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 |
| 1.123 | 0.302 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 12.73 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.60e-05 |
| Time: | 03:56:24 | Log-Likelihood: | -100.43 |
| No. Observations: | 23 | AIC: | 208.9 |
| Df Residuals: | 19 | BIC: | 213.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -3.9548 | 68.013 | -0.058 | 0.954 | -146.307 138.398 |
| C(dose)[T.1] | 56.5063 | 117.613 | 0.480 | 0.636 | -189.661 302.674 |
| expression | 10.0883 | 11.750 | 0.859 | 0.401 | -14.504 34.681 |
| expression:C(dose)[T.1] | -0.3894 | 20.572 | -0.019 | 0.985 | -43.446 42.667 |
| Omnibus: | 0.179 | Durbin-Watson: | 1.629 |
| Prob(Omnibus): | 0.915 | Jarque-Bera (JB): | 0.387 |
| Skew: | 0.084 | Prob(JB): | 0.824 |
| Kurtosis: | 2.388 | Cond. No. | 191. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.634 |
| Method: | Least Squares | F-statistic: | 20.09 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.64e-05 |
| Time: | 03:56:24 | Log-Likelihood: | -100.43 |
| No. Observations: | 23 | AIC: | 206.9 |
| Df Residuals: | 20 | BIC: | 210.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -3.2224 | 54.518 | -0.059 | 0.953 | -116.946 110.501 |
| C(dose)[T.1] | 54.2861 | 8.580 | 6.327 | 0.000 | 36.388 72.185 |
| expression | 9.9612 | 9.401 | 1.060 | 0.302 | -9.648 29.570 |
| Omnibus: | 0.184 | Durbin-Watson: | 1.634 |
| Prob(Omnibus): | 0.912 | Jarque-Bera (JB): | 0.390 |
| Skew: | 0.090 | Prob(JB): | 0.823 |
| Kurtosis: | 2.388 | Cond. No. | 75.9 |
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: | 03:56:24 | 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: | 0.003 |
| Model: | OLS | Adj. R-squared: | -0.045 |
| Method: | Least Squares | F-statistic: | 0.05639 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.815 |
| Time: | 03:56:24 | Log-Likelihood: | -113.07 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 58.2486 | 90.699 | 0.642 | 0.528 | -130.369 246.866 |
| expression | 3.7534 | 15.807 | 0.237 | 0.815 | -29.118 36.625 |
| Omnibus: | 2.942 | Durbin-Watson: | 2.441 |
| Prob(Omnibus): | 0.230 | Jarque-Bera (JB): | 1.518 |
| Skew: | 0.304 | Prob(JB): | 0.468 |
| Kurtosis: | 1.898 | Cond. No. | 74.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.435 | 0.522 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.469 |
| Model: | OLS | Adj. R-squared: | 0.325 |
| Method: | Least Squares | F-statistic: | 3.245 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0640 |
| Time: | 03:56:24 | Log-Likelihood: | -70.546 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 151.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -7.2322 | 225.454 | -0.032 | 0.975 | -503.453 488.988 |
| C(dose)[T.1] | -20.9074 | 341.436 | -0.061 | 0.952 | -772.402 730.587 |
| expression | 13.3280 | 40.192 | 0.332 | 0.746 | -75.133 101.789 |
| expression:C(dose)[T.1] | 9.9143 | 57.507 | 0.172 | 0.866 | -116.658 136.487 |
| Omnibus: | 0.896 | Durbin-Watson: | 0.945 |
| Prob(Omnibus): | 0.639 | Jarque-Bera (JB): | 0.777 |
| Skew: | -0.470 | Prob(JB): | 0.678 |
| Kurtosis: | 2.400 | Cond. No. | 343. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.468 |
| Model: | OLS | Adj. R-squared: | 0.379 |
| Method: | Least Squares | F-statistic: | 5.279 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0227 |
| Time: | 03:56:24 | Log-Likelihood: | -70.566 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -34.3604 | 154.794 | -0.222 | 0.828 | -371.627 302.906 |
| C(dose)[T.1] | 37.8088 | 23.181 | 1.631 | 0.129 | -12.699 88.317 |
| expression | 18.1708 | 27.559 | 0.659 | 0.522 | -41.876 78.217 |
| Omnibus: | 1.004 | Durbin-Watson: | 0.935 |
| Prob(Omnibus): | 0.605 | Jarque-Bera (JB): | 0.861 |
| Skew: | -0.496 | Prob(JB): | 0.650 |
| Kurtosis: | 2.373 | 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: | 03:56:24 | 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: | 0.350 |
| Model: | OLS | Adj. R-squared: | 0.300 |
| Method: | Least Squares | F-statistic: | 7.004 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0201 |
| Time: | 03:56:24 | Log-Likelihood: | -72.068 |
| No. Observations: | 15 | AIC: | 148.1 |
| Df Residuals: | 13 | BIC: | 149.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -212.9908 | 116.164 | -1.834 | 0.090 | -463.949 37.967 |
| expression | 51.6603 | 19.521 | 2.646 | 0.020 | 9.489 93.832 |
| Omnibus: | 0.658 | Durbin-Watson: | 1.274 |
| Prob(Omnibus): | 0.720 | Jarque-Bera (JB): | 0.633 |
| Skew: | 0.398 | Prob(JB): | 0.729 |
| Kurtosis: | 2.386 | Cond. No. | 86.8 |