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 |
| 0.000 | 1.000 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.719 |
| Model: | OLS | Adj. R-squared: | 0.675 |
| Method: | Least Squares | F-statistic: | 16.22 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.80e-05 |
| Time: | 04:44:24 | Log-Likelihood: | -98.501 |
| No. Observations: | 23 | AIC: | 205.0 |
| Df Residuals: | 19 | BIC: | 209.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 137.0596 | 45.052 | 3.042 | 0.007 | 42.764 231.355 |
| C(dose)[T.1] | -66.1324 | 55.583 | -1.190 | 0.249 | -182.468 50.204 |
| expression | -21.5429 | 11.625 | -1.853 | 0.079 | -45.874 2.788 |
| expression:C(dose)[T.1] | 29.7466 | 13.661 | 2.177 | 0.042 | 1.154 58.340 |
| Omnibus: | 0.545 | Durbin-Watson: | 2.264 |
| Prob(Omnibus): | 0.762 | Jarque-Bera (JB): | 0.599 |
| Skew: | 0.042 | Prob(JB): | 0.741 |
| Kurtosis: | 2.214 | Cond. No. | 88.5 |
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: | 18.49 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.83e-05 |
| Time: | 04:44:24 | 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 | 54.2234 | 26.295 | 2.062 | 0.052 | -0.627 109.074 |
| C(dose)[T.1] | 53.3395 | 9.685 | 5.507 | 0.000 | 33.137 73.542 |
| expression | -0.0039 | 6.653 | -0.001 | 1.000 | -13.882 13.874 |
| Omnibus: | 0.321 | Durbin-Watson: | 1.888 |
| Prob(Omnibus): | 0.852 | Jarque-Bera (JB): | 0.485 |
| Skew: | 0.059 | Prob(JB): | 0.785 |
| Kurtosis: | 2.299 | Cond. No. | 27.0 |
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: | 04:44:24 | 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: | 0.117 |
| Model: | OLS | Adj. R-squared: | 0.075 |
| Method: | Least Squares | F-statistic: | 2.778 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.110 |
| Time: | 04:44:24 | Log-Likelihood: | -111.68 |
| No. Observations: | 23 | AIC: | 227.4 |
| Df Residuals: | 21 | BIC: | 229.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 15.3460 | 39.214 | 0.391 | 0.699 | -66.204 96.896 |
| expression | 15.5437 | 9.326 | 1.667 | 0.110 | -3.851 34.939 |
| Omnibus: | 0.076 | Durbin-Watson: | 2.542 |
| Prob(Omnibus): | 0.963 | Jarque-Bera (JB): | 0.263 |
| Skew: | 0.101 | Prob(JB): | 0.877 |
| Kurtosis: | 2.517 | Cond. No. | 25.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.021 | 0.332 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.582 |
| Model: | OLS | Adj. R-squared: | 0.468 |
| Method: | Least Squares | F-statistic: | 5.103 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0187 |
| Time: | 04:44:24 | Log-Likelihood: | -68.760 |
| No. Observations: | 15 | AIC: | 145.5 |
| Df Residuals: | 11 | BIC: | 148.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 203.7261 | 74.391 | 2.739 | 0.019 | 39.994 367.459 |
| C(dose)[T.1] | -106.3952 | 102.247 | -1.041 | 0.320 | -331.438 118.648 |
| expression | -37.9436 | 20.504 | -1.851 | 0.091 | -83.072 7.185 |
| expression:C(dose)[T.1] | 43.3077 | 28.166 | 1.538 | 0.152 | -18.686 105.301 |
| Omnibus: | 4.326 | Durbin-Watson: | 1.214 |
| Prob(Omnibus): | 0.115 | Jarque-Bera (JB): | 2.051 |
| Skew: | -0.860 | Prob(JB): | 0.359 |
| Kurtosis: | 3.570 | Cond. No. | 75.2 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.492 |
| Model: | OLS | Adj. R-squared: | 0.407 |
| Method: | Least Squares | F-statistic: | 5.811 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0172 |
| Time: | 04:44:24 | Log-Likelihood: | -70.220 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 12 | BIC: | 148.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 121.2877 | 54.421 | 2.229 | 0.046 | 2.715 239.860 |
| C(dose)[T.1] | 49.2679 | 15.110 | 3.261 | 0.007 | 16.346 82.189 |
| expression | -14.9937 | 14.835 | -1.011 | 0.332 | -47.317 17.330 |
| Omnibus: | 2.548 | Durbin-Watson: | 1.053 |
| Prob(Omnibus): | 0.280 | Jarque-Bera (JB): | 1.549 |
| Skew: | -0.782 | Prob(JB): | 0.461 |
| Kurtosis: | 2.817 | Cond. No. | 28.4 |
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: | 04:44:24 | 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: | 0.042 |
| Model: | OLS | Adj. R-squared: | -0.032 |
| Method: | Least Squares | F-statistic: | 0.5692 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.464 |
| Time: | 04:44:24 | Log-Likelihood: | -74.979 |
| No. Observations: | 15 | AIC: | 154.0 |
| Df Residuals: | 13 | BIC: | 155.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 146.7504 | 71.062 | 2.065 | 0.059 | -6.769 300.270 |
| expression | -14.7674 | 19.574 | -0.754 | 0.464 | -57.055 27.520 |
| Omnibus: | 3.314 | Durbin-Watson: | 1.821 |
| Prob(Omnibus): | 0.191 | Jarque-Bera (JB): | 1.437 |
| Skew: | 0.378 | Prob(JB): | 0.488 |
| Kurtosis: | 1.686 | Cond. No. | 27.9 |