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 |
| 1.162 | 0.294 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.692 |
| Model: | OLS | Adj. R-squared: | 0.643 |
| Method: | Least Squares | F-statistic: | 14.24 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.23e-05 |
| Time: | 04:44:10 | Log-Likelihood: | -99.558 |
| No. Observations: | 23 | AIC: | 207.1 |
| Df Residuals: | 19 | BIC: | 211.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 2.2577 | 89.619 | 0.025 | 0.980 | -185.317 189.833 |
| C(dose)[T.1] | 175.7080 | 101.030 | 1.739 | 0.098 | -35.750 387.166 |
| expression | 7.8973 | 13.595 | 0.581 | 0.568 | -20.557 36.351 |
| expression:C(dose)[T.1] | -18.5074 | 15.276 | -1.212 | 0.241 | -50.480 13.466 |
| Omnibus: | 3.390 | Durbin-Watson: | 1.994 |
| Prob(Omnibus): | 0.184 | Jarque-Bera (JB): | 1.493 |
| Skew: | 0.224 | Prob(JB): | 0.474 |
| Kurtosis: | 1.835 | Cond. No. | 241. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.635 |
| Method: | Least Squares | F-statistic: | 20.15 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.61e-05 |
| Time: | 04:44:10 | Log-Likelihood: | -100.41 |
| 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 | 98.6788 | 41.681 | 2.367 | 0.028 | 11.733 185.624 |
| C(dose)[T.1] | 53.7348 | 8.534 | 6.297 | 0.000 | 35.934 71.536 |
| expression | -6.7602 | 6.272 | -1.078 | 0.294 | -19.844 6.324 |
| Omnibus: | 1.433 | Durbin-Watson: | 2.014 |
| Prob(Omnibus): | 0.489 | Jarque-Bera (JB): | 1.102 |
| Skew: | 0.302 | Prob(JB): | 0.576 |
| Kurtosis: | 2.114 | Cond. No. | 66.6 |
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: | 04:44:10 | 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: | 0.011 |
| Model: | OLS | Adj. R-squared: | -0.036 |
| Method: | Least Squares | F-statistic: | 0.2288 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.637 |
| Time: | 04:44:10 | Log-Likelihood: | -112.98 |
| No. Observations: | 23 | AIC: | 230.0 |
| Df Residuals: | 21 | BIC: | 232.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 113.0953 | 70.142 | 1.612 | 0.122 | -32.772 258.963 |
| expression | -5.0523 | 10.561 | -0.478 | 0.637 | -27.016 16.911 |
| Omnibus: | 3.411 | Durbin-Watson: | 2.578 |
| Prob(Omnibus): | 0.182 | Jarque-Bera (JB): | 1.518 |
| Skew: | 0.240 | Prob(JB): | 0.468 |
| Kurtosis: | 1.836 | Cond. No. | 66.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.003 | 0.959 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.303 |
| Method: | Least Squares | F-statistic: | 3.026 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0754 |
| Time: | 04:44:10 | Log-Likelihood: | -70.787 |
| No. Observations: | 15 | AIC: | 149.6 |
| Df Residuals: | 11 | BIC: | 152.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 120.7519 | 225.453 | 0.536 | 0.603 | -375.467 616.971 |
| C(dose)[T.1] | -20.8914 | 275.978 | -0.076 | 0.941 | -628.315 586.532 |
| expression | -7.0078 | 29.587 | -0.237 | 0.817 | -72.129 58.114 |
| expression:C(dose)[T.1] | 9.2214 | 36.262 | 0.254 | 0.804 | -70.591 89.034 |
| Omnibus: | 2.457 | Durbin-Watson: | 0.859 |
| Prob(Omnibus): | 0.293 | Jarque-Bera (JB): | 1.735 |
| Skew: | -0.803 | Prob(JB): | 0.420 |
| Kurtosis: | 2.560 | Cond. No. | 374. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.887 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0280 |
| Time: | 04:44:11 | Log-Likelihood: | -70.831 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 74.0384 | 125.512 | 0.590 | 0.566 | -199.430 347.506 |
| C(dose)[T.1] | 49.1654 | 15.749 | 3.122 | 0.009 | 14.852 83.479 |
| expression | -0.8687 | 16.426 | -0.053 | 0.959 | -36.657 34.920 |
| Omnibus: | 2.727 | Durbin-Watson: | 0.806 |
| Prob(Omnibus): | 0.256 | Jarque-Bera (JB): | 1.882 |
| Skew: | -0.846 | Prob(JB): | 0.390 |
| Kurtosis: | 2.616 | Cond. No. | 124. |
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: | 04:44:11 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.076 |
| Method: | Least Squares | F-statistic: | 0.01714 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.898 |
| Time: | 04:44:11 | Log-Likelihood: | -75.290 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 114.7623 | 161.453 | 0.711 | 0.490 | -234.036 463.561 |
| expression | -2.7794 | 21.229 | -0.131 | 0.898 | -48.643 43.084 |
| Omnibus: | 0.739 | Durbin-Watson: | 1.614 |
| Prob(Omnibus): | 0.691 | Jarque-Bera (JB): | 0.629 |
| Skew: | 0.049 | Prob(JB): | 0.730 |
| Kurtosis: | 2.002 | Cond. No. | 123. |