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 |
| 1.665 | 0.212 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.809 |
| Model: | OLS | Adj. R-squared: | 0.778 |
| Method: | Least Squares | F-statistic: | 26.77 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.94e-07 |
| Time: | 05:06:01 | Log-Likelihood: | -94.086 |
| No. Observations: | 23 | AIC: | 196.2 |
| Df Residuals: | 19 | BIC: | 200.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -68.6616 | 63.127 | -1.088 | 0.290 | -200.788 63.465 |
| C(dose)[T.1] | 320.9876 | 75.820 | 4.234 | 0.000 | 162.295 479.681 |
| expression | 29.8611 | 15.301 | 1.952 | 0.066 | -2.165 61.887 |
| expression:C(dose)[T.1] | -68.9728 | 19.004 | -3.629 | 0.002 | -108.749 -29.197 |
| Omnibus: | 0.463 | Durbin-Watson: | 1.638 |
| Prob(Omnibus): | 0.793 | Jarque-Bera (JB): | 0.578 |
| Skew: | 0.256 | Prob(JB): | 0.749 |
| Kurtosis: | 2.415 | Cond. No. | 132. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.676 |
| Model: | OLS | Adj. R-squared: | 0.644 |
| Method: | Least Squares | F-statistic: | 20.87 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.27e-05 |
| Time: | 05:06:01 | Log-Likelihood: | -100.14 |
| No. Observations: | 23 | AIC: | 206.3 |
| Df Residuals: | 20 | BIC: | 209.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 115.3208 | 47.715 | 2.417 | 0.025 | 15.789 214.853 |
| C(dose)[T.1] | 47.2032 | 9.674 | 4.879 | 0.000 | 27.023 67.384 |
| expression | -14.8522 | 11.509 | -1.290 | 0.212 | -38.860 9.156 |
| Omnibus: | 1.236 | Durbin-Watson: | 2.092 |
| Prob(Omnibus): | 0.539 | Jarque-Bera (JB): | 0.936 |
| Skew: | 0.474 | Prob(JB): | 0.626 |
| Kurtosis: | 2.721 | Cond. No. | 48.0 |
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: | 05:06:02 | 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: | 0.290 |
| Model: | OLS | Adj. R-squared: | 0.257 |
| Method: | Least Squares | F-statistic: | 8.594 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00797 |
| Time: | 05:06:02 | Log-Likelihood: | -109.16 |
| No. Observations: | 23 | AIC: | 222.3 |
| Df Residuals: | 21 | BIC: | 224.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 245.9787 | 57.039 | 4.312 | 0.000 | 127.359 364.598 |
| expression | -42.4440 | 14.478 | -2.932 | 0.008 | -72.553 -12.335 |
| Omnibus: | 0.621 | Durbin-Watson: | 2.466 |
| Prob(Omnibus): | 0.733 | Jarque-Bera (JB): | 0.655 |
| Skew: | -0.136 | Prob(JB): | 0.721 |
| Kurtosis: | 2.219 | Cond. No. | 39.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.065 | 0.803 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.476 |
| Model: | OLS | Adj. R-squared: | 0.333 |
| Method: | Least Squares | F-statistic: | 3.332 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0600 |
| Time: | 05:06:02 | Log-Likelihood: | -70.451 |
| No. Observations: | 15 | AIC: | 148.9 |
| Df Residuals: | 11 | BIC: | 151.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 61.0086 | 71.347 | 0.855 | 0.411 | -96.025 218.042 |
| C(dose)[T.1] | 159.7782 | 156.159 | 1.023 | 0.328 | -183.926 503.482 |
| expression | 1.5420 | 16.904 | 0.091 | 0.929 | -35.664 38.748 |
| expression:C(dose)[T.1] | -27.4214 | 38.333 | -0.715 | 0.489 | -111.793 56.950 |
| Omnibus: | 2.324 | Durbin-Watson: | 1.015 |
| Prob(Omnibus): | 0.313 | Jarque-Bera (JB): | 1.085 |
| Skew: | -0.657 | Prob(JB): | 0.581 |
| Kurtosis: | 3.102 | Cond. No. | 101. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 4.944 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0272 |
| Time: | 05:06:02 | Log-Likelihood: | -70.792 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 83.2102 | 62.922 | 1.322 | 0.211 | -53.886 220.306 |
| C(dose)[T.1] | 48.6711 | 15.832 | 3.074 | 0.010 | 14.177 83.165 |
| expression | -3.7905 | 14.860 | -0.255 | 0.803 | -36.168 28.587 |
| Omnibus: | 2.548 | Durbin-Watson: | 0.799 |
| Prob(Omnibus): | 0.280 | Jarque-Bera (JB): | 1.758 |
| Skew: | -0.815 | Prob(JB): | 0.415 |
| Kurtosis: | 2.608 | Cond. No. | 35.3 |
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: | 05:06:02 | 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: | 0.020 |
| Model: | OLS | Adj. R-squared: | -0.055 |
| Method: | Least Squares | F-statistic: | 0.2645 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.616 |
| Time: | 05:06:02 | Log-Likelihood: | -75.149 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 133.4718 | 78.051 | 1.710 | 0.111 | -35.148 302.091 |
| expression | -9.7334 | 18.926 | -0.514 | 0.616 | -50.621 31.155 |
| Omnibus: | 0.491 | Durbin-Watson: | 1.539 |
| Prob(Omnibus): | 0.782 | Jarque-Bera (JB): | 0.536 |
| Skew: | 0.011 | Prob(JB): | 0.765 |
| Kurtosis: | 2.074 | Cond. No. | 33.8 |