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 |
| 4.175 | 0.054 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.731 |
| Model: | OLS | Adj. R-squared: | 0.688 |
| Method: | Least Squares | F-statistic: | 17.18 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 1.22e-05 |
| Time: | 11:49:51 | Log-Likelihood: | -98.018 |
| No. Observations: | 23 | AIC: | 204.0 |
| Df Residuals: | 19 | BIC: | 208.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 36.0062 | 78.290 | 0.460 | 0.651 | -127.856 199.868 |
| C(dose)[T.1] | 153.2181 | 85.623 | 1.789 | 0.089 | -25.993 332.429 |
| expression | 3.2251 | 13.838 | 0.233 | 0.818 | -25.738 32.189 |
| expression:C(dose)[T.1] | -18.5753 | 15.258 | -1.217 | 0.238 | -50.510 13.360 |
| Omnibus: | 0.525 | Durbin-Watson: | 1.999 |
| Prob(Omnibus): | 0.769 | Jarque-Bera (JB): | 0.116 |
| Skew: | -0.174 | Prob(JB): | 0.944 |
| Kurtosis: | 3.021 | Cond. No. | 185. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.710 |
| Model: | OLS | Adj. R-squared: | 0.681 |
| Method: | Least Squares | F-statistic: | 24.44 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 4.26e-06 |
| Time: | 11:49:51 | Log-Likelihood: | -98.882 |
| No. Observations: | 23 | AIC: | 203.8 |
| Df Residuals: | 20 | BIC: | 207.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 122.2391 | 33.747 | 3.622 | 0.002 | 51.844 192.634 |
| C(dose)[T.1] | 49.4463 | 8.201 | 6.029 | 0.000 | 32.340 66.553 |
| expression | -12.0540 | 5.899 | -2.043 | 0.054 | -24.359 0.251 |
| Omnibus: | 0.360 | Durbin-Watson: | 2.001 |
| Prob(Omnibus): | 0.835 | Jarque-Bera (JB): | 0.024 |
| Skew: | 0.080 | Prob(JB): | 0.988 |
| Kurtosis: | 2.998 | Cond. No. | 48.6 |
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, 03 Dec 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 11:49:51 | 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: | 0.182 |
| Model: | OLS | Adj. R-squared: | 0.143 |
| Method: | Least Squares | F-statistic: | 4.670 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0424 |
| Time: | 11:49:51 | Log-Likelihood: | -110.80 |
| No. Observations: | 23 | AIC: | 225.6 |
| Df Residuals: | 21 | BIC: | 227.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 191.2214 | 52.009 | 3.677 | 0.001 | 83.062 299.381 |
| expression | -20.3124 | 9.399 | -2.161 | 0.042 | -39.860 -0.765 |
| Omnibus: | 5.445 | Durbin-Watson: | 2.602 |
| Prob(Omnibus): | 0.066 | Jarque-Bera (JB): | 1.669 |
| Skew: | 0.029 | Prob(JB): | 0.434 |
| Kurtosis: | 1.682 | Cond. No. | 45.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.487 | 0.246 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.523 |
| Model: | OLS | Adj. R-squared: | 0.393 |
| Method: | Least Squares | F-statistic: | 4.024 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0370 |
| Time: | 11:49:51 | Log-Likelihood: | -69.744 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 11 | BIC: | 150.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 198.3705 | 118.471 | 1.674 | 0.122 | -62.382 459.123 |
| C(dose)[T.1] | -37.6777 | 134.685 | -0.280 | 0.785 | -334.117 258.762 |
| expression | -22.8325 | 20.566 | -1.110 | 0.291 | -68.098 22.433 |
| expression:C(dose)[T.1] | 13.7035 | 24.381 | 0.562 | 0.585 | -39.959 67.366 |
| Omnibus: | 1.620 | Durbin-Watson: | 1.333 |
| Prob(Omnibus): | 0.445 | Jarque-Bera (JB): | 0.942 |
| Skew: | -0.604 | Prob(JB): | 0.624 |
| Kurtosis: | 2.782 | Cond. No. | 140. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.510 |
| Model: | OLS | Adj. R-squared: | 0.428 |
| Method: | Least Squares | F-statistic: | 6.234 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0139 |
| Time: | 11:49:51 | Log-Likelihood: | -69.957 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 12 | BIC: | 148.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 142.4524 | 62.462 | 2.281 | 0.042 | 6.359 278.546 |
| C(dose)[T.1] | 37.3223 | 17.754 | 2.102 | 0.057 | -1.360 76.005 |
| expression | -13.0820 | 10.726 | -1.220 | 0.246 | -36.453 10.289 |
| Omnibus: | 1.272 | Durbin-Watson: | 1.207 |
| Prob(Omnibus): | 0.529 | Jarque-Bera (JB): | 0.687 |
| Skew: | -0.515 | Prob(JB): | 0.709 |
| Kurtosis: | 2.807 | Cond. No. | 47.2 |
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, 03 Dec 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 11:49:51 | 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: | 0.329 |
| Model: | OLS | Adj. R-squared: | 0.277 |
| Method: | Least Squares | F-statistic: | 6.373 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0254 |
| Time: | 11:49:51 | Log-Likelihood: | -72.308 |
| No. Observations: | 15 | AIC: | 148.6 |
| Df Residuals: | 13 | BIC: | 150.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 227.2853 | 53.581 | 4.242 | 0.001 | 111.531 343.040 |
| expression | -25.4473 | 10.080 | -2.524 | 0.025 | -47.225 -3.670 |
| Omnibus: | 2.247 | Durbin-Watson: | 1.610 |
| Prob(Omnibus): | 0.325 | Jarque-Bera (JB): | 1.245 |
| Skew: | 0.704 | Prob(JB): | 0.537 |
| Kurtosis: | 2.902 | Cond. No. | 35.4 |