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 |
| 1.970 | 0.176 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.688 |
| Model: | OLS | Adj. R-squared: | 0.639 |
| Method: | Least Squares | F-statistic: | 13.97 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.77e-05 |
| Time: | 04:25:03 | Log-Likelihood: | -99.706 |
| No. Observations: | 23 | AIC: | 207.4 |
| Df Residuals: | 19 | BIC: | 212.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -318.1702 | 255.915 | -1.243 | 0.229 | -853.806 217.465 |
| C(dose)[T.1] | 298.4767 | 357.093 | 0.836 | 0.414 | -448.928 1045.881 |
| expression | 41.8930 | 28.783 | 1.455 | 0.162 | -18.351 102.137 |
| expression:C(dose)[T.1] | -27.4403 | 40.351 | -0.680 | 0.505 | -111.896 57.015 |
| Omnibus: | 0.703 | Durbin-Watson: | 1.358 |
| Prob(Omnibus): | 0.704 | Jarque-Bera (JB): | 0.745 |
| Skew: | -0.256 | Prob(JB): | 0.689 |
| Kurtosis: | 2.282 | Cond. No. | 983. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.681 |
| Model: | OLS | Adj. R-squared: | 0.649 |
| Method: | Least Squares | F-statistic: | 21.30 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.11e-05 |
| Time: | 04:25:03 | Log-Likelihood: | -99.982 |
| No. Observations: | 23 | AIC: | 206.0 |
| Df Residuals: | 20 | BIC: | 209.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -194.0619 | 176.976 | -1.097 | 0.286 | -563.228 175.105 |
| C(dose)[T.1] | 55.7096 | 8.536 | 6.526 | 0.000 | 37.903 73.516 |
| expression | 27.9307 | 19.899 | 1.404 | 0.176 | -13.579 69.440 |
| Omnibus: | 0.972 | Durbin-Watson: | 1.580 |
| Prob(Omnibus): | 0.615 | Jarque-Bera (JB): | 0.813 |
| Skew: | -0.161 | Prob(JB): | 0.666 |
| Kurtosis: | 2.137 | Cond. No. | 380. |
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: | 04:25:03 | 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: | 0.004332 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.948 |
| Time: | 04:25:03 | 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 | 60.1077 | 298.043 | 0.202 | 0.842 | -559.706 679.922 |
| expression | 2.2162 | 33.674 | 0.066 | 0.948 | -67.813 72.246 |
| Omnibus: | 3.275 | Durbin-Watson: | 2.486 |
| Prob(Omnibus): | 0.194 | Jarque-Bera (JB): | 1.569 |
| Skew: | 0.292 | Prob(JB): | 0.456 |
| Kurtosis: | 1.862 | Cond. No. | 370. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.903 | 0.114 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.558 |
| Model: | OLS | Adj. R-squared: | 0.437 |
| Method: | Least Squares | F-statistic: | 4.624 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0251 |
| Time: | 04:25:04 | Log-Likelihood: | -69.181 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 11 | BIC: | 149.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -374.0787 | 332.470 | -1.125 | 0.284 | -1105.841 357.683 |
| C(dose)[T.1] | -97.9801 | 690.986 | -0.142 | 0.890 | -1618.831 1422.871 |
| expression | 50.0934 | 37.702 | 1.329 | 0.211 | -32.889 133.076 |
| expression:C(dose)[T.1] | 15.2800 | 77.105 | 0.198 | 0.847 | -154.427 184.987 |
| Omnibus: | 1.248 | Durbin-Watson: | 0.931 |
| Prob(Omnibus): | 0.536 | Jarque-Bera (JB): | 0.982 |
| Skew: | -0.562 | Prob(JB): | 0.612 |
| Kurtosis: | 2.444 | Cond. No. | 1.02e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.556 |
| Model: | OLS | Adj. R-squared: | 0.482 |
| Method: | Least Squares | F-statistic: | 7.518 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00765 |
| Time: | 04:25:04 | Log-Likelihood: | -69.208 |
| No. Observations: | 15 | AIC: | 144.4 |
| Df Residuals: | 12 | BIC: | 146.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -406.2784 | 278.208 | -1.460 | 0.170 | -1012.441 199.884 |
| C(dose)[T.1] | 38.9168 | 15.358 | 2.534 | 0.026 | 5.454 72.379 |
| expression | 53.7468 | 31.544 | 1.704 | 0.114 | -14.981 122.475 |
| Omnibus: | 1.484 | Durbin-Watson: | 0.932 |
| Prob(Omnibus): | 0.476 | Jarque-Bera (JB): | 1.122 |
| Skew: | -0.616 | Prob(JB): | 0.571 |
| Kurtosis: | 2.472 | Cond. No. | 357. |
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: | 04:25:04 | 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: | 0.319 |
| Model: | OLS | Adj. R-squared: | 0.266 |
| Method: | Least Squares | F-statistic: | 6.080 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0284 |
| Time: | 04:25:04 | Log-Likelihood: | -72.422 |
| No. Observations: | 15 | AIC: | 148.8 |
| Df Residuals: | 13 | BIC: | 150.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -665.4610 | 307.977 | -2.161 | 0.050 | -1330.804 -0.118 |
| expression | 85.1451 | 34.530 | 2.466 | 0.028 | 10.547 159.744 |
| Omnibus: | 1.617 | Durbin-Watson: | 1.962 |
| Prob(Omnibus): | 0.446 | Jarque-Bera (JB): | 1.029 |
| Skew: | 0.332 | Prob(JB): | 0.598 |
| Kurtosis: | 1.902 | Cond. No. | 332. |