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 |
| 0.385 | 0.542 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.609 |
| Method: | Least Squares | F-statistic: | 12.41 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000100 |
| Time: | 05:08:02 | Log-Likelihood: | -100.62 |
| No. Observations: | 23 | AIC: | 209.2 |
| Df Residuals: | 19 | BIC: | 213.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 48.6847 | 49.115 | 0.991 | 0.334 | -54.114 151.484 |
| C(dose)[T.1] | 5.7392 | 79.640 | 0.072 | 0.943 | -160.949 172.427 |
| expression | 0.9687 | 8.547 | 0.113 | 0.911 | -16.920 18.858 |
| expression:C(dose)[T.1] | 8.4484 | 13.974 | 0.605 | 0.553 | -20.800 37.697 |
| Omnibus: | 0.429 | Durbin-Watson: | 1.860 |
| Prob(Omnibus): | 0.807 | Jarque-Bera (JB): | 0.563 |
| Skew: | 0.212 | Prob(JB): | 0.755 |
| Kurtosis: | 2.361 | Cond. No. | 130. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 19.04 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.34e-05 |
| Time: | 05:08:02 | Log-Likelihood: | -100.84 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 30.6649 | 38.413 | 0.798 | 0.434 | -49.462 110.792 |
| C(dose)[T.1] | 53.5889 | 8.696 | 6.162 | 0.000 | 35.449 71.729 |
| expression | 4.1290 | 6.654 | 0.621 | 0.542 | -9.751 18.009 |
| Omnibus: | 0.383 | Durbin-Watson: | 1.951 |
| Prob(Omnibus): | 0.826 | Jarque-Bera (JB): | 0.524 |
| Skew: | 0.095 | Prob(JB): | 0.770 |
| Kurtosis: | 2.286 | Cond. No. | 52.3 |
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: | 05:08:02 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.046 |
| Method: | Least Squares | F-statistic: | 0.04025 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.843 |
| Time: | 05:08:02 | Log-Likelihood: | -113.08 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 67.1481 | 63.061 | 1.065 | 0.299 | -63.995 198.292 |
| expression | 2.2157 | 11.044 | 0.201 | 0.843 | -20.751 25.182 |
| Omnibus: | 3.054 | Durbin-Watson: | 2.522 |
| Prob(Omnibus): | 0.217 | Jarque-Bera (JB): | 1.506 |
| Skew: | 0.281 | Prob(JB): | 0.471 |
| Kurtosis: | 1.880 | Cond. No. | 51.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.226 | 0.098 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.588 |
| Model: | OLS | Adj. R-squared: | 0.476 |
| Method: | Least Squares | F-statistic: | 5.232 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0174 |
| Time: | 05:08:02 | Log-Likelihood: | -68.650 |
| No. Observations: | 15 | AIC: | 145.3 |
| Df Residuals: | 11 | BIC: | 148.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 17.2889 | 98.234 | 0.176 | 0.863 | -198.922 233.500 |
| C(dose)[T.1] | -52.8453 | 128.269 | -0.412 | 0.688 | -335.163 229.472 |
| expression | 7.9101 | 15.411 | 0.513 | 0.618 | -26.009 41.829 |
| expression:C(dose)[T.1] | 15.3511 | 19.855 | 0.773 | 0.456 | -28.350 59.052 |
| Omnibus: | 2.167 | Durbin-Watson: | 0.547 |
| Prob(Omnibus): | 0.338 | Jarque-Bera (JB): | 1.652 |
| Skew: | -0.748 | Prob(JB): | 0.438 |
| Kurtosis: | 2.365 | Cond. No. | 167. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.566 |
| Model: | OLS | Adj. R-squared: | 0.493 |
| Method: | Least Squares | F-statistic: | 7.811 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00672 |
| Time: | 05:08:02 | Log-Likelihood: | -69.047 |
| No. Observations: | 15 | AIC: | 144.1 |
| Df Residuals: | 12 | BIC: | 146.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -41.3298 | 61.407 | -0.673 | 0.514 | -175.124 92.464 |
| C(dose)[T.1] | 45.7036 | 14.108 | 3.240 | 0.007 | 14.965 76.442 |
| expression | 17.1578 | 9.553 | 1.796 | 0.098 | -3.656 37.972 |
| Omnibus: | 2.798 | Durbin-Watson: | 0.586 |
| Prob(Omnibus): | 0.247 | Jarque-Bera (JB): | 1.887 |
| Skew: | -0.691 | Prob(JB): | 0.389 |
| Kurtosis: | 1.947 | Cond. No. | 58.7 |
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: | 05:08:02 | 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: | 0.186 |
| Model: | OLS | Adj. R-squared: | 0.123 |
| Method: | Least Squares | F-statistic: | 2.963 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.109 |
| Time: | 05:08:02 | Log-Likelihood: | -73.760 |
| No. Observations: | 15 | AIC: | 151.5 |
| Df Residuals: | 13 | BIC: | 152.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -44.4584 | 80.767 | -0.550 | 0.591 | -218.945 130.028 |
| expression | 21.4237 | 12.446 | 1.721 | 0.109 | -5.465 48.312 |
| Omnibus: | 0.184 | Durbin-Watson: | 1.943 |
| Prob(Omnibus): | 0.912 | Jarque-Bera (JB): | 0.384 |
| Skew: | -0.100 | Prob(JB): | 0.825 |
| Kurtosis: | 2.242 | Cond. No. | 58.5 |