: | 0.157 |
| Model: | OLS | Adj. R-squared: | 0.117 |
| Method: | Least Squares | F-statistic: | 3.914 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0612 |
| Time: | 04:46:29 | Log-Likelihood: | -111.14 |
| No. Observations: | 23 | AIC: | 226.3 |
| Df Residuals: | 21 | BIC: | 228.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 7.5977 | 37.052 | 0.205 | 0.840 | -69.457 84.652 |
| expression | 14.4194 | 7.289 | 1.978 | 0.061 | -0.738 29.577 |
| Omnibus: | 0.774 | Durbin-Watson: | 2.504 |
| Prob(Omnibus): | 0.679 | Jarque-Bera (JB): | 0.800 |
| Skew: | -0.351 | Prob(JB): | 0.670 |
| Kurtosis: | 2.414 | Cond. No. | 29.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.601 | 0.133 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.547 |
| Model: | OLS | Adj. R-squared: | 0.423 |
| Method: | Least Squares | F-statistic: | 4.427 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0284 |
| Time: | 04:46:29 | Log-Likelihood: | -69.362 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 11 | BIC: | 149.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 162.5517 | 66.237 | 2.454 | 0.032 | 16.764 308.339 |
| C(dose)[T.1] | 46.4310 | 191.615 | 0.242 | 0.813 | -375.312 468.174 |
| expression | -14.8764 | 10.218 | -1.456 | 0.173 | -37.366 7.614 |
| expression:C(dose)[T.1] | 0.1819 | 30.334 | 0.006 | 0.995 | -66.583 66.947 |
| Omnibus: | 4.375 | Durbin-Watson: | 1.022 |
| Prob(Omnibus): | 0.112 | Jarque-Bera (JB): | 2.463 |
| Skew: | -0.987 | Prob(JB): | 0.292 |
| Kurtosis: | 3.207 | Cond. No. | 196. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.547 |
| Model: | OLS | Adj. R-squared: | 0.471 |
| Method: | Least Squares | F-statistic: | 7.244 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00865 |
| Time: | 04:46:29 | Log-Likelihood: | -69.362 |
| No. Observations: | 15 | AIC: | 144.7 |
| Df Residuals: | 12 | BIC: | 146.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 162.4198 | 59.814 | 2.715 | 0.019 | 32.095 292.744 |
| C(dose)[T.1] | 47.5765 | 14.304 | 3.326 | 0.006 | 16.410 78.743 |
| expression | -14.8557 | 9.211 | -1.613 | 0.133 | -34.926 5.214 |
| Omnibus: | 4.389 | Durbin-Watson: | 1.020 |
| Prob(Omnibus): | 0.111 | Jarque-Bera (JB): | 2.471 |
| Skew: | -0.989 | Prob(JB): | 0.291 |
| Kurtosis: | 3.210 | Cond. No. | 55.1 |
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: | 04:46:29 | 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: | 0.129 |
| Model: | OLS | Adj. R-squared: | 0.062 |
| Method: | Least Squares | F-statistic: | 1.931 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.188 |
| Time: | 04:46:29 | Log-Likelihood: | -74.261 |
| No. Observations: | 15 | AIC: | 152.5 |
| Df Residuals: | 13 | BIC: | 153.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 201.4252 | 78.122 | 2.578 | 0.023 | 32.653 370.198 |
| expression | -17.0071 | 12.239 | -1.390 | 0.188 | -43.447 9.433 |
| Omnibus: | 4.414 | Durbin-Watson: | 2.015 |
| Prob(Omnibus): | 0.110 | Jarque-Bera (JB): | 1.490 |
| Skew: | 0.293 | Prob(JB): | 0.475 |
| Kurtosis: | 1.572 | Cond. No. | 53.9 |