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 |
| 0.024 | 0.879 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.594 |
| Method: | Least Squares | F-statistic: | 11.74 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000140 |
| Time: | 03:33:00 | Log-Likelihood: | -101.04 |
| No. Observations: | 23 | AIC: | 210.1 |
| Df Residuals: | 19 | BIC: | 214.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 44.6619 | 133.697 | 0.334 | 0.742 | -235.170 324.494 |
| C(dose)[T.1] | 27.6995 | 248.859 | 0.111 | 0.913 | -493.169 548.568 |
| expression | 1.1201 | 15.671 | 0.071 | 0.944 | -31.679 33.919 |
| expression:C(dose)[T.1] | 3.0312 | 29.297 | 0.103 | 0.919 | -58.287 64.350 |
| Omnibus: | 0.173 | Durbin-Watson: | 1.858 |
| Prob(Omnibus): | 0.917 | Jarque-Bera (JB): | 0.386 |
| Skew: | 0.022 | Prob(JB): | 0.824 |
| Kurtosis: | 2.367 | Cond. No. | 568. |
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: | 18.53 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.80e-05 |
| Time: | 03:33:00 | Log-Likelihood: | -101.05 |
| 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 | 37.2707 | 110.182 | 0.338 | 0.739 | -192.565 267.106 |
| C(dose)[T.1] | 53.4307 | 8.786 | 6.082 | 0.000 | 35.104 71.757 |
| expression | 1.9874 | 12.909 | 0.154 | 0.879 | -24.940 28.915 |
| Omnibus: | 0.208 | Durbin-Watson: | 1.857 |
| Prob(Omnibus): | 0.901 | Jarque-Bera (JB): | 0.411 |
| Skew: | 0.044 | Prob(JB): | 0.814 |
| Kurtosis: | 2.351 | Cond. No. | 217. |
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: | 03:33:00 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.046 |
| Method: | Least Squares | F-statistic: | 0.02639 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.873 |
| Time: | 03:33:00 | Log-Likelihood: | -113.09 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 109.0097 | 180.459 | 0.604 | 0.552 | -266.275 484.295 |
| expression | -3.4461 | 21.213 | -0.162 | 0.873 | -47.562 40.670 |
| Omnibus: | 3.120 | Durbin-Watson: | 2.507 |
| Prob(Omnibus): | 0.210 | Jarque-Bera (JB): | 1.540 |
| Skew: | 0.294 | Prob(JB): | 0.463 |
| Kurtosis: | 1.877 | Cond. No. | 216. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.857 | 0.198 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.523 |
| Model: | OLS | Adj. R-squared: | 0.393 |
| Method: | Least Squares | F-statistic: | 4.019 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0372 |
| Time: | 03:33:01 | Log-Likelihood: | -69.750 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 11 | BIC: | 150.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -110.6738 | 182.893 | -0.605 | 0.557 | -513.218 291.870 |
| C(dose)[T.1] | 72.2158 | 255.559 | 0.283 | 0.783 | -490.267 634.698 |
| expression | 22.2459 | 22.802 | 0.976 | 0.350 | -27.940 72.432 |
| expression:C(dose)[T.1] | -2.5639 | 32.114 | -0.080 | 0.938 | -73.247 68.119 |
| Omnibus: | 1.652 | Durbin-Watson: | 1.063 |
| Prob(Omnibus): | 0.438 | Jarque-Bera (JB): | 1.311 |
| Skew: | -0.587 | Prob(JB): | 0.519 |
| Kurtosis: | 2.153 | Cond. No. | 360. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.523 |
| Model: | OLS | Adj. R-squared: | 0.443 |
| Method: | Least Squares | F-statistic: | 6.569 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0118 |
| Time: | 03:33:01 | Log-Likelihood: | -69.754 |
| No. Observations: | 15 | AIC: | 145.5 |
| Df Residuals: | 12 | BIC: | 147.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -100.3259 | 123.577 | -0.812 | 0.433 | -369.578 168.926 |
| C(dose)[T.1] | 51.8502 | 14.776 | 3.509 | 0.004 | 19.656 84.045 |
| expression | 20.9534 | 15.377 | 1.363 | 0.198 | -12.551 54.458 |
| Omnibus: | 1.602 | Durbin-Watson: | 1.027 |
| Prob(Omnibus): | 0.449 | Jarque-Bera (JB): | 1.275 |
| Skew: | -0.572 | Prob(JB): | 0.529 |
| Kurtosis: | 2.145 | Cond. No. | 137. |
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: | 03:33:01 | 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: | 0.033 |
| Model: | OLS | Adj. R-squared: | -0.042 |
| Method: | Least Squares | F-statistic: | 0.4408 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.518 |
| Time: | 03:33:01 | Log-Likelihood: | -75.050 |
| No. Observations: | 15 | AIC: | 154.1 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -16.2124 | 165.791 | -0.098 | 0.924 | -374.383 341.958 |
| expression | 13.8412 | 20.846 | 0.664 | 0.518 | -31.195 58.877 |
| Omnibus: | 1.817 | Durbin-Watson: | 1.849 |
| Prob(Omnibus): | 0.403 | Jarque-Bera (JB): | 0.962 |
| Skew: | 0.185 | Prob(JB): | 0.618 |
| Kurtosis: | 1.816 | Cond. No. | 134. |