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 |
| 0.111 | 0.743 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.596 |
| Method: | Least Squares | F-statistic: | 11.82 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.000135 |
| Time: | 23:05:37 | Log-Likelihood: | -100.99 |
| No. Observations: | 23 | AIC: | 210.0 |
| Df Residuals: | 19 | BIC: | 214.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 97.0624 | 178.456 | 0.544 | 0.593 | -276.449 470.574 |
| C(dose)[T.1] | 91.9266 | 388.932 | 0.236 | 0.816 | -722.117 905.970 |
| expression | -4.3217 | 17.986 | -0.240 | 0.813 | -41.966 33.323 |
| expression:C(dose)[T.1] | -3.3879 | 37.326 | -0.091 | 0.929 | -81.512 74.736 |
| Omnibus: | 1.005 | Durbin-Watson: | 1.899 |
| Prob(Omnibus): | 0.605 | Jarque-Bera (JB): | 0.784 |
| Skew: | 0.056 | Prob(JB): | 0.676 |
| Kurtosis: | 2.102 | Cond. No. | 1.06e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.65 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 2.68e-05 |
| Time: | 23:05:37 | Log-Likelihood: | -101.00 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 104.8626 | 152.473 | 0.688 | 0.500 | -213.191 422.916 |
| C(dose)[T.1] | 56.6469 | 13.251 | 4.275 | 0.000 | 29.006 84.288 |
| expression | -5.1083 | 15.364 | -0.332 | 0.743 | -37.158 26.941 |
| Omnibus: | 0.885 | Durbin-Watson: | 1.910 |
| Prob(Omnibus): | 0.642 | Jarque-Bera (JB): | 0.739 |
| Skew: | 0.045 | Prob(JB): | 0.691 |
| Kurtosis: | 2.126 | Cond. No. | 362. |
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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 23:05:37 | 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: | 0.332 |
| Model: | OLS | Adj. R-squared: | 0.300 |
| Method: | Least Squares | F-statistic: | 10.44 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.00400 |
| Time: | 23:05:37 | Log-Likelihood: | -108.46 |
| No. Observations: | 23 | AIC: | 220.9 |
| Df Residuals: | 21 | BIC: | 223.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -372.6310 | 140.119 | -2.659 | 0.015 | -664.025 -81.237 |
| expression | 44.2354 | 13.690 | 3.231 | 0.004 | 15.765 72.706 |
| Omnibus: | 0.833 | Durbin-Watson: | 2.115 |
| Prob(Omnibus): | 0.659 | Jarque-Bera (JB): | 0.774 |
| Skew: | 0.189 | Prob(JB): | 0.679 |
| Kurtosis: | 2.185 | Cond. No. | 245. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.512 | 0.242 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.545 |
| Model: | OLS | Adj. R-squared: | 0.420 |
| Method: | Least Squares | F-statistic: | 4.384 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0292 |
| Time: | 23:05:37 | Log-Likelihood: | -69.401 |
| No. Observations: | 15 | AIC: | 146.8 |
| Df Residuals: | 11 | BIC: | 149.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 277.9146 | 140.574 | 1.977 | 0.074 | -31.486 587.315 |
| C(dose)[T.1] | -128.8681 | 195.010 | -0.661 | 0.522 | -558.082 300.345 |
| expression | -26.2458 | 17.475 | -1.502 | 0.161 | -64.709 12.217 |
| expression:C(dose)[T.1] | 22.1476 | 24.405 | 0.908 | 0.384 | -31.567 75.862 |
| Omnibus: | 2.849 | Durbin-Watson: | 1.177 |
| Prob(Omnibus): | 0.241 | Jarque-Bera (JB): | 1.685 |
| Skew: | -0.819 | Prob(JB): | 0.431 |
| Kurtosis: | 2.890 | Cond. No. | 284. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.510 |
| Model: | OLS | Adj. R-squared: | 0.429 |
| Method: | Least Squares | F-statistic: | 6.256 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0138 |
| Time: | 23:05:37 | Log-Likelihood: | -69.943 |
| 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 | 186.8416 | 97.710 | 1.912 | 0.080 | -26.050 399.734 |
| C(dose)[T.1] | 47.5799 | 14.891 | 3.195 | 0.008 | 15.135 80.024 |
| expression | -14.8898 | 12.108 | -1.230 | 0.242 | -41.272 11.492 |
| Omnibus: | 2.897 | Durbin-Watson: | 0.885 |
| Prob(Omnibus): | 0.235 | Jarque-Bera (JB): | 2.081 |
| Skew: | -0.881 | Prob(JB): | 0.353 |
| Kurtosis: | 2.529 | 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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 23:05:37 | 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: | 0.094 |
| Model: | OLS | Adj. R-squared: | 0.024 |
| Method: | Least Squares | F-statistic: | 1.348 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.266 |
| Time: | 23:05:37 | Log-Likelihood: | -74.560 |
| No. Observations: | 15 | AIC: | 153.1 |
| Df Residuals: | 13 | BIC: | 154.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 239.4112 | 125.890 | 1.902 | 0.080 | -32.558 511.380 |
| expression | -18.3052 | 15.765 | -1.161 | 0.266 | -52.363 15.753 |
| Omnibus: | 4.032 | Durbin-Watson: | 1.823 |
| Prob(Omnibus): | 0.133 | Jarque-Bera (JB): | 1.508 |
| Skew: | 0.349 | Prob(JB): | 0.471 |
| Kurtosis: | 1.613 | Cond. No. | 106. |