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 |
| 0.535 | 0.473 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.702 |
| Model: | OLS | Adj. R-squared: | 0.655 |
| Method: | Least Squares | F-statistic: | 14.90 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 3.15e-05 |
| Time: | 22:45:54 | Log-Likelihood: | -99.192 |
| No. Observations: | 23 | AIC: | 206.4 |
| Df Residuals: | 19 | BIC: | 210.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -41.5543 | 54.194 | -0.767 | 0.453 | -154.984 71.875 |
| C(dose)[T.1] | 167.4732 | 68.372 | 2.449 | 0.024 | 24.369 310.577 |
| expression | 21.8394 | 12.290 | 1.777 | 0.092 | -3.884 47.562 |
| expression:C(dose)[T.1] | -26.2236 | 15.746 | -1.665 | 0.112 | -59.181 6.734 |
| Omnibus: | 0.003 | Durbin-Watson: | 1.877 |
| Prob(Omnibus): | 0.999 | Jarque-Bera (JB): | 0.134 |
| Skew: | 0.012 | Prob(JB): | 0.935 |
| Kurtosis: | 2.627 | Cond. No. | 102. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 19.26 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.18e-05 |
| Time: | 22:45:54 | Log-Likelihood: | -100.76 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 28.4940 | 35.657 | 0.799 | 0.434 | -45.886 102.874 |
| C(dose)[T.1] | 54.4753 | 8.794 | 6.195 | 0.000 | 36.132 72.818 |
| expression | 5.8643 | 8.016 | 0.732 | 0.473 | -10.858 22.586 |
| Omnibus: | 0.344 | Durbin-Watson: | 1.909 |
| Prob(Omnibus): | 0.842 | Jarque-Bera (JB): | 0.505 |
| Skew: | 0.141 | Prob(JB): | 0.777 |
| Kurtosis: | 2.332 | Cond. No. | 37.8 |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:45:55 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.045 |
| Method: | Least Squares | F-statistic: | 0.04934 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.826 |
| Time: | 22:45:55 | Log-Likelihood: | -113.08 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 92.2593 | 56.920 | 1.621 | 0.120 | -26.112 210.631 |
| expression | -2.9221 | 13.155 | -0.222 | 0.826 | -30.279 24.435 |
| Omnibus: | 2.923 | Durbin-Watson: | 2.448 |
| Prob(Omnibus): | 0.232 | Jarque-Bera (JB): | 1.415 |
| Skew: | 0.235 | Prob(JB): | 0.493 |
| Kurtosis: | 1.880 | Cond. No. | 36.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.577 | 0.462 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.570 |
| Model: | OLS | Adj. R-squared: | 0.452 |
| Method: | Least Squares | F-statistic: | 4.855 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0218 |
| Time: | 22:45:55 | Log-Likelihood: | -68.975 |
| No. Observations: | 15 | AIC: | 146.0 |
| Df Residuals: | 11 | BIC: | 148.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 225.7310 | 98.423 | 2.293 | 0.043 | 9.103 442.358 |
| C(dose)[T.1] | -185.4026 | 148.543 | -1.248 | 0.238 | -512.343 141.538 |
| expression | -35.4684 | 21.924 | -1.618 | 0.134 | -83.722 12.785 |
| expression:C(dose)[T.1] | 53.6229 | 34.291 | 1.564 | 0.146 | -21.852 129.097 |
| Omnibus: | 7.128 | Durbin-Watson: | 1.209 |
| Prob(Omnibus): | 0.028 | Jarque-Bera (JB): | 3.828 |
| Skew: | -1.090 | Prob(JB): | 0.147 |
| Kurtosis: | 4.173 | Cond. No. | 120. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.474 |
| Model: | OLS | Adj. R-squared: | 0.386 |
| Method: | Least Squares | F-statistic: | 5.408 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0212 |
| Time: | 22:45:55 | Log-Likelihood: | -70.481 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 12 | BIC: | 149.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 127.9047 | 80.429 | 1.590 | 0.138 | -47.335 303.145 |
| C(dose)[T.1] | 45.6657 | 16.062 | 2.843 | 0.015 | 10.669 80.662 |
| expression | -13.5500 | 17.844 | -0.759 | 0.462 | -52.429 25.329 |
| Omnibus: | 1.853 | Durbin-Watson: | 0.851 |
| Prob(Omnibus): | 0.396 | Jarque-Bera (JB): | 1.422 |
| Skew: | -0.689 | Prob(JB): | 0.491 |
| Kurtosis: | 2.389 | Cond. No. | 48.3 |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:45:55 | 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: | 0.120 |
| Model: | OLS | Adj. R-squared: | 0.052 |
| Method: | Least Squares | F-statistic: | 1.769 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.206 |
| Time: | 22:45:55 | Log-Likelihood: | -74.343 |
| No. Observations: | 15 | AIC: | 152.7 |
| Df Residuals: | 13 | BIC: | 154.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 215.7625 | 92.293 | 2.338 | 0.036 | 16.375 415.150 |
| expression | -28.2353 | 21.229 | -1.330 | 0.206 | -74.098 17.627 |
| Omnibus: | 3.855 | Durbin-Watson: | 1.553 |
| Prob(Omnibus): | 0.146 | Jarque-Bera (JB): | 1.773 |
| Skew: | 0.526 | Prob(JB): | 0.412 |
| Kurtosis: | 1.684 | Cond. No. | 44.3 |