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 |
| 3.046 | 0.096 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.803 |
| Model: | OLS | Adj. R-squared: | 0.772 |
| Method: | Least Squares | F-statistic: | 25.76 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.60e-07 |
| Time: | 05:15:45 | Log-Likelihood: | -94.442 |
| No. Observations: | 23 | AIC: | 196.9 |
| Df Residuals: | 19 | BIC: | 201.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 46.8766 | 23.524 | 1.993 | 0.061 | -2.359 96.113 |
| C(dose)[T.1] | -116.6775 | 52.137 | -2.238 | 0.037 | -225.801 -7.554 |
| expression | 1.5180 | 4.774 | 0.318 | 0.754 | -8.473 11.509 |
| expression:C(dose)[T.1] | 32.0491 | 9.975 | 3.213 | 0.005 | 11.172 52.926 |
| Omnibus: | 1.557 | Durbin-Watson: | 2.315 |
| Prob(Omnibus): | 0.459 | Jarque-Bera (JB): | 1.090 |
| Skew: | 0.525 | Prob(JB): | 0.580 |
| Kurtosis: | 2.809 | Cond. No. | 96.8 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.695 |
| Model: | OLS | Adj. R-squared: | 0.665 |
| Method: | Least Squares | F-statistic: | 22.83 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.86e-06 |
| Time: | 05:15:45 | Log-Likelihood: | -99.432 |
| No. Observations: | 23 | AIC: | 204.9 |
| Df Residuals: | 20 | BIC: | 208.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 11.4224 | 25.156 | 0.454 | 0.655 | -41.052 63.897 |
| C(dose)[T.1] | 49.3204 | 8.488 | 5.811 | 0.000 | 31.616 67.025 |
| expression | 8.8585 | 5.075 | 1.745 | 0.096 | -1.728 19.445 |
| Omnibus: | 1.208 | Durbin-Watson: | 1.672 |
| Prob(Omnibus): | 0.547 | Jarque-Bera (JB): | 1.048 |
| Skew: | -0.333 | Prob(JB): | 0.592 |
| Kurtosis: | 2.195 | Cond. No. | 32.9 |
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: | 05:15:45 | 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: | 0.181 |
| Model: | OLS | Adj. R-squared: | 0.142 |
| Method: | Least Squares | F-statistic: | 4.649 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0428 |
| Time: | 05:15:45 | Log-Likelihood: | -110.80 |
| No. Observations: | 23 | AIC: | 225.6 |
| Df Residuals: | 21 | BIC: | 227.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -5.3456 | 39.986 | -0.134 | 0.895 | -88.502 77.810 |
| expression | 16.8549 | 7.817 | 2.156 | 0.043 | 0.599 33.111 |
| Omnibus: | 1.231 | Durbin-Watson: | 2.262 |
| Prob(Omnibus): | 0.540 | Jarque-Bera (JB): | 0.908 |
| Skew: | -0.470 | Prob(JB): | 0.635 |
| Kurtosis: | 2.746 | Cond. No. | 32.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.435 | 0.089 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.593 |
| Model: | OLS | Adj. R-squared: | 0.482 |
| Method: | Least Squares | F-statistic: | 5.341 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0163 |
| Time: | 05:15:45 | Log-Likelihood: | -68.559 |
| No. Observations: | 15 | AIC: | 145.1 |
| Df Residuals: | 11 | BIC: | 148.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 162.8410 | 70.027 | 2.325 | 0.040 | 8.713 316.969 |
| C(dose)[T.1] | 177.2871 | 173.207 | 1.024 | 0.328 | -203.939 558.513 |
| expression | -14.6891 | 10.663 | -1.378 | 0.196 | -38.159 8.781 |
| expression:C(dose)[T.1] | -20.7822 | 27.267 | -0.762 | 0.462 | -80.796 39.232 |
| Omnibus: | 3.836 | Durbin-Watson: | 0.962 |
| Prob(Omnibus): | 0.147 | Jarque-Bera (JB): | 2.416 |
| Skew: | -0.982 | Prob(JB): | 0.299 |
| Kurtosis: | 2.898 | Cond. No. | 189. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.571 |
| Model: | OLS | Adj. R-squared: | 0.500 |
| Method: | Least Squares | F-statistic: | 8.000 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00620 |
| Time: | 05:15:45 | Log-Likelihood: | -68.945 |
| No. Observations: | 15 | AIC: | 143.9 |
| Df Residuals: | 12 | BIC: | 146.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 183.4857 | 63.439 | 2.892 | 0.014 | 45.265 321.707 |
| C(dose)[T.1] | 45.7208 | 14.005 | 3.265 | 0.007 | 15.208 76.234 |
| expression | -17.8674 | 9.641 | -1.853 | 0.089 | -38.874 3.139 |
| Omnibus: | 3.320 | Durbin-Watson: | 1.032 |
| Prob(Omnibus): | 0.190 | Jarque-Bera (JB): | 2.114 |
| Skew: | -0.915 | Prob(JB): | 0.347 |
| Kurtosis: | 2.817 | Cond. No. | 60.6 |
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: | 05:15:45 | 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: | 0.191 |
| Model: | OLS | Adj. R-squared: | 0.129 |
| Method: | Least Squares | F-statistic: | 3.065 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.104 |
| Time: | 05:15:45 | Log-Likelihood: | -73.712 |
| No. Observations: | 15 | AIC: | 151.4 |
| Df Residuals: | 13 | BIC: | 152.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 234.8116 | 81.140 | 2.894 | 0.013 | 59.520 410.103 |
| expression | -22.0824 | 12.614 | -1.751 | 0.104 | -49.333 5.168 |
| Omnibus: | 3.683 | Durbin-Watson: | 2.099 |
| Prob(Omnibus): | 0.159 | Jarque-Bera (JB): | 1.270 |
| Skew: | 0.155 | Prob(JB): | 0.530 |
| Kurtosis: | 1.609 | Cond. No. | 58.5 |