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 |
| 0.131 | 0.722 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.598 |
| Method: | Least Squares | F-statistic: | 11.92 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000128 |
| Time: | 04:26:33 | Log-Likelihood: | -100.93 |
| 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 | 54.4220 | 124.618 | 0.437 | 0.667 | -206.406 315.250 |
| C(dose)[T.1] | 4.4335 | 162.573 | 0.027 | 0.979 | -335.836 344.703 |
| expression | -0.0304 | 17.704 | -0.002 | 0.999 | -37.085 37.025 |
| expression:C(dose)[T.1] | 7.1526 | 23.362 | 0.306 | 0.763 | -41.744 56.050 |
| Omnibus: | 0.437 | Durbin-Watson: | 1.934 |
| Prob(Omnibus): | 0.804 | Jarque-Bera (JB): | 0.546 |
| Skew: | 0.036 | Prob(JB): | 0.761 |
| Kurtosis: | 2.249 | Cond. No. | 346. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.68 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.66e-05 |
| Time: | 04:26:33 | 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 | 25.5439 | 79.577 | 0.321 | 0.752 | -140.452 191.540 |
| C(dose)[T.1] | 54.1280 | 9.011 | 6.007 | 0.000 | 35.331 72.925 |
| expression | 4.0773 | 11.287 | 0.361 | 0.722 | -19.466 27.621 |
| Omnibus: | 0.238 | Durbin-Watson: | 1.947 |
| Prob(Omnibus): | 0.888 | Jarque-Bera (JB): | 0.432 |
| Skew: | 0.080 | Prob(JB): | 0.806 |
| Kurtosis: | 2.348 | Cond. No. | 130. |
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: | 04:26:33 | 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: | 0.022 |
| Model: | OLS | Adj. R-squared: | -0.024 |
| Method: | Least Squares | F-statistic: | 0.4798 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.496 |
| Time: | 04:26:33 | Log-Likelihood: | -112.84 |
| No. Observations: | 23 | AIC: | 229.7 |
| Df Residuals: | 21 | BIC: | 232.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 165.6943 | 124.327 | 1.333 | 0.197 | -92.857 424.246 |
| expression | -12.3930 | 17.891 | -0.693 | 0.496 | -49.600 24.814 |
| Omnibus: | 2.089 | Durbin-Watson: | 2.415 |
| Prob(Omnibus): | 0.352 | Jarque-Bera (JB): | 1.359 |
| Skew: | 0.337 | Prob(JB): | 0.507 |
| Kurtosis: | 2.018 | Cond. No. | 124. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.005 | 0.943 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.674 |
| Model: | OLS | Adj. R-squared: | 0.585 |
| Method: | Least Squares | F-statistic: | 7.590 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00503 |
| Time: | 04:26:33 | Log-Likelihood: | -66.888 |
| No. Observations: | 15 | AIC: | 141.8 |
| Df Residuals: | 11 | BIC: | 144.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 32.2337 | 62.029 | 0.520 | 0.614 | -104.292 168.759 |
| C(dose)[T.1] | 793.9315 | 270.238 | 2.938 | 0.013 | 199.142 1388.721 |
| expression | 5.0429 | 8.789 | 0.574 | 0.578 | -14.301 24.387 |
| expression:C(dose)[T.1] | -101.0843 | 36.652 | -2.758 | 0.019 | -181.755 -20.413 |
| Omnibus: | 4.043 | Durbin-Watson: | 0.936 |
| Prob(Omnibus): | 0.132 | Jarque-Bera (JB): | 2.203 |
| Skew: | -0.933 | Prob(JB): | 0.332 |
| Kurtosis: | 3.212 | Cond. No. | 371. |
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: | 4.890 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0280 |
| Time: | 04:26:33 | Log-Likelihood: | -70.830 |
| 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 | 72.7989 | 75.036 | 0.970 | 0.351 | -90.690 236.288 |
| C(dose)[T.1] | 49.5109 | 16.324 | 3.033 | 0.010 | 13.943 85.079 |
| expression | -0.7695 | 10.625 | -0.072 | 0.943 | -23.919 22.380 |
| Omnibus: | 2.674 | Durbin-Watson: | 0.819 |
| Prob(Omnibus): | 0.263 | Jarque-Bera (JB): | 1.867 |
| Skew: | -0.839 | Prob(JB): | 0.393 |
| Kurtosis: | 2.589 | Cond. No. | 70.7 |
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: | 04:26:33 | 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: | 0.027 |
| Model: | OLS | Adj. R-squared: | -0.048 |
| Method: | Least Squares | F-statistic: | 0.3559 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.561 |
| Time: | 04:26:33 | Log-Likelihood: | -75.097 |
| No. Observations: | 15 | AIC: | 154.2 |
| Df Residuals: | 13 | BIC: | 155.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 37.5121 | 94.660 | 0.396 | 0.698 | -166.989 242.013 |
| expression | 7.8024 | 13.079 | 0.597 | 0.561 | -20.452 36.057 |
| Omnibus: | 1.302 | Durbin-Watson: | 1.538 |
| Prob(Omnibus): | 0.521 | Jarque-Bera (JB): | 0.799 |
| Skew: | 0.086 | Prob(JB): | 0.671 |
| Kurtosis: | 1.883 | Cond. No. | 69.6 |