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 |
| 0.005 | 0.945 | 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: | 12.37 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000102 |
| Time: | 04:26:25 | Log-Likelihood: | -100.65 |
| No. Observations: | 23 | AIC: | 209.3 |
| Df Residuals: | 19 | BIC: | 213.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 76.3450 | 37.946 | 2.012 | 0.059 | -3.077 155.767 |
| C(dose)[T.1] | 5.1695 | 58.784 | 0.088 | 0.931 | -117.867 128.207 |
| expression | -4.1235 | 6.976 | -0.591 | 0.561 | -18.725 10.478 |
| expression:C(dose)[T.1] | 8.9149 | 10.751 | 0.829 | 0.417 | -13.587 31.416 |
| Omnibus: | 0.336 | Durbin-Watson: | 1.958 |
| Prob(Omnibus): | 0.846 | Jarque-Bera (JB): | 0.495 |
| Skew: | 0.083 | Prob(JB): | 0.781 |
| Kurtosis: | 2.301 | Cond. No. | 94.1 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.50 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.83e-05 |
| Time: | 04:26:25 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 56.1930 | 28.915 | 1.943 | 0.066 | -4.122 116.508 |
| C(dose)[T.1] | 53.3609 | 8.775 | 6.081 | 0.000 | 35.056 71.666 |
| expression | -0.3697 | 5.266 | -0.070 | 0.945 | -11.355 10.616 |
| Omnibus: | 0.348 | Durbin-Watson: | 1.886 |
| Prob(Omnibus): | 0.840 | Jarque-Bera (JB): | 0.500 |
| Skew: | 0.051 | Prob(JB): | 0.779 |
| Kurtosis: | 2.285 | Cond. No. | 37.4 |
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: | 04:26:25 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.01005 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.921 |
| Time: | 04:26:25 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 75.0267 | 47.353 | 1.584 | 0.128 | -23.450 173.504 |
| expression | 0.8688 | 8.668 | 0.100 | 0.921 | -17.157 18.895 |
| Omnibus: | 3.214 | Durbin-Watson: | 2.485 |
| Prob(Omnibus): | 0.200 | Jarque-Bera (JB): | 1.539 |
| Skew: | 0.281 | Prob(JB): | 0.463 |
| Kurtosis: | 1.864 | Cond. No. | 37.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.317 | 0.583 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.478 |
| Model: | OLS | Adj. R-squared: | 0.336 |
| Method: | Least Squares | F-statistic: | 3.358 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0589 |
| Time: | 04:26:25 | Log-Likelihood: | -70.424 |
| No. Observations: | 15 | AIC: | 148.8 |
| Df Residuals: | 11 | BIC: | 151.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -25.1749 | 119.018 | -0.212 | 0.836 | -287.132 236.782 |
| C(dose)[T.1] | 153.0093 | 188.203 | 0.813 | 0.433 | -261.222 567.241 |
| expression | 17.6943 | 22.632 | 0.782 | 0.451 | -32.118 67.506 |
| expression:C(dose)[T.1] | -19.7579 | 35.050 | -0.564 | 0.584 | -96.902 57.386 |
| Omnibus: | 2.913 | Durbin-Watson: | 0.709 |
| Prob(Omnibus): | 0.233 | Jarque-Bera (JB): | 1.944 |
| Skew: | -0.868 | Prob(JB): | 0.378 |
| Kurtosis: | 2.693 | Cond. No. | 168. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.463 |
| Model: | OLS | Adj. R-squared: | 0.373 |
| Method: | Least Squares | F-statistic: | 5.173 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0240 |
| Time: | 04:26:25 | Log-Likelihood: | -70.637 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 12 | BIC: | 149.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 17.9373 | 88.563 | 0.203 | 0.843 | -175.025 210.900 |
| C(dose)[T.1] | 47.3189 | 15.889 | 2.978 | 0.012 | 12.700 81.938 |
| expression | 9.4566 | 16.783 | 0.563 | 0.583 | -27.110 46.023 |
| Omnibus: | 2.281 | Durbin-Watson: | 0.830 |
| Prob(Omnibus): | 0.320 | Jarque-Bera (JB): | 1.644 |
| Skew: | -0.774 | Prob(JB): | 0.440 |
| Kurtosis: | 2.514 | Cond. No. | 63.7 |
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: | 04:26:25 | 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: | 0.066 |
| Model: | OLS | Adj. R-squared: | -0.006 |
| Method: | Least Squares | F-statistic: | 0.9196 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.355 |
| Time: | 04:26:26 | Log-Likelihood: | -74.787 |
| No. Observations: | 15 | AIC: | 153.6 |
| Df Residuals: | 13 | BIC: | 155.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -12.7927 | 111.446 | -0.115 | 0.910 | -253.558 227.973 |
| expression | 19.9385 | 20.791 | 0.959 | 0.355 | -24.978 64.855 |
| Omnibus: | 0.764 | Durbin-Watson: | 1.712 |
| Prob(Omnibus): | 0.683 | Jarque-Bera (JB): | 0.709 |
| Skew: | 0.272 | Prob(JB): | 0.702 |
| Kurtosis: | 2.084 | Cond. No. | 62.9 |