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 |
| 1.335 | 0.262 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.681 |
| Model: | OLS | Adj. R-squared: | 0.631 |
| Method: | Least Squares | F-statistic: | 13.55 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.81e-05 |
| Time: | 04:01:51 | Log-Likelihood: | -99.950 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 19 | BIC: | 212.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 75.6905 | 42.073 | 1.799 | 0.088 | -12.369 163.750 |
| C(dose)[T.1] | 108.7402 | 73.213 | 1.485 | 0.154 | -44.497 261.977 |
| expression | -4.5843 | 8.889 | -0.516 | 0.612 | -23.189 14.020 |
| expression:C(dose)[T.1] | -12.6302 | 16.033 | -0.788 | 0.441 | -46.188 20.927 |
| Omnibus: | 0.056 | Durbin-Watson: | 1.817 |
| Prob(Omnibus): | 0.972 | Jarque-Bera (JB): | 0.061 |
| Skew: | -0.017 | Prob(JB): | 0.970 |
| Kurtosis: | 2.750 | Cond. No. | 98.7 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.671 |
| Model: | OLS | Adj. R-squared: | 0.638 |
| Method: | Least Squares | F-statistic: | 20.40 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.48e-05 |
| Time: | 04:01:51 | Log-Likelihood: | -100.32 |
| No. Observations: | 23 | AIC: | 206.6 |
| Df Residuals: | 20 | BIC: | 210.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 93.8821 | 34.834 | 2.695 | 0.014 | 21.220 166.544 |
| C(dose)[T.1] | 51.4769 | 8.642 | 5.956 | 0.000 | 33.449 69.504 |
| expression | -8.4664 | 7.327 | -1.155 | 0.262 | -23.751 6.818 |
| Omnibus: | 0.061 | Durbin-Watson: | 1.939 |
| Prob(Omnibus): | 0.970 | Jarque-Bera (JB): | 0.255 |
| Skew: | 0.084 | Prob(JB): | 0.880 |
| Kurtosis: | 2.512 | Cond. No. | 39.9 |
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: | 04:01:51 | 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: | 0.087 |
| Model: | OLS | Adj. R-squared: | 0.044 |
| Method: | Least Squares | F-statistic: | 2.012 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.171 |
| Time: | 04:01:51 | Log-Likelihood: | -112.05 |
| No. Observations: | 23 | AIC: | 228.1 |
| Df Residuals: | 21 | BIC: | 230.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 155.7454 | 54.043 | 2.882 | 0.009 | 43.357 268.134 |
| expression | -16.5965 | 11.701 | -1.418 | 0.171 | -40.930 7.737 |
| Omnibus: | 5.788 | Durbin-Watson: | 2.398 |
| Prob(Omnibus): | 0.055 | Jarque-Bera (JB): | 1.813 |
| Skew: | 0.181 | Prob(JB): | 0.404 |
| Kurtosis: | 1.673 | Cond. No. | 37.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.111 | 0.313 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.497 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 3.622 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0488 |
| Time: | 04:01:51 | Log-Likelihood: | -70.147 |
| No. Observations: | 15 | AIC: | 148.3 |
| Df Residuals: | 11 | BIC: | 151.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 145.3979 | 105.617 | 1.377 | 0.196 | -87.064 377.860 |
| C(dose)[T.1] | 23.5828 | 129.330 | 0.182 | 0.859 | -261.070 308.236 |
| expression | -16.4934 | 22.210 | -0.743 | 0.473 | -65.377 32.390 |
| expression:C(dose)[T.1] | 4.9513 | 27.540 | 0.180 | 0.861 | -55.663 65.566 |
| Omnibus: | 4.819 | Durbin-Watson: | 0.686 |
| Prob(Omnibus): | 0.090 | Jarque-Bera (JB): | 3.016 |
| Skew: | -1.098 | Prob(JB): | 0.221 |
| Kurtosis: | 3.044 | Cond. No. | 114. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.495 |
| Model: | OLS | Adj. R-squared: | 0.411 |
| Method: | Least Squares | F-statistic: | 5.893 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0165 |
| Time: | 04:01:51 | Log-Likelihood: | -70.169 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 12 | BIC: | 148.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 130.1749 | 60.533 | 2.150 | 0.053 | -1.714 262.064 |
| C(dose)[T.1] | 46.6583 | 15.249 | 3.060 | 0.010 | 13.433 79.883 |
| expression | -13.2731 | 12.592 | -1.054 | 0.313 | -40.708 14.162 |
| Omnibus: | 5.218 | Durbin-Watson: | 0.674 |
| Prob(Omnibus): | 0.074 | Jarque-Bera (JB): | 3.269 |
| Skew: | -1.142 | Prob(JB): | 0.195 |
| Kurtosis: | 3.103 | Cond. No. | 39.6 |
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: | 04:01:51 | 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: | 0.102 |
| Model: | OLS | Adj. R-squared: | 0.033 |
| Method: | Least Squares | F-statistic: | 1.475 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.246 |
| Time: | 04:01:51 | Log-Likelihood: | -74.494 |
| No. Observations: | 15 | AIC: | 153.0 |
| Df Residuals: | 13 | BIC: | 154.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 183.1975 | 74.348 | 2.464 | 0.028 | 22.579 343.816 |
| expression | -19.3566 | 15.939 | -1.214 | 0.246 | -53.790 15.077 |
| Omnibus: | 1.813 | Durbin-Watson: | 1.741 |
| Prob(Omnibus): | 0.404 | Jarque-Bera (JB): | 0.910 |
| Skew: | -0.049 | Prob(JB): | 0.635 |
| Kurtosis: | 1.798 | Cond. No. | 37.6 |