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 |
| 5.194 | 0.034 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.755 |
| Model: | OLS | Adj. R-squared: | 0.717 |
| Method: | Least Squares | F-statistic: | 19.54 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.98e-06 |
| Time: | 04:32:06 | Log-Likelihood: | -96.922 |
| No. Observations: | 23 | AIC: | 201.8 |
| Df Residuals: | 19 | BIC: | 206.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 6.9440 | 57.031 | 0.122 | 0.904 | -112.423 126.311 |
| C(dose)[T.1] | -90.2664 | 90.064 | -1.002 | 0.329 | -278.772 98.240 |
| expression | 7.4005 | 8.893 | 0.832 | 0.416 | -11.212 26.013 |
| expression:C(dose)[T.1] | 22.9779 | 14.192 | 1.619 | 0.122 | -6.727 52.683 |
| Omnibus: | 0.526 | Durbin-Watson: | 1.875 |
| Prob(Omnibus): | 0.769 | Jarque-Bera (JB): | 0.599 |
| Skew: | -0.097 | Prob(JB): | 0.741 |
| Kurtosis: | 2.234 | Cond. No. | 193. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.721 |
| Model: | OLS | Adj. R-squared: | 0.694 |
| Method: | Least Squares | F-statistic: | 25.89 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.82e-06 |
| Time: | 04:32:06 | Log-Likelihood: | -98.408 |
| No. Observations: | 23 | AIC: | 202.8 |
| Df Residuals: | 20 | BIC: | 206.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -50.6717 | 46.338 | -1.094 | 0.287 | -147.330 45.987 |
| C(dose)[T.1] | 55.0388 | 7.849 | 7.012 | 0.000 | 38.665 71.412 |
| expression | 16.4218 | 7.206 | 2.279 | 0.034 | 1.391 31.453 |
| Omnibus: | 5.489 | Durbin-Watson: | 1.770 |
| Prob(Omnibus): | 0.064 | Jarque-Bera (JB): | 1.714 |
| Skew: | -0.116 | Prob(JB): | 0.424 |
| Kurtosis: | 1.683 | Cond. No. | 77.6 |
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: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 04:32:06 | 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: | 0.037 |
| Model: | OLS | Adj. R-squared: | -0.009 |
| Method: | Least Squares | F-statistic: | 0.7961 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.382 |
| Time: | 04:32:06 | Log-Likelihood: | -112.68 |
| No. Observations: | 23 | AIC: | 229.4 |
| Df Residuals: | 21 | BIC: | 231.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 6.1097 | 82.801 | 0.074 | 0.942 | -166.084 178.304 |
| expression | 11.6154 | 13.018 | 0.892 | 0.382 | -15.458 38.688 |
| Omnibus: | 2.907 | Durbin-Watson: | 2.510 |
| Prob(Omnibus): | 0.234 | Jarque-Bera (JB): | 1.318 |
| Skew: | 0.145 | Prob(JB): | 0.517 |
| Kurtosis: | 1.863 | Cond. No. | 76.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 4.850 | 0.048 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.609 |
| Model: | OLS | Adj. R-squared: | 0.502 |
| Method: | Least Squares | F-statistic: | 5.705 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0132 |
| Time: | 04:32:06 | Log-Likelihood: | -68.262 |
| No. Observations: | 15 | AIC: | 144.5 |
| Df Residuals: | 11 | BIC: | 147.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 286.4559 | 132.010 | 2.170 | 0.053 | -4.096 577.007 |
| C(dose)[T.1] | 2.2184 | 186.129 | 0.012 | 0.991 | -407.449 411.886 |
| expression | -28.3062 | 17.010 | -1.664 | 0.124 | -65.745 9.133 |
| expression:C(dose)[T.1] | 4.7499 | 24.707 | 0.192 | 0.851 | -49.630 59.129 |
| Omnibus: | 3.919 | Durbin-Watson: | 0.877 |
| Prob(Omnibus): | 0.141 | Jarque-Bera (JB): | 2.062 |
| Skew: | -0.899 | Prob(JB): | 0.357 |
| Kurtosis: | 3.255 | Cond. No. | 271. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.607 |
| Model: | OLS | Adj. R-squared: | 0.542 |
| Method: | Least Squares | F-statistic: | 9.284 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00366 |
| Time: | 04:32:06 | Log-Likelihood: | -68.287 |
| No. Observations: | 15 | AIC: | 142.6 |
| Df Residuals: | 12 | BIC: | 144.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 269.0344 | 92.061 | 2.922 | 0.013 | 68.451 469.618 |
| C(dose)[T.1] | 37.8876 | 14.241 | 2.660 | 0.021 | 6.859 68.916 |
| expression | -26.0547 | 11.831 | -2.202 | 0.048 | -51.833 -0.276 |
| Omnibus: | 3.837 | Durbin-Watson: | 0.860 |
| Prob(Omnibus): | 0.147 | Jarque-Bera (JB): | 2.013 |
| Skew: | -0.889 | Prob(JB): | 0.365 |
| Kurtosis: | 3.244 | Cond. No. | 107. |
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: | 04:32:06 | 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: | 0.376 |
| Model: | OLS | Adj. R-squared: | 0.328 |
| Method: | Least Squares | F-statistic: | 7.829 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0151 |
| Time: | 04:32:06 | Log-Likelihood: | -71.765 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 13 | BIC: | 148.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 374.4419 | 100.668 | 3.720 | 0.003 | 156.961 591.923 |
| expression | -37.4052 | 13.368 | -2.798 | 0.015 | -66.286 -8.524 |
| Omnibus: | 2.193 | Durbin-Watson: | 1.929 |
| Prob(Omnibus): | 0.334 | Jarque-Bera (JB): | 1.213 |
| Skew: | 0.372 | Prob(JB): | 0.545 |
| Kurtosis: | 1.823 | Cond. No. | 96.1 |