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 |
| 0.058 | 0.812 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.598 |
| Method: | Least Squares | F-statistic: | 11.89 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.000130 |
| Time: | 19:05:00 | Log-Likelihood: | -100.95 |
| No. Observations: | 23 | AIC: | 209.9 |
| Df Residuals: | 19 | BIC: | 214.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 50.6778 | 107.394 | 0.472 | 0.642 | -174.101 275.456 |
| C(dose)[T.1] | -26.9663 | 221.987 | -0.121 | 0.905 | -491.590 437.658 |
| expression | 0.5180 | 15.730 | 0.033 | 0.974 | -32.405 33.441 |
| expression:C(dose)[T.1] | 11.9303 | 32.844 | 0.363 | 0.720 | -56.812 80.673 |
| Omnibus: | 0.670 | Durbin-Watson: | 1.917 |
| Prob(Omnibus): | 0.715 | Jarque-Bera (JB): | 0.697 |
| Skew: | 0.185 | Prob(JB): | 0.706 |
| Kurtosis: | 2.232 | Cond. No. | 401. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.58 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 2.75e-05 |
| Time: | 19:05:00 | Log-Likelihood: | -101.03 |
| 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 | 32.0259 | 92.253 | 0.347 | 0.732 | -160.411 224.463 |
| C(dose)[T.1] | 53.6024 | 8.826 | 6.073 | 0.000 | 35.192 72.013 |
| expression | 3.2544 | 13.505 | 0.241 | 0.812 | -24.917 31.426 |
| Omnibus: | 0.390 | Durbin-Watson: | 1.857 |
| Prob(Omnibus): | 0.823 | Jarque-Bera (JB): | 0.527 |
| Skew: | 0.089 | Prob(JB): | 0.768 |
| Kurtosis: | 2.280 | Cond. No. | 147. |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 19:05:00 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.043 |
| Method: | Least Squares | F-statistic: | 0.1000 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.755 |
| Time: | 19:05:00 | Log-Likelihood: | -113.05 |
| 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 | 126.9874 | 149.637 | 0.849 | 0.406 | -184.199 438.174 |
| expression | -6.9749 | 22.054 | -0.316 | 0.755 | -52.839 38.889 |
| Omnibus: | 3.613 | Durbin-Watson: | 2.486 |
| Prob(Omnibus): | 0.164 | Jarque-Bera (JB): | 1.608 |
| Skew: | 0.276 | Prob(JB): | 0.448 |
| Kurtosis: | 1.828 | Cond. No. | 144. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.171 | 0.686 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.522 |
| Model: | OLS | Adj. R-squared: | 0.392 |
| Method: | Least Squares | F-statistic: | 4.003 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0376 |
| Time: | 19:05:00 | Log-Likelihood: | -69.765 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 11 | BIC: | 150.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 133.5462 | 66.286 | 2.015 | 0.069 | -12.348 279.440 |
| C(dose)[T.1] | -101.3556 | 123.793 | -0.819 | 0.430 | -373.823 171.112 |
| expression | -10.2965 | 10.175 | -1.012 | 0.333 | -32.691 12.098 |
| expression:C(dose)[T.1] | 23.3737 | 19.056 | 1.227 | 0.246 | -18.567 65.315 |
| Omnibus: | 3.005 | Durbin-Watson: | 1.153 |
| Prob(Omnibus): | 0.223 | Jarque-Bera (JB): | 1.660 |
| Skew: | -0.815 | Prob(JB): | 0.436 |
| Kurtosis: | 3.014 | Cond. No. | 131. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.457 |
| Model: | OLS | Adj. R-squared: | 0.366 |
| Method: | Least Squares | F-statistic: | 5.040 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0258 |
| Time: | 19:05:00 | Log-Likelihood: | -70.727 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 90.7539 | 57.535 | 1.577 | 0.141 | -34.605 216.113 |
| C(dose)[T.1] | 49.3245 | 15.632 | 3.155 | 0.008 | 15.266 83.383 |
| expression | -3.6325 | 8.782 | -0.414 | 0.686 | -22.767 15.502 |
| Omnibus: | 2.309 | Durbin-Watson: | 0.884 |
| Prob(Omnibus): | 0.315 | Jarque-Bera (JB): | 1.669 |
| Skew: | -0.779 | Prob(JB): | 0.434 |
| Kurtosis: | 2.507 | Cond. No. | 49.3 |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 19:05:00 | 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: | 0.006 |
| Model: | OLS | Adj. R-squared: | -0.071 |
| Method: | Least Squares | F-statistic: | 0.07302 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.791 |
| Time: | 19:05:00 | Log-Likelihood: | -75.258 |
| 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 | 113.5249 | 74.183 | 1.530 | 0.150 | -46.737 273.787 |
| expression | -3.0835 | 11.411 | -0.270 | 0.791 | -27.735 21.568 |
| Omnibus: | 1.359 | Durbin-Watson: | 1.648 |
| Prob(Omnibus): | 0.507 | Jarque-Bera (JB): | 0.837 |
| Skew: | 0.154 | Prob(JB): | 0.658 |
| Kurtosis: | 1.884 | Cond. No. | 48.7 |