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 |
| 1.745 | 0.201 | 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: | 0.635 |
| Method: | Least Squares | F-statistic: | 13.78 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 5.22e-05 |
| Time: | 22:55:11 | Log-Likelihood: | -99.818 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 19 | BIC: | 212.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -17.6044 | 51.098 | -0.345 | 0.734 | -124.554 89.345 |
| C(dose)[T.1] | 101.6268 | 76.754 | 1.324 | 0.201 | -59.020 262.274 |
| expression | 11.9721 | 8.462 | 1.415 | 0.173 | -5.739 29.683 |
| expression:C(dose)[T.1] | -8.3650 | 12.159 | -0.688 | 0.500 | -33.814 17.084 |
| Omnibus: | 0.127 | Durbin-Watson: | 1.653 |
| Prob(Omnibus): | 0.939 | Jarque-Bera (JB): | 0.334 |
| Skew: | 0.101 | Prob(JB): | 0.846 |
| Kurtosis: | 2.445 | Cond. No. | 150. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.677 |
| Model: | OLS | Adj. R-squared: | 0.645 |
| Method: | Least Squares | F-statistic: | 20.98 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.23e-05 |
| Time: | 22:55:11 | Log-Likelihood: | -100.10 |
| No. Observations: | 23 | AIC: | 206.2 |
| Df Residuals: | 20 | BIC: | 209.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 6.6970 | 36.433 | 0.184 | 0.856 | -69.301 82.695 |
| C(dose)[T.1] | 49.1949 | 8.976 | 5.481 | 0.000 | 30.471 67.919 |
| expression | 7.9207 | 5.996 | 1.321 | 0.201 | -4.587 20.428 |
| Omnibus: | 0.229 | Durbin-Watson: | 1.670 |
| Prob(Omnibus): | 0.892 | Jarque-Bera (JB): | 0.364 |
| Skew: | 0.196 | Prob(JB): | 0.834 |
| Kurtosis: | 2.525 | Cond. No. | 56.2 |
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: | 22:55:11 | 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: | 0.192 |
| Model: | OLS | Adj. R-squared: | 0.154 |
| Method: | Least Squares | F-statistic: | 5.004 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0363 |
| Time: | 22:55:11 | Log-Likelihood: | -110.65 |
| No. Observations: | 23 | AIC: | 225.3 |
| Df Residuals: | 21 | BIC: | 227.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -41.5050 | 54.575 | -0.761 | 0.455 | -155.001 71.991 |
| expression | 19.4004 | 8.672 | 2.237 | 0.036 | 1.365 37.435 |
| Omnibus: | 1.926 | Durbin-Watson: | 2.232 |
| Prob(Omnibus): | 0.382 | Jarque-Bera (JB): | 1.673 |
| Skew: | 0.579 | Prob(JB): | 0.433 |
| Kurtosis: | 2.363 | Cond. No. | 54.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.039 | 0.847 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.531 |
| Model: | OLS | Adj. R-squared: | 0.404 |
| Method: | Least Squares | F-statistic: | 4.158 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0339 |
| Time: | 22:55:11 | Log-Likelihood: | -69.615 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 11 | BIC: | 150.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 117.8176 | 56.235 | 2.095 | 0.060 | -5.954 241.589 |
| C(dose)[T.1] | -87.5705 | 100.143 | -0.874 | 0.401 | -307.983 132.842 |
| expression | -7.4293 | 8.129 | -0.914 | 0.380 | -25.321 10.462 |
| expression:C(dose)[T.1] | 20.7818 | 15.086 | 1.378 | 0.196 | -12.423 53.986 |
| Omnibus: | 0.626 | Durbin-Watson: | 1.198 |
| Prob(Omnibus): | 0.731 | Jarque-Bera (JB): | 0.153 |
| Skew: | -0.245 | Prob(JB): | 0.927 |
| Kurtosis: | 2.933 | Cond. No. | 110. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.359 |
| Method: | Least Squares | F-statistic: | 4.920 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0275 |
| Time: | 22:55:11 | Log-Likelihood: | -70.809 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 76.8931 | 49.500 | 1.553 | 0.146 | -30.959 184.745 |
| C(dose)[T.1] | 48.7590 | 15.871 | 3.072 | 0.010 | 14.179 83.339 |
| expression | -1.3954 | 7.099 | -0.197 | 0.847 | -16.864 14.073 |
| Omnibus: | 3.112 | Durbin-Watson: | 0.773 |
| Prob(Omnibus): | 0.211 | Jarque-Bera (JB): | 2.092 |
| Skew: | -0.902 | Prob(JB): | 0.351 |
| Kurtosis: | 2.693 | Cond. No. | 43.6 |
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: | 22:55:12 | 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: | 0.018 |
| Model: | OLS | Adj. R-squared: | -0.057 |
| Method: | Least Squares | F-statistic: | 0.2434 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.630 |
| Time: | 22:55:12 | Log-Likelihood: | -75.161 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 123.1286 | 60.558 | 2.033 | 0.063 | -7.699 253.956 |
| expression | -4.4536 | 9.027 | -0.493 | 0.630 | -23.955 15.048 |
| Omnibus: | 1.119 | Durbin-Watson: | 1.673 |
| Prob(Omnibus): | 0.571 | Jarque-Bera (JB): | 0.755 |
| Skew: | 0.105 | Prob(JB): | 0.685 |
| Kurtosis: | 1.921 | Cond. No. | 41.2 |