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 |
| 1.228 | 0.281 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.705 |
| Model: | OLS | Adj. R-squared: | 0.659 |
| Method: | Least Squares | F-statistic: | 15.17 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.80e-05 |
| Time: | 04:49:12 | Log-Likelihood: | -99.049 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 19 | BIC: | 210.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 65.2479 | 54.593 | 1.195 | 0.247 | -49.016 179.512 |
| C(dose)[T.1] | -71.3821 | 81.304 | -0.878 | 0.391 | -241.554 98.790 |
| expression | -1.8313 | 9.006 | -0.203 | 0.841 | -20.682 17.019 |
| expression:C(dose)[T.1] | 20.1430 | 13.205 | 1.525 | 0.144 | -7.496 47.782 |
| Omnibus: | 0.272 | Durbin-Watson: | 1.984 |
| Prob(Omnibus): | 0.873 | Jarque-Bera (JB): | 0.423 |
| Skew: | 0.202 | Prob(JB): | 0.810 |
| Kurtosis: | 2.473 | Cond. No. | 159. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.669 |
| Model: | OLS | Adj. R-squared: | 0.636 |
| Method: | Least Squares | F-statistic: | 20.24 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.56e-05 |
| Time: | 04:49:13 | Log-Likelihood: | -100.38 |
| No. Observations: | 23 | AIC: | 206.8 |
| Df Residuals: | 20 | BIC: | 210.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 8.7641 | 41.424 | 0.212 | 0.835 | -77.644 95.172 |
| C(dose)[T.1] | 51.9833 | 8.600 | 6.045 | 0.000 | 34.045 69.922 |
| expression | 7.5383 | 6.802 | 1.108 | 0.281 | -6.650 21.726 |
| Omnibus: | 0.764 | Durbin-Watson: | 1.840 |
| Prob(Omnibus): | 0.682 | Jarque-Bera (JB): | 0.694 |
| Skew: | 0.050 | Prob(JB): | 0.707 |
| Kurtosis: | 2.155 | Cond. No. | 61.6 |
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: | 04:49:13 | 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: | 0.065 |
| Model: | OLS | Adj. R-squared: | 0.021 |
| Method: | Least Squares | F-statistic: | 1.467 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.239 |
| Time: | 04:49:13 | Log-Likelihood: | -112.33 |
| No. Observations: | 23 | AIC: | 228.7 |
| Df Residuals: | 21 | BIC: | 230.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -2.0837 | 67.906 | -0.031 | 0.976 | -143.303 139.135 |
| expression | 13.3786 | 11.047 | 1.211 | 0.239 | -9.596 36.353 |
| Omnibus: | 2.864 | Durbin-Watson: | 2.365 |
| Prob(Omnibus): | 0.239 | Jarque-Bera (JB): | 1.261 |
| Skew: | 0.056 | Prob(JB): | 0.532 |
| Kurtosis: | 1.858 | Cond. No. | 61.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.983 | 0.341 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.528 |
| Model: | OLS | Adj. R-squared: | 0.399 |
| Method: | Least Squares | F-statistic: | 4.096 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0353 |
| Time: | 04:49:13 | Log-Likelihood: | -69.675 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 11 | BIC: | 150.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 170.7068 | 77.272 | 2.209 | 0.049 | 0.631 340.782 |
| C(dose)[T.1] | -64.8380 | 123.626 | -0.524 | 0.610 | -336.936 207.260 |
| expression | -17.9239 | 13.271 | -1.351 | 0.204 | -47.134 11.286 |
| expression:C(dose)[T.1] | 19.7895 | 21.284 | 0.930 | 0.372 | -27.056 66.635 |
| Omnibus: | 2.462 | Durbin-Watson: | 0.841 |
| Prob(Omnibus): | 0.292 | Jarque-Bera (JB): | 1.419 |
| Skew: | -0.751 | Prob(JB): | 0.492 |
| Kurtosis: | 2.880 | Cond. No. | 123. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.491 |
| Model: | OLS | Adj. R-squared: | 0.406 |
| Method: | Least Squares | F-statistic: | 5.777 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0175 |
| Time: | 04:49:13 | Log-Likelihood: | -70.242 |
| No. Observations: | 15 | AIC: | 146.5 |
| Df Residuals: | 12 | BIC: | 148.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 126.3742 | 60.464 | 2.090 | 0.059 | -5.364 258.113 |
| C(dose)[T.1] | 49.2326 | 15.132 | 3.254 | 0.007 | 16.263 82.202 |
| expression | -10.2300 | 10.317 | -0.992 | 0.341 | -32.708 12.248 |
| Omnibus: | 1.925 | Durbin-Watson: | 0.696 |
| Prob(Omnibus): | 0.382 | Jarque-Bera (JB): | 1.147 |
| Skew: | -0.669 | Prob(JB): | 0.564 |
| Kurtosis: | 2.784 | Cond. No. | 48.1 |
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: | 04:49:13 | 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: | 0.041 |
| Model: | OLS | Adj. R-squared: | -0.033 |
| Method: | Least Squares | F-statistic: | 0.5570 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.469 |
| Time: | 04:49:13 | Log-Likelihood: | -74.985 |
| No. Observations: | 15 | AIC: | 154.0 |
| Df Residuals: | 13 | BIC: | 155.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 152.1649 | 79.008 | 1.926 | 0.076 | -18.522 322.852 |
| expression | -10.1490 | 13.598 | -0.746 | 0.469 | -39.526 19.228 |
| Omnibus: | 2.524 | Durbin-Watson: | 1.628 |
| Prob(Omnibus): | 0.283 | Jarque-Bera (JB): | 1.251 |
| Skew: | 0.346 | Prob(JB): | 0.535 |
| Kurtosis: | 1.766 | Cond. No. | 47.5 |