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 |
| 0.049 | 0.826 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.667 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 12.67 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.85e-05 |
| Time: | 04:42:42 | Log-Likelihood: | -100.47 |
| No. Observations: | 23 | AIC: | 208.9 |
| Df Residuals: | 19 | BIC: | 213.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 116.8546 | 72.495 | 1.612 | 0.123 | -34.880 268.589 |
| C(dose)[T.1] | -40.6910 | 95.790 | -0.425 | 0.676 | -241.182 159.800 |
| expression | -7.7389 | 8.924 | -0.867 | 0.397 | -26.417 10.940 |
| expression:C(dose)[T.1] | 11.9248 | 12.193 | 0.978 | 0.340 | -13.596 37.446 |
| Omnibus: | 0.084 | Durbin-Watson: | 2.050 |
| Prob(Omnibus): | 0.959 | Jarque-Bera (JB): | 0.294 |
| Skew: | -0.080 | Prob(JB): | 0.863 |
| Kurtosis: | 2.470 | Cond. No. | 230. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.57 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.76e-05 |
| Time: | 04:42:42 | Log-Likelihood: | -101.03 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 65.1453 | 49.544 | 1.315 | 0.203 | -38.202 168.493 |
| C(dose)[T.1] | 52.5293 | 9.482 | 5.540 | 0.000 | 32.750 72.309 |
| expression | -1.3511 | 6.074 | -0.222 | 0.826 | -14.022 11.320 |
| Omnibus: | 0.411 | Durbin-Watson: | 1.920 |
| Prob(Omnibus): | 0.814 | Jarque-Bera (JB): | 0.534 |
| Skew: | 0.056 | Prob(JB): | 0.766 |
| Kurtosis: | 2.262 | Cond. No. | 90.7 |
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: | 04:42:42 | 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: | 0.113 |
| Model: | OLS | Adj. R-squared: | 0.070 |
| Method: | Least Squares | F-statistic: | 2.669 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.117 |
| Time: | 04:42:42 | Log-Likelihood: | -111.73 |
| No. Observations: | 23 | AIC: | 227.5 |
| Df Residuals: | 21 | BIC: | 229.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 190.9246 | 68.415 | 2.791 | 0.011 | 48.648 333.201 |
| expression | -14.2409 | 8.718 | -1.634 | 0.117 | -32.370 3.888 |
| Omnibus: | 1.590 | Durbin-Watson: | 2.349 |
| Prob(Omnibus): | 0.452 | Jarque-Bera (JB): | 1.412 |
| Skew: | 0.514 | Prob(JB): | 0.494 |
| Kurtosis: | 2.353 | Cond. No. | 80.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 8.325 | 0.014 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.685 |
| Model: | OLS | Adj. R-squared: | 0.599 |
| Method: | Least Squares | F-statistic: | 7.971 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00421 |
| Time: | 04:42:42 | Log-Likelihood: | -66.638 |
| No. Observations: | 15 | AIC: | 141.3 |
| Df Residuals: | 11 | BIC: | 144.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 385.6210 | 178.766 | 2.157 | 0.054 | -7.840 779.082 |
| C(dose)[T.1] | -61.6861 | 201.267 | -0.306 | 0.765 | -504.673 381.301 |
| expression | -50.7722 | 28.488 | -1.782 | 0.102 | -113.474 11.929 |
| expression:C(dose)[T.1] | 19.1214 | 31.768 | 0.602 | 0.559 | -50.800 89.042 |
| Omnibus: | 2.014 | Durbin-Watson: | 1.269 |
| Prob(Omnibus): | 0.365 | Jarque-Bera (JB): | 1.526 |
| Skew: | -0.722 | Prob(JB): | 0.466 |
| Kurtosis: | 2.404 | Cond. No. | 327. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.675 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 12.44 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00119 |
| Time: | 04:42:42 | Log-Likelihood: | -66.881 |
| No. Observations: | 15 | AIC: | 139.8 |
| Df Residuals: | 12 | BIC: | 141.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 289.2552 | 77.387 | 3.738 | 0.003 | 120.644 457.867 |
| C(dose)[T.1] | 59.2077 | 12.582 | 4.706 | 0.001 | 31.794 86.621 |
| expression | -35.3957 | 12.268 | -2.885 | 0.014 | -62.124 -8.667 |
| Omnibus: | 2.637 | Durbin-Watson: | 1.056 |
| Prob(Omnibus): | 0.268 | Jarque-Bera (JB): | 1.855 |
| Skew: | -0.834 | Prob(JB): | 0.395 |
| Kurtosis: | 2.571 | Cond. No. | 84.9 |
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: | 04:42:42 | 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: | 0.074 |
| Model: | OLS | Adj. R-squared: | 0.003 |
| Method: | Least Squares | F-statistic: | 1.039 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.327 |
| Time: | 04:42:42 | Log-Likelihood: | -74.724 |
| No. Observations: | 15 | AIC: | 153.4 |
| Df Residuals: | 13 | BIC: | 154.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 218.6603 | 123.038 | 1.777 | 0.099 | -47.147 484.467 |
| expression | -19.4758 | 19.110 | -1.019 | 0.327 | -60.761 21.810 |
| Omnibus: | 1.769 | Durbin-Watson: | 1.892 |
| Prob(Omnibus): | 0.413 | Jarque-Bera (JB): | 0.960 |
| Skew: | 0.200 | Prob(JB): | 0.619 |
| Kurtosis: | 1.827 | Cond. No. | 83.0 |