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 |
| 0.135 | 0.717 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 12.72 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 8.64e-05 |
| Time: | 22:03:13 | Log-Likelihood: | -100.44 |
| No. Observations: | 23 | AIC: | 208.9 |
| Df Residuals: | 19 | BIC: | 213.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -146.1881 | 215.463 | -0.678 | 0.506 | -597.157 304.781 |
| C(dose)[T.1] | 351.0495 | 309.870 | 1.133 | 0.271 | -297.517 999.616 |
| expression | 22.6539 | 24.347 | 0.930 | 0.364 | -28.306 73.614 |
| expression:C(dose)[T.1] | -33.7116 | 35.109 | -0.960 | 0.349 | -107.195 39.771 |
| Omnibus: | 0.852 | Durbin-Watson: | 1.643 |
| Prob(Omnibus): | 0.653 | Jarque-Bera (JB): | 0.725 |
| Skew: | -0.033 | Prob(JB): | 0.696 |
| Kurtosis: | 2.133 | Cond. No. | 814. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.617 |
| Method: | Least Squares | F-statistic: | 18.69 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 2.65e-05 |
| Time: | 22:03:14 | Log-Likelihood: | -100.99 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -2.7689 | 154.987 | -0.018 | 0.986 | -326.067 320.529 |
| C(dose)[T.1] | 53.6287 | 8.776 | 6.111 | 0.000 | 35.322 71.935 |
| expression | 6.4410 | 17.507 | 0.368 | 0.717 | -30.078 42.961 |
| Omnibus: | 0.116 | Durbin-Watson: | 1.791 |
| Prob(Omnibus): | 0.944 | Jarque-Bera (JB): | 0.340 |
| Skew: | 0.005 | Prob(JB): | 0.844 |
| Kurtosis: | 2.404 | Cond. No. | 318. |
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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:03:14 | 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: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.01248 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.912 |
| Time: | 22:03:14 | Log-Likelihood: | -113.10 |
| 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 | 108.1234 | 254.344 | 0.425 | 0.675 | -420.813 637.060 |
| expression | -3.2190 | 28.811 | -0.112 | 0.912 | -63.135 56.697 |
| Omnibus: | 3.337 | Durbin-Watson: | 2.515 |
| Prob(Omnibus): | 0.189 | Jarque-Bera (JB): | 1.560 |
| Skew: | 0.279 | Prob(JB): | 0.458 |
| Kurtosis: | 1.853 | Cond. No. | 315. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.244 | 0.630 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.491 |
| Model: | OLS | Adj. R-squared: | 0.352 |
| Method: | Least Squares | F-statistic: | 3.534 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0519 |
| Time: | 22:03:14 | Log-Likelihood: | -70.238 |
| No. Observations: | 15 | AIC: | 148.5 |
| Df Residuals: | 11 | BIC: | 151.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 292.7767 | 248.765 | 1.177 | 0.264 | -254.752 840.305 |
| C(dose)[T.1] | -252.7734 | 361.727 | -0.699 | 0.499 | -1048.930 543.383 |
| expression | -24.8979 | 27.456 | -0.907 | 0.384 | -85.327 35.531 |
| expression:C(dose)[T.1] | 34.0030 | 41.545 | 0.818 | 0.430 | -57.437 125.443 |
| Omnibus: | 2.187 | Durbin-Watson: | 0.978 |
| Prob(Omnibus): | 0.335 | Jarque-Bera (JB): | 1.413 |
| Skew: | -0.737 | Prob(JB): | 0.493 |
| Kurtosis: | 2.701 | Cond. No. | 524. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.460 |
| Model: | OLS | Adj. R-squared: | 0.370 |
| Method: | Least Squares | F-statistic: | 5.107 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0249 |
| Time: | 22:03:14 | Log-Likelihood: | -70.682 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 12 | BIC: | 149.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 158.3669 | 184.268 | 0.859 | 0.407 | -243.118 559.851 |
| C(dose)[T.1] | 42.8095 | 20.240 | 2.115 | 0.056 | -1.289 86.908 |
| expression | -10.0474 | 20.320 | -0.494 | 0.630 | -54.321 34.226 |
| Omnibus: | 2.960 | Durbin-Watson: | 0.768 |
| Prob(Omnibus): | 0.228 | Jarque-Bera (JB): | 2.046 |
| Skew: | -0.885 | Prob(JB): | 0.360 |
| Kurtosis: | 2.623 | Cond. No. | 210. |
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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:03:14 | 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: | 0.258 |
| Model: | OLS | Adj. R-squared: | 0.201 |
| Method: | Least Squares | F-statistic: | 4.529 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0530 |
| Time: | 22:03:14 | Log-Likelihood: | -73.058 |
| No. Observations: | 15 | AIC: | 150.1 |
| Df Residuals: | 13 | BIC: | 151.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 420.1668 | 153.668 | 2.734 | 0.017 | 88.187 752.146 |
| expression | -37.4776 | 17.610 | -2.128 | 0.053 | -75.522 0.567 |
| Omnibus: | 3.460 | Durbin-Watson: | 1.297 |
| Prob(Omnibus): | 0.177 | Jarque-Bera (JB): | 1.200 |
| Skew: | -0.065 | Prob(JB): | 0.549 |
| Kurtosis: | 1.621 | Cond. No. | 155. |