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 |
| 0.723 | 0.405 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.608 |
| Method: | Least Squares | F-statistic: | 12.38 |
| Date: | Sun, 02 Feb 2025 | Prob (F-statistic): | 0.000102 |
| Time: | 21:46:41 | Log-Likelihood: | -100.65 |
| No. Observations: | 23 | AIC: | 209.3 |
| Df Residuals: | 19 | BIC: | 213.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 35.5485 | 30.865 | 1.152 | 0.264 | -29.053 100.150 |
| C(dose)[T.1] | 47.6931 | 53.380 | 0.893 | 0.383 | -64.033 159.419 |
| expression | 4.6585 | 7.553 | 0.617 | 0.545 | -11.150 20.467 |
| expression:C(dose)[T.1] | 1.3787 | 13.099 | 0.105 | 0.917 | -26.037 28.794 |
| Omnibus: | 0.089 | Durbin-Watson: | 2.081 |
| Prob(Omnibus): | 0.957 | Jarque-Bera (JB): | 0.315 |
| Skew: | -0.005 | Prob(JB): | 0.854 |
| Kurtosis: | 2.427 | Cond. No. | 63.1 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.627 |
| Method: | Least Squares | F-statistic: | 19.53 |
| Date: | Sun, 02 Feb 2025 | Prob (F-statistic): | 1.99e-05 |
| Time: | 21:46:41 | Log-Likelihood: | -100.65 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 33.7122 | 24.825 | 1.358 | 0.190 | -18.071 85.495 |
| C(dose)[T.1] | 53.2341 | 8.616 | 6.178 | 0.000 | 35.261 71.207 |
| expression | 5.1169 | 6.016 | 0.850 | 0.405 | -7.433 17.667 |
| Omnibus: | 0.178 | Durbin-Watson: | 2.082 |
| Prob(Omnibus): | 0.915 | Jarque-Bera (JB): | 0.390 |
| Skew: | -0.015 | Prob(JB): | 0.823 |
| Kurtosis: | 2.363 | Cond. No. | 25.1 |
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: | Sun, 02 Feb 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 21:46:41 | 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: | 0.015 |
| Model: | OLS | Adj. R-squared: | -0.032 |
| Method: | Least Squares | F-statistic: | 0.3172 |
| Date: | Sun, 02 Feb 2025 | Prob (F-statistic): | 0.579 |
| Time: | 21:46:41 | Log-Likelihood: | -112.93 |
| No. Observations: | 23 | AIC: | 229.9 |
| Df Residuals: | 21 | BIC: | 232.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 57.0740 | 40.835 | 1.398 | 0.177 | -27.846 141.995 |
| expression | 5.6394 | 10.012 | 0.563 | 0.579 | -15.182 26.461 |
| Omnibus: | 2.841 | Durbin-Watson: | 2.693 |
| Prob(Omnibus): | 0.242 | Jarque-Bera (JB): | 1.458 |
| Skew: | 0.279 | Prob(JB): | 0.482 |
| Kurtosis: | 1.901 | Cond. No. | 24.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.821 | 0.383 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.755 |
| Model: | OLS | Adj. R-squared: | 0.689 |
| Method: | Least Squares | F-statistic: | 11.32 |
| Date: | Sun, 02 Feb 2025 | Prob (F-statistic): | 0.00109 |
| Time: | 21:46:41 | Log-Likelihood: | -64.740 |
| No. Observations: | 15 | AIC: | 137.5 |
| Df Residuals: | 11 | BIC: | 140.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 154.8381 | 38.839 | 3.987 | 0.002 | 69.354 240.323 |
| C(dose)[T.1] | -336.5788 | 109.735 | -3.067 | 0.011 | -578.104 -95.054 |
| expression | -19.7466 | 8.586 | -2.300 | 0.042 | -38.644 -0.849 |
| expression:C(dose)[T.1] | 94.8178 | 27.148 | 3.493 | 0.005 | 35.066 154.570 |
| Omnibus: | 0.113 | Durbin-Watson: | 1.651 |
| Prob(Omnibus): | 0.945 | Jarque-Bera (JB): | 0.177 |
| Skew: | 0.151 | Prob(JB): | 0.915 |
| Kurtosis: | 2.563 | Cond. No. | 103. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.484 |
| Model: | OLS | Adj. R-squared: | 0.398 |
| Method: | Least Squares | F-statistic: | 5.630 |
| Date: | Sun, 02 Feb 2025 | Prob (F-statistic): | 0.0189 |
| Time: | 21:46:41 | Log-Likelihood: | -70.337 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 12 | BIC: | 148.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 112.8549 | 51.351 | 2.198 | 0.048 | 0.971 224.739 |
| C(dose)[T.1] | 44.5565 | 16.065 | 2.773 | 0.017 | 9.553 79.560 |
| expression | -10.2622 | 11.325 | -0.906 | 0.383 | -34.938 14.413 |
| Omnibus: | 3.480 | Durbin-Watson: | 1.091 |
| Prob(Omnibus): | 0.176 | Jarque-Bera (JB): | 1.735 |
| Skew: | -0.823 | Prob(JB): | 0.420 |
| Kurtosis: | 3.259 | Cond. No. | 30.7 |
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: | Sun, 02 Feb 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 21:46:41 | 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: | 0.153 |
| Model: | OLS | Adj. R-squared: | 0.088 |
| Method: | Least Squares | F-statistic: | 2.355 |
| Date: | Sun, 02 Feb 2025 | Prob (F-statistic): | 0.149 |
| Time: | 21:46:41 | Log-Likelihood: | -74.051 |
| No. Observations: | 15 | AIC: | 152.1 |
| Df Residuals: | 13 | BIC: | 153.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 178.5216 | 56.082 | 3.183 | 0.007 | 57.364 299.679 |
| expression | -20.2739 | 13.212 | -1.535 | 0.149 | -48.816 8.268 |
| Omnibus: | 2.344 | Durbin-Watson: | 1.686 |
| Prob(Omnibus): | 0.310 | Jarque-Bera (JB): | 1.374 |
| Skew: | 0.467 | Prob(JB): | 0.503 |
| Kurtosis: | 1.848 | Cond. No. | 26.8 |