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 |
| 0.187 | 0.670 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.676 |
| Model: | OLS | Adj. R-squared: | 0.625 |
| Method: | Least Squares | F-statistic: | 13.22 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.80e-05 |
| Time: | 04:59:15 | Log-Likelihood: | -100.14 |
| No. Observations: | 23 | AIC: | 208.3 |
| Df Residuals: | 19 | BIC: | 212.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 28.1385 | 53.164 | 0.529 | 0.603 | -83.134 139.411 |
| C(dose)[T.1] | 146.1963 | 78.738 | 1.857 | 0.079 | -18.605 310.998 |
| expression | 5.0795 | 10.293 | 0.494 | 0.627 | -16.463 26.622 |
| expression:C(dose)[T.1] | -17.6645 | 14.978 | -1.179 | 0.253 | -49.013 13.684 |
| Omnibus: | 0.173 | Durbin-Watson: | 1.841 |
| Prob(Omnibus): | 0.917 | Jarque-Bera (JB): | 0.378 |
| Skew: | -0.107 | Prob(JB): | 0.828 |
| Kurtosis: | 2.410 | Cond. No. | 127. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 18.76 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.58e-05 |
| Time: | 04:59:15 | Log-Likelihood: | -100.96 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 70.9536 | 39.217 | 1.809 | 0.085 | -10.851 152.759 |
| C(dose)[T.1] | 53.9070 | 8.828 | 6.106 | 0.000 | 35.492 72.322 |
| expression | -3.2627 | 7.550 | -0.432 | 0.670 | -19.012 12.486 |
| Omnibus: | 0.214 | Durbin-Watson: | 1.918 |
| Prob(Omnibus): | 0.898 | Jarque-Bera (JB): | 0.415 |
| Skew: | 0.077 | Prob(JB): | 0.813 |
| Kurtosis: | 2.360 | Cond. No. | 49.1 |
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: | 04:59:15 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.043 |
| Method: | Least Squares | F-statistic: | 0.08637 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.772 |
| Time: | 04:59:15 | Log-Likelihood: | -113.06 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 60.8170 | 64.714 | 0.940 | 0.358 | -73.763 195.397 |
| expression | 3.6236 | 12.330 | 0.294 | 0.772 | -22.018 29.265 |
| Omnibus: | 2.804 | Durbin-Watson: | 2.454 |
| Prob(Omnibus): | 0.246 | Jarque-Bera (JB): | 1.524 |
| Skew: | 0.326 | Prob(JB): | 0.467 |
| Kurtosis: | 1.921 | Cond. No. | 48.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.061 | 0.323 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.701 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 8.589 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00320 |
| Time: | 04:59:15 | Log-Likelihood: | -66.250 |
| No. Observations: | 15 | AIC: | 140.5 |
| Df Residuals: | 11 | BIC: | 143.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 73.1659 | 40.850 | 1.791 | 0.101 | -16.744 163.075 |
| C(dose)[T.1] | -209.1827 | 92.952 | -2.250 | 0.046 | -413.768 -4.598 |
| expression | -1.3388 | 9.306 | -0.144 | 0.888 | -21.822 19.144 |
| expression:C(dose)[T.1] | 55.3916 | 20.064 | 2.761 | 0.019 | 11.230 99.553 |
| Omnibus: | 2.462 | Durbin-Watson: | 1.695 |
| Prob(Omnibus): | 0.292 | Jarque-Bera (JB): | 1.250 |
| Skew: | -0.707 | Prob(JB): | 0.535 |
| Kurtosis: | 3.037 | Cond. No. | 88.3 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.494 |
| Model: | OLS | Adj. R-squared: | 0.409 |
| Method: | Least Squares | F-statistic: | 5.847 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0169 |
| Time: | 04:59:15 | Log-Likelihood: | -70.198 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 12 | BIC: | 148.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 22.1002 | 45.371 | 0.487 | 0.635 | -76.754 120.954 |
| C(dose)[T.1] | 45.0851 | 15.606 | 2.889 | 0.014 | 11.082 79.088 |
| expression | 10.5776 | 10.271 | 1.030 | 0.323 | -11.800 32.955 |
| Omnibus: | 1.823 | Durbin-Watson: | 0.856 |
| Prob(Omnibus): | 0.402 | Jarque-Bera (JB): | 1.303 |
| Skew: | -0.685 | Prob(JB): | 0.521 |
| Kurtosis: | 2.541 | Cond. No. | 29.0 |
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: | 04:59:15 | 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: | 0.141 |
| Model: | OLS | Adj. R-squared: | 0.075 |
| Method: | Least Squares | F-statistic: | 2.139 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.167 |
| Time: | 04:59:15 | Log-Likelihood: | -74.158 |
| No. Observations: | 15 | AIC: | 152.3 |
| Df Residuals: | 13 | BIC: | 153.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 12.0484 | 56.593 | 0.213 | 0.835 | -110.212 134.309 |
| expression | 18.1672 | 12.421 | 1.463 | 0.167 | -8.667 45.002 |
| Omnibus: | 0.612 | Durbin-Watson: | 1.594 |
| Prob(Omnibus): | 0.736 | Jarque-Bera (JB): | 0.638 |
| Skew: | -0.262 | Prob(JB): | 0.727 |
| Kurtosis: | 2.137 | Cond. No. | 28.7 |