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 |
| 2.508 | 0.129 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.703 |
| Model: | OLS | Adj. R-squared: | 0.656 |
| Method: | Least Squares | F-statistic: | 14.97 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.05e-05 |
| Time: | 03:31:20 | Log-Likelihood: | -99.154 |
| No. Observations: | 23 | AIC: | 206.3 |
| Df Residuals: | 19 | BIC: | 210.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 70.1189 | 100.213 | 0.700 | 0.493 | -139.630 279.868 |
| C(dose)[T.1] | -49.6251 | 110.924 | -0.447 | 0.660 | -281.792 182.542 |
| expression | -2.7338 | 17.191 | -0.159 | 0.875 | -38.714 33.247 |
| expression:C(dose)[T.1] | 18.5289 | 19.204 | 0.965 | 0.347 | -21.666 58.723 |
| Omnibus: | 0.473 | Durbin-Watson: | 1.664 |
| Prob(Omnibus): | 0.789 | Jarque-Bera (JB): | 0.576 |
| Skew: | 0.116 | Prob(JB): | 0.750 |
| Kurtosis: | 2.260 | Cond. No. | 232. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.688 |
| Model: | OLS | Adj. R-squared: | 0.657 |
| Method: | Least Squares | F-statistic: | 22.07 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.70e-06 |
| Time: | 03:31:20 | Log-Likelihood: | -99.704 |
| No. Observations: | 23 | AIC: | 205.4 |
| Df Residuals: | 20 | BIC: | 208.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -16.2932 | 44.885 | -0.363 | 0.720 | -109.922 77.336 |
| C(dose)[T.1] | 57.0766 | 8.598 | 6.639 | 0.000 | 39.143 75.011 |
| expression | 12.1137 | 7.649 | 1.584 | 0.129 | -3.843 28.070 |
| Omnibus: | 1.035 | Durbin-Watson: | 1.908 |
| Prob(Omnibus): | 0.596 | Jarque-Bera (JB): | 0.855 |
| Skew: | 0.190 | Prob(JB): | 0.652 |
| Kurtosis: | 2.136 | Cond. No. | 64.1 |
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: | 03:31:20 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.02038 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.888 |
| Time: | 03:31:20 | Log-Likelihood: | -113.09 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 90.1222 | 73.233 | 1.231 | 0.232 | -62.173 242.418 |
| expression | -1.8343 | 12.848 | -0.143 | 0.888 | -28.552 24.884 |
| Omnibus: | 3.171 | Durbin-Watson: | 2.483 |
| Prob(Omnibus): | 0.205 | Jarque-Bera (JB): | 1.580 |
| Skew: | 0.312 | Prob(JB): | 0.454 |
| Kurtosis: | 1.878 | Cond. No. | 59.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.735 | 0.408 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.507 |
| Model: | OLS | Adj. R-squared: | 0.373 |
| Method: | Least Squares | F-statistic: | 3.778 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0438 |
| Time: | 03:31:20 | Log-Likelihood: | -69.989 |
| No. Observations: | 15 | AIC: | 148.0 |
| Df Residuals: | 11 | BIC: | 150.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -144.9710 | 186.548 | -0.777 | 0.453 | -555.560 265.618 |
| C(dose)[T.1] | 244.2672 | 259.083 | 0.943 | 0.366 | -325.970 814.505 |
| expression | 32.1072 | 28.147 | 1.141 | 0.278 | -29.844 94.059 |
| expression:C(dose)[T.1] | -29.6095 | 38.229 | -0.775 | 0.455 | -113.751 54.532 |
| Omnibus: | 2.035 | Durbin-Watson: | 0.835 |
| Prob(Omnibus): | 0.362 | Jarque-Bera (JB): | 1.420 |
| Skew: | -0.723 | Prob(JB): | 0.492 |
| Kurtosis: | 2.578 | Cond. No. | 314. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.481 |
| Model: | OLS | Adj. R-squared: | 0.394 |
| Method: | Least Squares | F-statistic: | 5.552 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0196 |
| Time: | 03:31:20 | Log-Likelihood: | -70.387 |
| No. Observations: | 15 | AIC: | 146.8 |
| Df Residuals: | 12 | BIC: | 148.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -38.7871 | 124.383 | -0.312 | 0.761 | -309.795 232.221 |
| C(dose)[T.1] | 44.0190 | 16.429 | 2.679 | 0.020 | 8.224 79.814 |
| expression | 16.0560 | 18.727 | 0.857 | 0.408 | -24.746 56.858 |
| Omnibus: | 1.904 | Durbin-Watson: | 0.724 |
| Prob(Omnibus): | 0.386 | Jarque-Bera (JB): | 1.448 |
| Skew: | -0.610 | Prob(JB): | 0.485 |
| Kurtosis: | 2.090 | Cond. No. | 114. |
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: | 03:31:20 | 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: | 0.170 |
| Model: | OLS | Adj. R-squared: | 0.106 |
| Method: | Least Squares | F-statistic: | 2.660 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.127 |
| Time: | 03:31:20 | Log-Likelihood: | -73.904 |
| 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 | -140.4869 | 143.874 | -0.976 | 0.347 | -451.307 170.334 |
| expression | 34.4988 | 21.154 | 1.631 | 0.127 | -11.201 80.198 |
| Omnibus: | 2.053 | Durbin-Watson: | 1.556 |
| Prob(Omnibus): | 0.358 | Jarque-Bera (JB): | 1.354 |
| Skew: | 0.504 | Prob(JB): | 0.508 |
| Kurtosis: | 1.928 | Cond. No. | 108. |