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 |
| 0.016 | 0.902 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.596 |
| Method: | Least Squares | F-statistic: | 11.81 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000136 |
| Time: | 03:42:25 | Log-Likelihood: | -101.00 |
| No. Observations: | 23 | AIC: | 210.0 |
| Df Residuals: | 19 | BIC: | 214.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 51.3933 | 42.439 | 1.211 | 0.241 | -37.433 140.219 |
| C(dose)[T.1] | 74.1037 | 72.702 | 1.019 | 0.321 | -78.063 226.271 |
| expression | 0.6031 | 8.995 | 0.067 | 0.947 | -18.223 19.429 |
| expression:C(dose)[T.1] | -4.4976 | 15.586 | -0.289 | 0.776 | -37.119 28.124 |
| Omnibus: | 0.577 | Durbin-Watson: | 1.882 |
| Prob(Omnibus): | 0.750 | Jarque-Bera (JB): | 0.637 |
| Skew: | 0.146 | Prob(JB): | 0.727 |
| Kurtosis: | 2.239 | Cond. No. | 95.8 |
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: | 18.52 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.81e-05 |
| Time: | 03:42:25 | 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 | 58.3847 | 34.036 | 1.715 | 0.102 | -12.613 129.382 |
| C(dose)[T.1] | 53.2851 | 8.776 | 6.071 | 0.000 | 34.978 71.592 |
| expression | -0.8948 | 7.175 | -0.125 | 0.902 | -15.862 14.073 |
| Omnibus: | 0.370 | Durbin-Watson: | 1.856 |
| Prob(Omnibus): | 0.831 | Jarque-Bera (JB): | 0.514 |
| Skew: | 0.080 | Prob(JB): | 0.773 |
| Kurtosis: | 2.285 | Cond. No. | 38.2 |
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: | 03:42:25 | 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: | 0.003 |
| Model: | OLS | Adj. R-squared: | -0.044 |
| Method: | Least Squares | F-statistic: | 0.06320 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.804 |
| Time: | 03:42:25 | Log-Likelihood: | -113.07 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 93.4740 | 55.193 | 1.694 | 0.105 | -21.307 208.255 |
| expression | -2.9649 | 11.794 | -0.251 | 0.804 | -27.491 21.562 |
| Omnibus: | 2.896 | Durbin-Watson: | 2.475 |
| Prob(Omnibus): | 0.235 | Jarque-Bera (JB): | 1.490 |
| Skew: | 0.293 | Prob(JB): | 0.475 |
| Kurtosis: | 1.899 | Cond. No. | 37.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.048 | 0.830 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.481 |
| Model: | OLS | Adj. R-squared: | 0.339 |
| Method: | Least Squares | F-statistic: | 3.394 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0574 |
| Time: | 03:42:25 | Log-Likelihood: | -70.386 |
| No. Observations: | 15 | AIC: | 148.8 |
| Df Residuals: | 11 | BIC: | 151.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 26.1613 | 116.449 | 0.225 | 0.826 | -230.141 282.463 |
| C(dose)[T.1] | 180.0586 | 168.261 | 1.070 | 0.307 | -190.281 550.398 |
| expression | 8.6611 | 24.317 | 0.356 | 0.728 | -44.861 62.183 |
| expression:C(dose)[T.1] | -29.4687 | 37.156 | -0.793 | 0.444 | -111.248 52.311 |
| Omnibus: | 1.734 | Durbin-Watson: | 0.981 |
| Prob(Omnibus): | 0.420 | Jarque-Bera (JB): | 1.380 |
| Skew: | -0.637 | Prob(JB): | 0.502 |
| Kurtosis: | 2.236 | Cond. No. | 130. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.359 |
| Method: | Least Squares | F-statistic: | 4.928 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0274 |
| Time: | 03:42:25 | Log-Likelihood: | -70.803 |
| 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 | 86.3028 | 86.998 | 0.992 | 0.341 | -103.250 275.856 |
| C(dose)[T.1] | 47.3790 | 17.768 | 2.667 | 0.021 | 8.666 86.092 |
| expression | -3.9613 | 18.100 | -0.219 | 0.830 | -43.397 35.474 |
| Omnibus: | 2.325 | Durbin-Watson: | 0.731 |
| Prob(Omnibus): | 0.313 | Jarque-Bera (JB): | 1.707 |
| Skew: | -0.783 | Prob(JB): | 0.426 |
| Kurtosis: | 2.470 | Cond. No. | 53.4 |
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: | 03:42:25 | 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: | 0.126 |
| Model: | OLS | Adj. R-squared: | 0.058 |
| Method: | Least Squares | F-statistic: | 1.868 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.195 |
| Time: | 03:42:25 | Log-Likelihood: | -74.293 |
| No. Observations: | 15 | AIC: | 152.6 |
| Df Residuals: | 13 | BIC: | 154.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 213.5223 | 88.205 | 2.421 | 0.031 | 22.968 404.077 |
| expression | -26.5168 | 19.401 | -1.367 | 0.195 | -68.430 15.396 |
| Omnibus: | 0.273 | Durbin-Watson: | 1.197 |
| Prob(Omnibus): | 0.872 | Jarque-Bera (JB): | 0.439 |
| Skew: | -0.082 | Prob(JB): | 0.803 |
| Kurtosis: | 2.178 | Cond. No. | 44.2 |