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 |
| 0.338 | 0.567 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.702 |
| Model: | OLS | Adj. R-squared: | 0.655 |
| Method: | Least Squares | F-statistic: | 14.92 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.13e-05 |
| Time: | 03:48:10 | Log-Likelihood: | -99.184 |
| No. Observations: | 23 | AIC: | 206.4 |
| Df Residuals: | 19 | BIC: | 210.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 32.0142 | 71.665 | 0.447 | 0.660 | -117.982 182.010 |
| C(dose)[T.1] | 303.0332 | 144.826 | 2.092 | 0.050 | -0.091 606.157 |
| expression | 2.8627 | 9.214 | 0.311 | 0.759 | -16.423 22.148 |
| expression:C(dose)[T.1] | -32.7755 | 18.923 | -1.732 | 0.099 | -72.383 6.832 |
| Omnibus: | 1.743 | Durbin-Watson: | 1.971 |
| Prob(Omnibus): | 0.418 | Jarque-Bera (JB): | 1.506 |
| Skew: | 0.570 | Prob(JB): | 0.471 |
| Kurtosis: | 2.480 | Cond. No. | 320. |
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: | 18.98 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.40e-05 |
| Time: | 03:48:10 | 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 | 92.2578 | 65.716 | 1.404 | 0.176 | -44.823 229.339 |
| C(dose)[T.1] | 52.6141 | 8.785 | 5.989 | 0.000 | 34.289 70.939 |
| expression | -4.9078 | 8.441 | -0.581 | 0.567 | -22.515 12.699 |
| Omnibus: | 0.630 | Durbin-Watson: | 2.085 |
| Prob(Omnibus): | 0.730 | Jarque-Bera (JB): | 0.680 |
| Skew: | 0.191 | Prob(JB): | 0.712 |
| Kurtosis: | 2.249 | Cond. No. | 119. |
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: | 03:48:10 | 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: | 0.036 |
| Model: | OLS | Adj. R-squared: | -0.010 |
| Method: | Least Squares | F-statistic: | 0.7835 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.386 |
| Time: | 03:48:10 | Log-Likelihood: | -112.68 |
| No. Observations: | 23 | AIC: | 229.4 |
| Df Residuals: | 21 | BIC: | 231.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 172.3943 | 104.942 | 1.643 | 0.115 | -45.844 390.633 |
| expression | -12.0636 | 13.629 | -0.885 | 0.386 | -40.406 16.279 |
| Omnibus: | 2.470 | Durbin-Watson: | 2.704 |
| Prob(Omnibus): | 0.291 | Jarque-Bera (JB): | 1.250 |
| Skew: | 0.173 | Prob(JB): | 0.535 |
| Kurtosis: | 1.912 | Cond. No. | 116. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.823 | 0.382 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.498 |
| Model: | OLS | Adj. R-squared: | 0.361 |
| Method: | Least Squares | F-statistic: | 3.634 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0484 |
| Time: | 03:48:11 | Log-Likelihood: | -70.135 |
| No. Observations: | 15 | AIC: | 148.3 |
| Df Residuals: | 11 | BIC: | 151.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 48.7084 | 123.639 | 0.394 | 0.701 | -223.418 320.835 |
| C(dose)[T.1] | -41.6866 | 163.820 | -0.254 | 0.804 | -402.252 318.878 |
| expression | 2.7780 | 18.269 | 0.152 | 0.882 | -37.431 42.987 |
| expression:C(dose)[T.1] | 13.0748 | 23.937 | 0.546 | 0.596 | -39.610 65.760 |
| Omnibus: | 1.937 | Durbin-Watson: | 0.760 |
| Prob(Omnibus): | 0.380 | Jarque-Bera (JB): | 1.172 |
| Skew: | -0.388 | Prob(JB): | 0.557 |
| Kurtosis: | 1.872 | Cond. No. | 202. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.484 |
| Model: | OLS | Adj. R-squared: | 0.398 |
| Method: | Least Squares | F-statistic: | 5.632 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0188 |
| Time: | 03:48:11 | Log-Likelihood: | -70.335 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 12 | BIC: | 148.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -2.6114 | 77.983 | -0.033 | 0.974 | -172.522 167.300 |
| C(dose)[T.1] | 47.3765 | 15.357 | 3.085 | 0.009 | 13.915 80.838 |
| expression | 10.3937 | 11.454 | 0.907 | 0.382 | -14.563 35.350 |
| Omnibus: | 2.299 | Durbin-Watson: | 0.765 |
| Prob(Omnibus): | 0.317 | Jarque-Bera (JB): | 1.249 |
| Skew: | -0.383 | Prob(JB): | 0.535 |
| Kurtosis: | 1.811 | Cond. No. | 72.2 |
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: | 03:48:11 | 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: | 0.075 |
| Model: | OLS | Adj. R-squared: | 0.004 |
| Method: | Least Squares | F-statistic: | 1.055 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.323 |
| Time: | 03:48:11 | Log-Likelihood: | -74.715 |
| No. Observations: | 15 | AIC: | 153.4 |
| Df Residuals: | 13 | BIC: | 154.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -8.8723 | 100.293 | -0.088 | 0.931 | -225.542 207.797 |
| expression | 15.0085 | 14.610 | 1.027 | 0.323 | -16.554 46.571 |
| Omnibus: | 1.233 | Durbin-Watson: | 1.449 |
| Prob(Omnibus): | 0.540 | Jarque-Bera (JB): | 0.826 |
| Skew: | 0.199 | Prob(JB): | 0.662 |
| Kurtosis: | 1.922 | Cond. No. | 71.9 |