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 |
| 4.261 | 0.052 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.713 |
| Model: | OLS | Adj. R-squared: | 0.668 |
| Method: | Least Squares | F-statistic: | 15.74 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.20e-05 |
| Time: | 05:13:29 | Log-Likelihood: | -98.748 |
| No. Observations: | 23 | AIC: | 205.5 |
| Df Residuals: | 19 | BIC: | 210.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 88.0056 | 19.074 | 4.614 | 0.000 | 48.082 127.929 |
| C(dose)[T.1] | 77.0265 | 67.384 | 1.143 | 0.267 | -64.009 218.062 |
| expression | -11.5613 | 6.235 | -1.854 | 0.079 | -24.610 1.488 |
| expression:C(dose)[T.1] | -9.6967 | 24.603 | -0.394 | 0.698 | -61.191 41.797 |
| Omnibus: | 0.096 | Durbin-Watson: | 2.063 |
| Prob(Omnibus): | 0.953 | Jarque-Bera (JB): | 0.058 |
| Skew: | 0.056 | Prob(JB): | 0.972 |
| Kurtosis: | 2.782 | Cond. No. | 60.0 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.711 |
| Model: | OLS | Adj. R-squared: | 0.682 |
| Method: | Least Squares | F-statistic: | 24.57 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.11e-06 |
| Time: | 05:13:29 | Log-Likelihood: | -98.842 |
| No. Observations: | 23 | AIC: | 203.7 |
| Df Residuals: | 20 | BIC: | 207.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 89.8260 | 18.112 | 4.960 | 0.000 | 52.045 127.607 |
| C(dose)[T.1] | 50.6678 | 8.067 | 6.281 | 0.000 | 33.841 67.495 |
| expression | -12.1839 | 5.902 | -2.064 | 0.052 | -24.496 0.128 |
| Omnibus: | 0.005 | Durbin-Watson: | 2.086 |
| Prob(Omnibus): | 0.997 | Jarque-Bera (JB): | 0.131 |
| Skew: | 0.020 | Prob(JB): | 0.937 |
| Kurtosis: | 2.633 | Cond. No. | 14.9 |
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: | 05:13:29 | 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: | 0.140 |
| Model: | OLS | Adj. R-squared: | 0.099 |
| Method: | Least Squares | F-statistic: | 3.419 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0786 |
| Time: | 05:13:29 | Log-Likelihood: | -111.37 |
| No. Observations: | 23 | AIC: | 226.7 |
| Df Residuals: | 21 | BIC: | 229.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 130.8082 | 28.428 | 4.601 | 0.000 | 71.689 189.928 |
| expression | -18.1267 | 9.803 | -1.849 | 0.079 | -38.512 2.259 |
| Omnibus: | 2.700 | Durbin-Watson: | 2.674 |
| Prob(Omnibus): | 0.259 | Jarque-Bera (JB): | 1.581 |
| Skew: | 0.372 | Prob(JB): | 0.454 |
| Kurtosis: | 1.953 | Cond. No. | 13.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.635 | 0.441 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.477 |
| Model: | OLS | Adj. R-squared: | 0.334 |
| Method: | Least Squares | F-statistic: | 3.340 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0597 |
| Time: | 05:13:29 | Log-Likelihood: | -70.444 |
| No. Observations: | 15 | AIC: | 148.9 |
| Df Residuals: | 11 | BIC: | 151.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 33.5633 | 66.560 | 0.504 | 0.624 | -112.933 180.060 |
| C(dose)[T.1] | 41.1972 | 100.204 | 0.411 | 0.689 | -179.350 261.745 |
| expression | 6.9085 | 13.367 | 0.517 | 0.615 | -22.512 36.329 |
| expression:C(dose)[T.1] | 1.2948 | 19.736 | 0.066 | 0.949 | -42.143 44.733 |
| Omnibus: | 1.375 | Durbin-Watson: | 0.873 |
| Prob(Omnibus): | 0.503 | Jarque-Bera (JB): | 1.088 |
| Skew: | -0.589 | Prob(JB): | 0.580 |
| Kurtosis: | 2.407 | Cond. No. | 86.2 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.476 |
| Model: | OLS | Adj. R-squared: | 0.389 |
| Method: | Least Squares | F-statistic: | 5.460 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0206 |
| Time: | 05:13:29 | Log-Likelihood: | -70.446 |
| No. Observations: | 15 | AIC: | 146.9 |
| Df Residuals: | 12 | BIC: | 149.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 30.6517 | 47.503 | 0.645 | 0.531 | -72.849 134.153 |
| C(dose)[T.1] | 47.6856 | 15.456 | 3.085 | 0.009 | 14.010 81.361 |
| expression | 7.5025 | 9.417 | 0.797 | 0.441 | -13.016 28.021 |
| Omnibus: | 1.342 | Durbin-Watson: | 0.879 |
| Prob(Omnibus): | 0.511 | Jarque-Bera (JB): | 1.070 |
| Skew: | -0.581 | Prob(JB): | 0.586 |
| Kurtosis: | 2.399 | Cond. No. | 32.9 |
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: | 05:13:29 | 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: | 0.061 |
| Model: | OLS | Adj. R-squared: | -0.011 |
| Method: | Least Squares | F-statistic: | 0.8471 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.374 |
| Time: | 05:13:29 | Log-Likelihood: | -74.827 |
| No. Observations: | 15 | AIC: | 153.7 |
| Df Residuals: | 13 | BIC: | 155.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 38.2256 | 61.035 | 0.626 | 0.542 | -93.633 170.084 |
| expression | 11.0675 | 12.025 | 0.920 | 0.374 | -14.910 37.045 |
| Omnibus: | 2.358 | Durbin-Watson: | 1.309 |
| Prob(Omnibus): | 0.308 | Jarque-Bera (JB): | 1.088 |
| Skew: | 0.208 | Prob(JB): | 0.580 |
| Kurtosis: | 1.748 | Cond. No. | 32.7 |