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 |
| 0.000 | 1.000 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.594 |
| Method: | Least Squares | F-statistic: | 11.72 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000143 |
| Time: | 04:54:54 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 210.1 |
| Df Residuals: | 19 | BIC: | 214.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 54.9518 | 38.486 | 1.428 | 0.170 | -25.600 135.503 |
| C(dose)[T.1] | 48.7646 | 93.927 | 0.519 | 0.610 | -147.826 245.355 |
| expression | -0.1725 | 8.810 | -0.020 | 0.985 | -18.612 18.267 |
| expression:C(dose)[T.1] | 1.0520 | 21.511 | 0.049 | 0.962 | -43.972 46.076 |
| Omnibus: | 0.375 | Durbin-Watson: | 1.897 |
| Prob(Omnibus): | 0.829 | Jarque-Bera (JB): | 0.516 |
| Skew: | 0.072 | Prob(JB): | 0.772 |
| Kurtosis: | 2.280 | Cond. No. | 111. |
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: | 18.49 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.83e-05 |
| Time: | 04:54:54 | Log-Likelihood: | -101.06 |
| 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 | 54.1911 | 34.313 | 1.579 | 0.130 | -17.385 125.767 |
| C(dose)[T.1] | 53.3370 | 8.776 | 6.077 | 0.000 | 35.030 71.644 |
| expression | 0.0040 | 7.834 | 0.001 | 1.000 | -16.338 16.346 |
| 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. | 36.2 |
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: | 04:54:54 | 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: | 0.01966 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.890 |
| Time: | 04:54:54 | 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 | 71.8887 | 56.296 | 1.277 | 0.216 | -45.184 188.962 |
| expression | 1.8075 | 12.890 | 0.140 | 0.890 | -25.000 28.615 |
| Omnibus: | 3.419 | Durbin-Watson: | 2.494 |
| Prob(Omnibus): | 0.181 | Jarque-Bera (JB): | 1.617 |
| Skew: | 0.304 | Prob(JB): | 0.446 |
| Kurtosis: | 1.852 | Cond. No. | 35.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.000 | 0.985 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.470 |
| Model: | OLS | Adj. R-squared: | 0.326 |
| Method: | Least Squares | F-statistic: | 3.254 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0636 |
| Time: | 04:54:54 | Log-Likelihood: | -70.535 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 151.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 100.4198 | 77.261 | 1.300 | 0.220 | -69.630 270.469 |
| C(dose)[T.1] | -30.7925 | 120.958 | -0.255 | 0.804 | -297.020 235.435 |
| expression | -5.5518 | 12.850 | -0.432 | 0.674 | -33.834 22.730 |
| expression:C(dose)[T.1] | 13.6815 | 20.510 | 0.667 | 0.518 | -31.461 58.824 |
| Omnibus: | 2.314 | Durbin-Watson: | 0.808 |
| Prob(Omnibus): | 0.314 | Jarque-Bera (JB): | 1.686 |
| Skew: | -0.780 | Prob(JB): | 0.430 |
| Kurtosis: | 2.489 | Cond. No. | 116. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.885 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0280 |
| Time: | 04:54:55 | Log-Likelihood: | -70.833 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 68.5083 | 59.249 | 1.156 | 0.270 | -60.583 197.600 |
| C(dose)[T.1] | 49.1671 | 15.818 | 3.108 | 0.009 | 14.702 83.633 |
| expression | -0.1817 | 9.781 | -0.019 | 0.985 | -21.493 21.129 |
| Omnibus: | 2.756 | Durbin-Watson: | 0.811 |
| Prob(Omnibus): | 0.252 | Jarque-Bera (JB): | 1.892 |
| Skew: | -0.850 | Prob(JB): | 0.388 |
| Kurtosis: | 2.627 | Cond. No. | 46.1 |
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: | 04:54:55 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.072 |
| Method: | Least Squares | F-statistic: | 0.06560 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.802 |
| Time: | 04:54:55 | Log-Likelihood: | -75.262 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 112.5099 | 74.264 | 1.515 | 0.154 | -47.928 272.948 |
| expression | -3.2176 | 12.562 | -0.256 | 0.802 | -30.357 23.922 |
| Omnibus: | 0.514 | Durbin-Watson: | 1.665 |
| Prob(Omnibus): | 0.773 | Jarque-Bera (JB): | 0.546 |
| Skew: | 0.038 | Prob(JB): | 0.761 |
| Kurtosis: | 2.068 | Cond. No. | 44.5 |