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 |
| 0.428 | 0.520 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.605 |
| Method: | Least Squares | F-statistic: | 12.24 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000109 |
| Time: | 04:46:14 | Log-Likelihood: | -100.73 |
| No. Observations: | 23 | AIC: | 209.5 |
| Df Residuals: | 19 | BIC: | 214.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 66.3273 | 43.478 | 1.526 | 0.144 | -24.674 157.329 |
| C(dose)[T.1] | 81.1616 | 72.772 | 1.115 | 0.279 | -71.151 233.474 |
| expression | -1.8072 | 6.419 | -0.282 | 0.781 | -15.242 11.628 |
| expression:C(dose)[T.1] | -4.0305 | 10.633 | -0.379 | 0.709 | -26.286 18.225 |
| Omnibus: | 0.118 | Durbin-Watson: | 2.047 |
| Prob(Omnibus): | 0.943 | Jarque-Bera (JB): | 0.267 |
| Skew: | 0.142 | Prob(JB): | 0.875 |
| Kurtosis: | 2.556 | Cond. No. | 140. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 19.10 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.29e-05 |
| Time: | 04:46:14 | Log-Likelihood: | -100.82 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 76.1767 | 34.105 | 2.234 | 0.037 | 5.034 147.319 |
| C(dose)[T.1] | 53.7841 | 8.704 | 6.179 | 0.000 | 35.627 71.941 |
| expression | -3.2760 | 5.007 | -0.654 | 0.520 | -13.720 7.168 |
| Omnibus: | 0.275 | Durbin-Watson: | 2.081 |
| Prob(Omnibus): | 0.872 | Jarque-Bera (JB): | 0.456 |
| Skew: | 0.051 | Prob(JB): | 0.796 |
| Kurtosis: | 2.318 | Cond. No. | 55.0 |
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: | 04:46:14 | 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: | 0.01042 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.920 |
| Time: | 04:46:14 | 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 | 85.4593 | 56.713 | 1.507 | 0.147 | -32.481 203.400 |
| expression | -0.8480 | 8.308 | -0.102 | 0.920 | -18.125 16.429 |
| Omnibus: | 3.199 | Durbin-Watson: | 2.524 |
| Prob(Omnibus): | 0.202 | Jarque-Bera (JB): | 1.542 |
| Skew: | 0.285 | Prob(JB): | 0.462 |
| Kurtosis: | 1.867 | Cond. No. | 54.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.005 | 0.946 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.306 |
| Method: | Least Squares | F-statistic: | 3.060 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0734 |
| Time: | 04:46:14 | Log-Likelihood: | -70.749 |
| No. Observations: | 15 | AIC: | 149.5 |
| Df Residuals: | 11 | BIC: | 152.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 54.8088 | 70.370 | 0.779 | 0.452 | -100.074 209.691 |
| C(dose)[T.1] | 119.0498 | 202.684 | 0.587 | 0.569 | -327.054 565.154 |
| expression | 2.1650 | 11.898 | 0.182 | 0.859 | -24.021 28.351 |
| expression:C(dose)[T.1] | -11.9389 | 34.519 | -0.346 | 0.736 | -87.914 64.036 |
| Omnibus: | 1.974 | Durbin-Watson: | 0.795 |
| Prob(Omnibus): | 0.373 | Jarque-Bera (JB): | 1.484 |
| Skew: | -0.716 | Prob(JB): | 0.476 |
| Kurtosis: | 2.431 | Cond. No. | 176. |
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: | 4.889 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0280 |
| Time: | 04:46:14 | Log-Likelihood: | -70.830 |
| 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 | 63.0760 | 63.712 | 0.990 | 0.342 | -75.740 201.892 |
| C(dose)[T.1] | 49.1764 | 15.739 | 3.124 | 0.009 | 14.884 83.469 |
| expression | 0.7467 | 10.751 | 0.069 | 0.946 | -22.678 24.171 |
| Omnibus: | 2.685 | Durbin-Watson: | 0.791 |
| Prob(Omnibus): | 0.261 | Jarque-Bera (JB): | 1.845 |
| Skew: | -0.838 | Prob(JB): | 0.398 |
| Kurtosis: | 2.622 | Cond. No. | 49.3 |
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, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 04:46:14 | 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: | 0.009613 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.923 |
| Time: | 04:46:14 | Log-Likelihood: | -75.295 |
| 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 | 85.6987 | 81.899 | 1.046 | 0.314 | -91.233 262.630 |
| expression | 1.3636 | 13.908 | 0.098 | 0.923 | -28.682 31.409 |
| Omnibus: | 0.722 | Durbin-Watson: | 1.598 |
| Prob(Omnibus): | 0.697 | Jarque-Bera (JB): | 0.624 |
| Skew: | 0.055 | Prob(JB): | 0.732 |
| Kurtosis: | 2.007 | Cond. No. | 48.8 |