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 |
| 0.001 | 0.975 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.695 |
| Model: | OLS | Adj. R-squared: | 0.647 |
| Method: | Least Squares | F-statistic: | 14.43 |
| Date: | Wed, 29 Jan 2025 | Prob (F-statistic): | 3.89e-05 |
| Time: | 00:49:31 | Log-Likelihood: | -99.452 |
| No. Observations: | 23 | AIC: | 206.9 |
| Df Residuals: | 19 | BIC: | 211.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -117.1076 | 173.529 | -0.675 | 0.508 | -480.308 246.093 |
| C(dose)[T.1] | 539.5034 | 287.802 | 1.875 | 0.076 | -62.873 1141.880 |
| expression | 18.9090 | 19.143 | 0.988 | 0.336 | -21.157 58.975 |
| expression:C(dose)[T.1] | -53.9205 | 31.904 | -1.690 | 0.107 | -120.696 12.855 |
| Omnibus: | 2.581 | Durbin-Watson: | 1.896 |
| Prob(Omnibus): | 0.275 | Jarque-Bera (JB): | 1.211 |
| Skew: | 0.078 | Prob(JB): | 0.546 |
| Kurtosis: | 1.887 | Cond. No. | 767. |
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: | Wed, 29 Jan 2025 | Prob (F-statistic): | 2.83e-05 |
| Time: | 00:49:31 | 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 | 58.7629 | 145.168 | 0.405 | 0.690 | -244.052 361.578 |
| C(dose)[T.1] | 53.3033 | 8.835 | 6.033 | 0.000 | 34.873 71.734 |
| expression | -0.5027 | 16.009 | -0.031 | 0.975 | -33.897 32.891 |
| Omnibus: | 0.335 | Durbin-Watson: | 1.877 |
| Prob(Omnibus): | 0.846 | Jarque-Bera (JB): | 0.493 |
| Skew: | 0.064 | Prob(JB): | 0.782 |
| Kurtosis: | 2.295 | Cond. No. | 303. |
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: | Wed, 29 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 00:49:31 | 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: | 0.010 |
| Model: | OLS | Adj. R-squared: | -0.037 |
| Method: | Least Squares | F-statistic: | 0.2221 |
| Date: | Wed, 29 Jan 2025 | Prob (F-statistic): | 0.642 |
| Time: | 00:49:31 | Log-Likelihood: | -112.98 |
| No. Observations: | 23 | AIC: | 230.0 |
| Df Residuals: | 21 | BIC: | 232.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 190.5015 | 235.187 | 0.810 | 0.427 | -298.597 679.600 |
| expression | -12.2714 | 26.039 | -0.471 | 0.642 | -66.423 41.880 |
| Omnibus: | 3.327 | Durbin-Watson: | 2.405 |
| Prob(Omnibus): | 0.190 | Jarque-Bera (JB): | 1.462 |
| Skew: | 0.208 | Prob(JB): | 0.481 |
| Kurtosis: | 1.837 | Cond. No. | 300. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 17.626 | 0.001 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.790 |
| Model: | OLS | Adj. R-squared: | 0.732 |
| Method: | Least Squares | F-statistic: | 13.78 |
| Date: | Wed, 29 Jan 2025 | Prob (F-statistic): | 0.000481 |
| Time: | 00:49:31 | Log-Likelihood: | -63.602 |
| No. Observations: | 15 | AIC: | 135.2 |
| Df Residuals: | 11 | BIC: | 138.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -342.5117 | 180.562 | -1.897 | 0.084 | -739.926 54.903 |
| C(dose)[T.1] | -180.9815 | 254.856 | -0.710 | 0.492 | -741.916 379.953 |
| expression | 43.9923 | 19.360 | 2.272 | 0.044 | 1.380 86.605 |
| expression:C(dose)[T.1] | 22.2176 | 26.840 | 0.828 | 0.425 | -36.858 81.293 |
| Omnibus: | 1.601 | Durbin-Watson: | 1.381 |
| Prob(Omnibus): | 0.449 | Jarque-Bera (JB): | 1.002 |
| Skew: | -0.616 | Prob(JB): | 0.606 |
| Kurtosis: | 2.709 | Cond. No. | 649. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.777 |
| Model: | OLS | Adj. R-squared: | 0.740 |
| Method: | Least Squares | F-statistic: | 20.87 |
| Date: | Wed, 29 Jan 2025 | Prob (F-statistic): | 0.000124 |
| Time: | 00:49:31 | Log-Likelihood: | -64.055 |
| No. Observations: | 15 | AIC: | 134.1 |
| Df Residuals: | 12 | BIC: | 136.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -450.2321 | 123.519 | -3.645 | 0.003 | -719.357 -181.107 |
| C(dose)[T.1] | 29.7778 | 11.034 | 2.699 | 0.019 | 5.738 53.818 |
| expression | 55.5523 | 13.232 | 4.198 | 0.001 | 26.722 84.382 |
| Omnibus: | 0.963 | Durbin-Watson: | 1.666 |
| Prob(Omnibus): | 0.618 | Jarque-Bera (JB): | 0.869 |
| Skew: | -0.457 | Prob(JB): | 0.648 |
| Kurtosis: | 2.256 | Cond. No. | 238. |
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: | Wed, 29 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 00:49:31 | 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: | 0.641 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 23.23 |
| Date: | Wed, 29 Jan 2025 | Prob (F-statistic): | 0.000335 |
| Time: | 00:49:31 | Log-Likelihood: | -67.613 |
| No. Observations: | 15 | AIC: | 139.2 |
| Df Residuals: | 13 | BIC: | 140.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -576.6415 | 139.202 | -4.142 | 0.001 | -877.370 -275.913 |
| expression | 70.5226 | 14.631 | 4.820 | 0.000 | 38.913 102.132 |
| Omnibus: | 2.892 | Durbin-Watson: | 2.161 |
| Prob(Omnibus): | 0.236 | Jarque-Bera (JB): | 1.114 |
| Skew: | -0.072 | Prob(JB): | 0.573 |
| Kurtosis: | 1.673 | Cond. No. | 220. |