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 |
| 1.107 | 0.305 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.705 |
| Model: | OLS | Adj. R-squared: | 0.659 |
| Method: | Least Squares | F-statistic: | 15.16 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 2.81e-05 |
| Time: | 11:45:37 | Log-Likelihood: | -99.050 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 19 | BIC: | 210.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 23.2814 | 66.271 | 0.351 | 0.729 | -115.425 161.988 |
| C(dose)[T.1] | 184.3458 | 85.797 | 2.149 | 0.045 | 4.770 363.922 |
| expression | 5.8353 | 12.458 | 0.468 | 0.645 | -20.239 31.910 |
| expression:C(dose)[T.1] | -25.7885 | 16.487 | -1.564 | 0.134 | -60.296 8.719 |
| Omnibus: | 0.696 | Durbin-Watson: | 1.670 |
| Prob(Omnibus): | 0.706 | Jarque-Bera (JB): | 0.673 |
| Skew: | 0.356 | Prob(JB): | 0.714 |
| Kurtosis: | 2.559 | Cond. No. | 150. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.667 |
| Model: | OLS | Adj. R-squared: | 0.634 |
| Method: | Least Squares | F-statistic: | 20.07 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 1.65e-05 |
| Time: | 11:45:37 | Log-Likelihood: | -100.44 |
| No. Observations: | 23 | AIC: | 206.9 |
| Df Residuals: | 20 | BIC: | 210.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 101.3196 | 45.171 | 2.243 | 0.036 | 7.094 195.545 |
| C(dose)[T.1] | 50.8115 | 8.868 | 5.730 | 0.000 | 32.313 69.310 |
| expression | -8.8890 | 8.450 | -1.052 | 0.305 | -26.515 8.737 |
| Omnibus: | 0.362 | Durbin-Watson: | 1.848 |
| Prob(Omnibus): | 0.834 | Jarque-Bera (JB): | 0.505 |
| Skew: | 0.220 | Prob(JB): | 0.777 |
| Kurtosis: | 2.422 | Cond. No. | 57.3 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 11:45:37 | 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: | 0.122 |
| Model: | OLS | Adj. R-squared: | 0.080 |
| Method: | Least Squares | F-statistic: | 2.907 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.103 |
| Time: | 11:45:37 | Log-Likelihood: | -111.61 |
| No. Observations: | 23 | AIC: | 227.2 |
| Df Residuals: | 21 | BIC: | 229.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 193.3090 | 66.968 | 2.887 | 0.009 | 54.041 332.577 |
| expression | -21.9965 | 12.902 | -1.705 | 0.103 | -48.827 4.834 |
| Omnibus: | 1.272 | Durbin-Watson: | 2.332 |
| Prob(Omnibus): | 0.530 | Jarque-Bera (JB): | 0.877 |
| Skew: | 0.076 | Prob(JB): | 0.645 |
| Kurtosis: | 2.056 | Cond. No. | 53.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.259 | 0.620 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.475 |
| Model: | OLS | Adj. R-squared: | 0.331 |
| Method: | Least Squares | F-statistic: | 3.314 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0609 |
| Time: | 11:45:37 | Log-Likelihood: | -70.472 |
| No. Observations: | 15 | AIC: | 148.9 |
| Df Residuals: | 11 | BIC: | 151.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 75.6484 | 136.532 | 0.554 | 0.591 | -224.857 376.154 |
| C(dose)[T.1] | 173.5974 | 226.657 | 0.766 | 0.460 | -325.271 672.466 |
| expression | -1.3763 | 22.776 | -0.060 | 0.953 | -51.506 48.753 |
| expression:C(dose)[T.1] | -20.4998 | 37.498 | -0.547 | 0.596 | -103.032 62.032 |
| Omnibus: | 2.104 | Durbin-Watson: | 0.841 |
| Prob(Omnibus): | 0.349 | Jarque-Bera (JB): | 1.598 |
| Skew: | -0.738 | Prob(JB): | 0.450 |
| Kurtosis: | 2.387 | Cond. No. | 221. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.460 |
| Model: | OLS | Adj. R-squared: | 0.371 |
| Method: | Least Squares | F-statistic: | 5.120 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0247 |
| Time: | 11:45:37 | Log-Likelihood: | -70.673 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 12 | BIC: | 149.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 120.8175 | 105.472 | 1.145 | 0.274 | -108.986 350.621 |
| C(dose)[T.1] | 49.9997 | 15.652 | 3.194 | 0.008 | 15.897 84.103 |
| expression | -8.9391 | 17.557 | -0.509 | 0.620 | -47.192 29.314 |
| Omnibus: | 2.480 | Durbin-Watson: | 0.972 |
| Prob(Omnibus): | 0.289 | Jarque-Bera (JB): | 1.814 |
| Skew: | -0.811 | Prob(JB): | 0.404 |
| Kurtosis: | 2.476 | Cond. No. | 84.5 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 11:45:37 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.075 |
| Method: | Least Squares | F-statistic: | 0.02072 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.888 |
| Time: | 11:45:37 | Log-Likelihood: | -75.288 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 113.4507 | 137.811 | 0.823 | 0.425 | -184.271 411.172 |
| expression | -3.2861 | 22.828 | -0.144 | 0.888 | -52.604 46.031 |
| Omnibus: | 0.644 | Durbin-Watson: | 1.686 |
| Prob(Omnibus): | 0.725 | Jarque-Bera (JB): | 0.595 |
| Skew: | 0.041 | Prob(JB): | 0.743 |
| Kurtosis: | 2.027 | Cond. No. | 84.2 |