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 |
| 0.901 | 0.354 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.664 |
| Model: | OLS | Adj. R-squared: | 0.611 |
| Method: | Least Squares | F-statistic: | 12.53 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.49e-05 |
| Time: | 05:19:42 | Log-Likelihood: | -100.56 |
| No. Observations: | 23 | AIC: | 209.1 |
| Df Residuals: | 19 | BIC: | 213.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 101.8192 | 71.234 | 1.429 | 0.169 | -47.276 250.914 |
| C(dose)[T.1] | 58.8711 | 109.894 | 0.536 | 0.598 | -171.141 288.883 |
| expression | -7.7903 | 11.613 | -0.671 | 0.510 | -32.097 16.516 |
| expression:C(dose)[T.1] | -0.1288 | 17.013 | -0.008 | 0.994 | -35.738 35.480 |
| Omnibus: | 0.018 | Durbin-Watson: | 1.804 |
| Prob(Omnibus): | 0.991 | Jarque-Bera (JB): | 0.218 |
| Skew: | 0.025 | Prob(JB): | 0.897 |
| Kurtosis: | 2.526 | Cond. No. | 210. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.664 |
| Model: | OLS | Adj. R-squared: | 0.631 |
| Method: | Least Squares | F-statistic: | 19.78 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.82e-05 |
| Time: | 05:19:42 | Log-Likelihood: | -100.56 |
| No. Observations: | 23 | AIC: | 207.1 |
| Df Residuals: | 20 | BIC: | 210.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 102.1858 | 50.901 | 2.008 | 0.058 | -3.992 208.364 |
| C(dose)[T.1] | 58.0429 | 9.909 | 5.858 | 0.000 | 37.374 78.712 |
| expression | -7.8503 | 8.272 | -0.949 | 0.354 | -25.105 9.405 |
| Omnibus: | 0.019 | Durbin-Watson: | 1.805 |
| Prob(Omnibus): | 0.991 | Jarque-Bera (JB): | 0.219 |
| Skew: | 0.026 | Prob(JB): | 0.896 |
| Kurtosis: | 2.525 | Cond. No. | 78.7 |
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: | 05:19:42 | 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: | 0.088 |
| Model: | OLS | Adj. R-squared: | 0.045 |
| Method: | Least Squares | F-statistic: | 2.027 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.169 |
| Time: | 05:19:42 | Log-Likelihood: | -112.05 |
| No. Observations: | 23 | AIC: | 228.1 |
| Df Residuals: | 21 | BIC: | 230.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -25.1983 | 74.013 | -0.340 | 0.737 | -179.117 128.721 |
| expression | 16.3976 | 11.517 | 1.424 | 0.169 | -7.554 40.350 |
| Omnibus: | 0.575 | Durbin-Watson: | 2.633 |
| Prob(Omnibus): | 0.750 | Jarque-Bera (JB): | 0.525 |
| Skew: | 0.323 | Prob(JB): | 0.769 |
| Kurtosis: | 2.638 | Cond. No. | 70.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.555 | 0.471 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.606 |
| Model: | OLS | Adj. R-squared: | 0.498 |
| Method: | Least Squares | F-statistic: | 5.629 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0138 |
| Time: | 05:19:42 | Log-Likelihood: | -68.323 |
| No. Observations: | 15 | AIC: | 144.6 |
| Df Residuals: | 11 | BIC: | 147.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 36.4919 | 56.154 | 0.650 | 0.529 | -87.103 160.087 |
| C(dose)[T.1] | 222.8341 | 90.888 | 2.452 | 0.032 | 22.790 422.878 |
| expression | 5.4257 | 9.686 | 0.560 | 0.587 | -15.893 26.744 |
| expression:C(dose)[T.1] | -29.7293 | 15.471 | -1.922 | 0.081 | -63.780 4.322 |
| Omnibus: | 9.415 | Durbin-Watson: | 1.170 |
| Prob(Omnibus): | 0.009 | Jarque-Bera (JB): | 5.809 |
| Skew: | -1.402 | Prob(JB): | 0.0548 |
| Kurtosis: | 4.198 | Cond. No. | 100. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.473 |
| Model: | OLS | Adj. R-squared: | 0.385 |
| Method: | Least Squares | F-statistic: | 5.388 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0214 |
| Time: | 05:19:42 | Log-Likelihood: | -70.494 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 12 | BIC: | 149.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 102.9375 | 48.959 | 2.103 | 0.057 | -3.735 209.610 |
| C(dose)[T.1] | 50.2533 | 15.453 | 3.252 | 0.007 | 16.584 83.922 |
| expression | -6.2276 | 8.357 | -0.745 | 0.471 | -24.436 11.981 |
| Omnibus: | 3.173 | Durbin-Watson: | 0.939 |
| Prob(Omnibus): | 0.205 | Jarque-Bera (JB): | 2.085 |
| Skew: | -0.904 | Prob(JB): | 0.353 |
| Kurtosis: | 2.743 | Cond. No. | 38.6 |
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: | 05:19:42 | 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: | 0.009 |
| Model: | OLS | Adj. R-squared: | -0.067 |
| Method: | Least Squares | F-statistic: | 0.1159 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.739 |
| Time: | 05:19:42 | Log-Likelihood: | -75.234 |
| 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 | 115.2901 | 64.324 | 1.792 | 0.096 | -23.673 254.253 |
| expression | -3.7331 | 10.967 | -0.340 | 0.739 | -27.425 19.959 |
| Omnibus: | 0.524 | Durbin-Watson: | 1.691 |
| Prob(Omnibus): | 0.770 | Jarque-Bera (JB): | 0.554 |
| Skew: | -0.074 | Prob(JB): | 0.758 |
| Kurtosis: | 2.070 | Cond. No. | 38.4 |