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 |
| 0.502 | 0.487 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.660 |
| Model: | OLS | Adj. R-squared: | 0.606 |
| Method: | Least Squares | F-statistic: | 12.29 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000107 |
| Time: | 03:47:04 | Log-Likelihood: | -100.70 |
| No. Observations: | 23 | AIC: | 209.4 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -30.3638 | 191.111 | -0.159 | 0.875 | -430.365 369.637 |
| C(dose)[T.1] | -98.8774 | 417.913 | -0.237 | 0.816 | -973.580 775.825 |
| expression | 8.0609 | 18.206 | 0.443 | 0.663 | -30.045 46.167 |
| expression:C(dose)[T.1] | 13.3289 | 38.187 | 0.349 | 0.731 | -66.598 93.256 |
| Omnibus: | 0.457 | Durbin-Watson: | 1.988 |
| Prob(Omnibus): | 0.796 | Jarque-Bera (JB): | 0.578 |
| Skew: | 0.166 | Prob(JB): | 0.749 |
| Kurtosis: | 2.298 | Cond. No. | 1.21e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 19.21 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.21e-05 |
| Time: | 03:47:04 | Log-Likelihood: | -100.78 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -62.1499 | 164.289 | -0.378 | 0.709 | -404.850 280.550 |
| C(dose)[T.1] | 46.9223 | 12.528 | 3.745 | 0.001 | 20.789 73.055 |
| expression | 11.0905 | 15.649 | 0.709 | 0.487 | -21.552 43.733 |
| Omnibus: | 0.313 | Durbin-Watson: | 1.962 |
| Prob(Omnibus): | 0.855 | Jarque-Bera (JB): | 0.478 |
| Skew: | 0.182 | Prob(JB): | 0.787 |
| Kurtosis: | 2.395 | Cond. No. | 414. |
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: | 03:47:05 | 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: | 0.418 |
| Model: | OLS | Adj. R-squared: | 0.390 |
| Method: | Least Squares | F-statistic: | 15.05 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000865 |
| Time: | 03:47:05 | Log-Likelihood: | -106.89 |
| No. Observations: | 23 | AIC: | 217.8 |
| Df Residuals: | 21 | BIC: | 220.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -495.6882 | 148.407 | -3.340 | 0.003 | -804.318 -187.058 |
| expression | 53.4350 | 13.772 | 3.880 | 0.001 | 24.794 82.076 |
| Omnibus: | 1.824 | Durbin-Watson: | 2.300 |
| Prob(Omnibus): | 0.402 | Jarque-Bera (JB): | 1.131 |
| Skew: | 0.217 | Prob(JB): | 0.568 |
| Kurtosis: | 2.004 | Cond. No. | 293. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.096 | 0.316 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.564 |
| Model: | OLS | Adj. R-squared: | 0.445 |
| Method: | Least Squares | F-statistic: | 4.745 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0233 |
| Time: | 03:47:05 | Log-Likelihood: | -69.073 |
| No. Observations: | 15 | AIC: | 146.1 |
| Df Residuals: | 11 | BIC: | 149.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 377.2516 | 399.291 | 0.945 | 0.365 | -501.581 1256.085 |
| C(dose)[T.1] | -536.3662 | 438.725 | -1.223 | 0.247 | -1501.994 429.262 |
| expression | -33.6604 | 43.365 | -0.776 | 0.454 | -129.106 61.785 |
| expression:C(dose)[T.1] | 62.5788 | 47.359 | 1.321 | 0.213 | -41.658 166.816 |
| Omnibus: | 0.132 | Durbin-Watson: | 1.276 |
| Prob(Omnibus): | 0.936 | Jarque-Bera (JB): | 0.159 |
| Skew: | -0.153 | Prob(JB): | 0.923 |
| Kurtosis: | 2.599 | Cond. No. | 884. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.495 |
| Model: | OLS | Adj. R-squared: | 0.411 |
| Method: | Least Squares | F-statistic: | 5.879 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0166 |
| Time: | 03:47:05 | Log-Likelihood: | -70.177 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 12 | BIC: | 148.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -105.6843 | 165.717 | -0.638 | 0.536 | -466.751 255.382 |
| C(dose)[T.1] | 42.9763 | 16.196 | 2.654 | 0.021 | 7.689 78.264 |
| expression | 18.8077 | 17.964 | 1.047 | 0.316 | -20.333 57.949 |
| Omnibus: | 0.732 | Durbin-Watson: | 1.177 |
| Prob(Omnibus): | 0.694 | Jarque-Bera (JB): | 0.666 |
| Skew: | -0.424 | Prob(JB): | 0.717 |
| Kurtosis: | 2.411 | Cond. No. | 210. |
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: | 03:47:05 | 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: | 0.199 |
| Model: | OLS | Adj. R-squared: | 0.137 |
| Method: | Least Squares | F-statistic: | 3.220 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0960 |
| Time: | 03:47:05 | Log-Likelihood: | -73.640 |
| No. Observations: | 15 | AIC: | 151.3 |
| Df Residuals: | 13 | BIC: | 152.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -246.8067 | 189.952 | -1.299 | 0.216 | -657.173 163.559 |
| expression | 36.2949 | 20.226 | 1.794 | 0.096 | -7.400 79.990 |
| Omnibus: | 0.958 | Durbin-Watson: | 1.667 |
| Prob(Omnibus): | 0.619 | Jarque-Bera (JB): | 0.048 |
| Skew: | 0.027 | Prob(JB): | 0.976 |
| Kurtosis: | 3.272 | Cond. No. | 198. |