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 |
| 0.100 | 0.755 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.598 |
| Method: | Least Squares | F-statistic: | 11.91 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000129 |
| Time: | 04:45:54 | Log-Likelihood: | -100.94 |
| No. Observations: | 23 | AIC: | 209.9 |
| Df Residuals: | 19 | BIC: | 214.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 83.7924 | 65.902 | 1.271 | 0.219 | -54.143 221.727 |
| C(dose)[T.1] | 25.4524 | 80.224 | 0.317 | 0.755 | -142.458 193.362 |
| expression | -5.7642 | 12.784 | -0.451 | 0.657 | -32.521 20.992 |
| expression:C(dose)[T.1] | 5.3840 | 16.313 | 0.330 | 0.745 | -28.759 39.527 |
| Omnibus: | 0.041 | Durbin-Watson: | 1.826 |
| Prob(Omnibus): | 0.980 | Jarque-Bera (JB): | 0.251 |
| Skew: | 0.045 | Prob(JB): | 0.882 |
| Kurtosis: | 2.496 | Cond. No. | 124. |
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: | 18.64 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.70e-05 |
| Time: | 04:45:54 | Log-Likelihood: | -101.01 |
| 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 | 66.8220 | 40.294 | 1.658 | 0.113 | -17.231 150.875 |
| C(dose)[T.1] | 51.7075 | 10.150 | 5.094 | 0.000 | 30.536 72.879 |
| expression | -2.4577 | 7.762 | -0.317 | 0.755 | -18.649 13.734 |
| Omnibus: | 0.406 | Durbin-Watson: | 1.875 |
| Prob(Omnibus): | 0.816 | Jarque-Bera (JB): | 0.542 |
| Skew: | 0.134 | Prob(JB): | 0.763 |
| Kurtosis: | 2.297 | Cond. No. | 47.3 |
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: | 04:45:54 | 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: | 0.198 |
| Model: | OLS | Adj. R-squared: | 0.159 |
| Method: | Least Squares | F-statistic: | 5.173 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0335 |
| Time: | 04:45:54 | Log-Likelihood: | -110.57 |
| No. Observations: | 23 | AIC: | 225.1 |
| Df Residuals: | 21 | BIC: | 227.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 188.1077 | 48.090 | 3.912 | 0.001 | 88.098 288.117 |
| expression | -22.5098 | 9.896 | -2.275 | 0.034 | -43.091 -1.929 |
| Omnibus: | 1.364 | Durbin-Watson: | 2.223 |
| Prob(Omnibus): | 0.506 | Jarque-Bera (JB): | 0.984 |
| Skew: | 0.493 | Prob(JB): | 0.611 |
| Kurtosis: | 2.770 | Cond. No. | 37.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.912 | 0.358 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.494 |
| Model: | OLS | Adj. R-squared: | 0.356 |
| Method: | Least Squares | F-statistic: | 3.580 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0503 |
| Time: | 04:45:54 | Log-Likelihood: | -70.191 |
| No. Observations: | 15 | AIC: | 148.4 |
| Df Residuals: | 11 | BIC: | 151.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -2.7673 | 72.245 | -0.038 | 0.970 | -161.778 156.244 |
| C(dose)[T.1] | 102.3512 | 159.125 | 0.643 | 0.533 | -247.881 452.583 |
| expression | 11.9243 | 12.116 | 0.984 | 0.346 | -14.743 38.591 |
| expression:C(dose)[T.1] | -9.2738 | 25.105 | -0.369 | 0.719 | -64.530 45.982 |
| Omnibus: | 2.419 | Durbin-Watson: | 0.748 |
| Prob(Omnibus): | 0.298 | Jarque-Bera (JB): | 1.591 |
| Skew: | -0.782 | Prob(JB): | 0.451 |
| Kurtosis: | 2.687 | Cond. No. | 156. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.488 |
| Model: | OLS | Adj. R-squared: | 0.402 |
| Method: | Least Squares | F-statistic: | 5.712 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0181 |
| Time: | 04:45:54 | Log-Likelihood: | -70.283 |
| No. Observations: | 15 | AIC: | 146.6 |
| Df Residuals: | 12 | BIC: | 148.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 9.9480 | 61.190 | 0.163 | 0.874 | -123.374 143.270 |
| C(dose)[T.1] | 43.8979 | 16.156 | 2.717 | 0.019 | 8.698 79.098 |
| expression | 9.7643 | 10.223 | 0.955 | 0.358 | -12.509 32.037 |
| Omnibus: | 2.875 | Durbin-Watson: | 0.701 |
| Prob(Omnibus): | 0.238 | Jarque-Bera (JB): | 1.786 |
| Skew: | -0.840 | Prob(JB): | 0.409 |
| Kurtosis: | 2.816 | Cond. No. | 51.9 |
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: | 04:45:54 | 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: | 0.173 |
| Model: | OLS | Adj. R-squared: | 0.109 |
| Method: | Least Squares | F-statistic: | 2.711 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.124 |
| Time: | 04:45:54 | Log-Likelihood: | -73.880 |
| No. Observations: | 15 | AIC: | 151.8 |
| Df Residuals: | 13 | BIC: | 153.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -25.5456 | 72.995 | -0.350 | 0.732 | -183.242 132.151 |
| expression | 19.3018 | 11.724 | 1.646 | 0.124 | -6.025 44.629 |
| Omnibus: | 2.687 | Durbin-Watson: | 1.367 |
| Prob(Omnibus): | 0.261 | Jarque-Bera (JB): | 1.139 |
| Skew: | 0.196 | Prob(JB): | 0.566 |
| Kurtosis: | 1.708 | Cond. No. | 50.4 |