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 |
| 0.108 | 0.746 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.608 |
| Method: | Least Squares | F-statistic: | 12.36 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000103 |
| Time: | 04:02:17 | Log-Likelihood: | -100.66 |
| No. Observations: | 23 | AIC: | 209.3 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 64.6240 | 38.704 | 1.670 | 0.111 | -16.385 145.633 |
| C(dose)[T.1] | 8.9222 | 58.791 | 0.152 | 0.881 | -114.130 131.974 |
| expression | -1.9436 | 7.132 | -0.273 | 0.788 | -16.870 12.983 |
| expression:C(dose)[T.1] | 7.9702 | 10.541 | 0.756 | 0.459 | -14.092 30.033 |
| Omnibus: | 0.100 | Durbin-Watson: | 1.980 |
| Prob(Omnibus): | 0.951 | Jarque-Bera (JB): | 0.307 |
| Skew: | -0.093 | Prob(JB): | 0.858 |
| Kurtosis: | 2.465 | Cond. No. | 97.2 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.65 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.69e-05 |
| Time: | 04:02:17 | Log-Likelihood: | -101.00 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 45.0731 | 28.490 | 1.582 | 0.129 | -14.356 104.502 |
| C(dose)[T.1] | 52.8554 | 8.869 | 5.960 | 0.000 | 34.356 71.355 |
| expression | 1.7047 | 5.195 | 0.328 | 0.746 | -9.132 12.541 |
| Omnibus: | 0.241 | Durbin-Watson: | 1.912 |
| Prob(Omnibus): | 0.886 | Jarque-Bera (JB): | 0.434 |
| Skew: | 0.083 | Prob(JB): | 0.805 |
| Kurtosis: | 2.347 | Cond. No. | 37.5 |
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: | 04:02:17 | 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: | 0.031 |
| Model: | OLS | Adj. R-squared: | -0.015 |
| Method: | Least Squares | F-statistic: | 0.6721 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.422 |
| Time: | 04:02:17 | Log-Likelihood: | -112.74 |
| No. Observations: | 23 | AIC: | 229.5 |
| Df Residuals: | 21 | BIC: | 231.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 42.1959 | 46.317 | 0.911 | 0.373 | -54.125 138.517 |
| expression | 6.8294 | 8.331 | 0.820 | 0.422 | -10.495 24.154 |
| Omnibus: | 4.254 | Durbin-Watson: | 2.559 |
| Prob(Omnibus): | 0.119 | Jarque-Bera (JB): | 1.622 |
| Skew: | 0.206 | Prob(JB): | 0.444 |
| Kurtosis: | 1.766 | Cond. No. | 37.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.122 | 0.310 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.500 |
| Model: | OLS | Adj. R-squared: | 0.364 |
| Method: | Least Squares | F-statistic: | 3.665 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0473 |
| Time: | 04:02:17 | Log-Likelihood: | -70.103 |
| No. Observations: | 15 | AIC: | 148.2 |
| Df Residuals: | 11 | BIC: | 151.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 128.0864 | 63.927 | 2.004 | 0.070 | -12.615 268.788 |
| C(dose)[T.1] | 16.1536 | 90.077 | 0.179 | 0.861 | -182.105 214.412 |
| expression | -15.6658 | 16.244 | -0.964 | 0.356 | -51.418 20.086 |
| expression:C(dose)[T.1] | 7.3669 | 24.844 | 0.297 | 0.772 | -47.315 62.049 |
| Omnibus: | 2.663 | Durbin-Watson: | 0.950 |
| Prob(Omnibus): | 0.264 | Jarque-Bera (JB): | 1.950 |
| Skew: | -0.843 | Prob(JB): | 0.377 |
| Kurtosis: | 2.472 | Cond. No. | 58.1 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.496 |
| Model: | OLS | Adj. R-squared: | 0.412 |
| Method: | Least Squares | F-statistic: | 5.903 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0164 |
| Time: | 04:02:17 | Log-Likelihood: | -70.162 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 12 | BIC: | 148.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 115.8928 | 47.048 | 2.463 | 0.030 | 13.385 218.401 |
| C(dose)[T.1] | 42.3818 | 16.368 | 2.589 | 0.024 | 6.718 78.045 |
| expression | -12.5166 | 11.814 | -1.059 | 0.310 | -38.258 13.225 |
| Omnibus: | 2.437 | Durbin-Watson: | 0.841 |
| Prob(Omnibus): | 0.296 | Jarque-Bera (JB): | 1.833 |
| Skew: | -0.800 | Prob(JB): | 0.400 |
| Kurtosis: | 2.390 | Cond. No. | 25.0 |
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: | 04:02:17 | 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: | 0.214 |
| Model: | OLS | Adj. R-squared: | 0.154 |
| Method: | Least Squares | F-statistic: | 3.546 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0823 |
| Time: | 04:02:17 | Log-Likelihood: | -73.491 |
| No. Observations: | 15 | AIC: | 151.0 |
| Df Residuals: | 13 | BIC: | 152.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 181.5527 | 47.534 | 3.819 | 0.002 | 78.861 284.244 |
| expression | -24.5380 | 13.031 | -1.883 | 0.082 | -52.690 3.614 |
| Omnibus: | 0.866 | Durbin-Watson: | 1.687 |
| Prob(Omnibus): | 0.649 | Jarque-Bera (JB): | 0.767 |
| Skew: | 0.305 | Prob(JB): | 0.682 |
| Kurtosis: | 2.075 | Cond. No. | 20.6 |