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 |
| 1.534 | 0.230 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.677 |
| Model: | OLS | Adj. R-squared: | 0.626 |
| Method: | Least Squares | F-statistic: | 13.26 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.65e-05 |
| Time: | 05:08:38 | Log-Likelihood: | -100.12 |
| No. Observations: | 23 | AIC: | 208.2 |
| Df Residuals: | 19 | BIC: | 212.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 135.7342 | 76.855 | 1.766 | 0.093 | -25.124 296.593 |
| C(dose)[T.1] | 15.5964 | 98.916 | 0.158 | 0.876 | -191.437 222.630 |
| expression | -23.0698 | 21.682 | -1.064 | 0.301 | -68.451 22.312 |
| expression:C(dose)[T.1] | 11.0800 | 27.531 | 0.402 | 0.692 | -46.544 68.704 |
| Omnibus: | 2.267 | Durbin-Watson: | 1.775 |
| Prob(Omnibus): | 0.322 | Jarque-Bera (JB): | 1.252 |
| Skew: | 0.223 | Prob(JB): | 0.535 |
| Kurtosis: | 1.948 | Cond. No. | 122. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.674 |
| Model: | OLS | Adj. R-squared: | 0.641 |
| Method: | Least Squares | F-statistic: | 20.68 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.35e-05 |
| Time: | 05:08:38 | Log-Likelihood: | -100.21 |
| No. Observations: | 23 | AIC: | 206.4 |
| Df Residuals: | 20 | BIC: | 209.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 111.4488 | 46.587 | 2.392 | 0.027 | 14.270 208.628 |
| C(dose)[T.1] | 55.2483 | 8.591 | 6.431 | 0.000 | 37.327 73.170 |
| expression | -16.1976 | 13.079 | -1.238 | 0.230 | -43.479 11.084 |
| Omnibus: | 2.362 | Durbin-Watson: | 1.793 |
| Prob(Omnibus): | 0.307 | Jarque-Bera (JB): | 1.383 |
| Skew: | 0.305 | Prob(JB): | 0.501 |
| Kurtosis: | 1.965 | Cond. No. | 43.2 |
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: | 05:08:38 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.002462 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.961 |
| Time: | 05:08:38 | Log-Likelihood: | -113.10 |
| 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 | 83.6351 | 79.285 | 1.055 | 0.303 | -81.247 248.517 |
| expression | -1.0912 | 21.991 | -0.050 | 0.961 | -46.825 44.642 |
| Omnibus: | 3.247 | Durbin-Watson: | 2.494 |
| Prob(Omnibus): | 0.197 | Jarque-Bera (JB): | 1.566 |
| Skew: | 0.294 | Prob(JB): | 0.457 |
| Kurtosis: | 1.865 | Cond. No. | 42.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.417 | 0.146 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.566 |
| Model: | OLS | Adj. R-squared: | 0.448 |
| Method: | Least Squares | F-statistic: | 4.785 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0227 |
| Time: | 05:08:38 | Log-Likelihood: | -69.037 |
| No. Observations: | 15 | AIC: | 146.1 |
| Df Residuals: | 11 | BIC: | 148.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -147.4480 | 146.823 | -1.004 | 0.337 | -470.604 175.707 |
| C(dose)[T.1] | 183.2948 | 171.988 | 1.066 | 0.309 | -195.248 561.838 |
| expression | 70.3341 | 47.932 | 1.467 | 0.170 | -35.164 175.832 |
| expression:C(dose)[T.1] | -44.4102 | 55.799 | -0.796 | 0.443 | -167.224 78.403 |
| Omnibus: | 2.328 | Durbin-Watson: | 1.185 |
| Prob(Omnibus): | 0.312 | Jarque-Bera (JB): | 1.104 |
| Skew: | -0.663 | Prob(JB): | 0.576 |
| Kurtosis: | 3.087 | Cond. No. | 120. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.541 |
| Model: | OLS | Adj. R-squared: | 0.465 |
| Method: | Least Squares | F-statistic: | 7.077 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00933 |
| Time: | 05:08:38 | Log-Likelihood: | -69.457 |
| No. Observations: | 15 | AIC: | 144.9 |
| Df Residuals: | 12 | BIC: | 147.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -47.3328 | 74.557 | -0.635 | 0.537 | -209.779 115.114 |
| C(dose)[T.1] | 46.9090 | 14.435 | 3.250 | 0.007 | 15.458 78.360 |
| expression | 37.5641 | 24.162 | 1.555 | 0.146 | -15.080 90.208 |
| Omnibus: | 1.748 | Durbin-Watson: | 0.841 |
| Prob(Omnibus): | 0.417 | Jarque-Bera (JB): | 0.831 |
| Skew: | -0.576 | Prob(JB): | 0.660 |
| Kurtosis: | 2.974 | Cond. No. | 36.1 |
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: | 05:08:38 | 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: | 0.137 |
| Model: | OLS | Adj. R-squared: | 0.071 |
| Method: | Least Squares | F-statistic: | 2.071 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.174 |
| Time: | 05:08:38 | Log-Likelihood: | -74.191 |
| No. Observations: | 15 | AIC: | 152.4 |
| Df Residuals: | 13 | BIC: | 153.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -47.0245 | 98.218 | -0.479 | 0.640 | -259.212 165.163 |
| expression | 45.5671 | 31.664 | 1.439 | 0.174 | -22.838 113.973 |
| Omnibus: | 1.015 | Durbin-Watson: | 1.900 |
| Prob(Omnibus): | 0.602 | Jarque-Bera (JB): | 0.903 |
| Skew: | 0.468 | Prob(JB): | 0.637 |
| Kurtosis: | 2.246 | Cond. No. | 35.6 |