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 |
| 0.014 | 0.906 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.743 |
| Model: | OLS | Adj. R-squared: | 0.702 |
| Method: | Least Squares | F-statistic: | 18.30 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.88e-06 |
| Time: | 04:52:50 | Log-Likelihood: | -97.485 |
| No. Observations: | 23 | AIC: | 203.0 |
| Df Residuals: | 19 | BIC: | 207.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 107.5987 | 51.264 | 2.099 | 0.049 | 0.303 214.895 |
| C(dose)[T.1] | -267.7413 | 122.215 | -2.191 | 0.041 | -523.541 -11.942 |
| expression | -8.8583 | 8.459 | -1.047 | 0.308 | -26.564 8.847 |
| expression:C(dose)[T.1] | 50.0275 | 19.025 | 2.630 | 0.017 | 10.207 89.848 |
| Omnibus: | 1.262 | Durbin-Watson: | 2.585 |
| Prob(Omnibus): | 0.532 | Jarque-Bera (JB): | 1.098 |
| Skew: | 0.360 | Prob(JB): | 0.577 |
| Kurtosis: | 2.207 | Cond. No. | 239. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.52 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.81e-05 |
| Time: | 04:52:50 | Log-Likelihood: | -101.05 |
| 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 | 47.9870 | 52.337 | 0.917 | 0.370 | -61.186 157.160 |
| C(dose)[T.1] | 52.8469 | 9.677 | 5.461 | 0.000 | 32.662 73.032 |
| expression | 1.0322 | 8.625 | 0.120 | 0.906 | -16.959 19.024 |
| Omnibus: | 0.312 | Durbin-Watson: | 1.870 |
| Prob(Omnibus): | 0.856 | Jarque-Bera (JB): | 0.479 |
| Skew: | 0.070 | Prob(JB): | 0.787 |
| Kurtosis: | 2.307 | Cond. No. | 77.4 |
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: | 04:52:50 | 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: | 0.126 |
| Model: | OLS | Adj. R-squared: | 0.085 |
| Method: | Least Squares | F-statistic: | 3.036 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0960 |
| Time: | 04:52:50 | Log-Likelihood: | -111.55 |
| No. Observations: | 23 | AIC: | 227.1 |
| Df Residuals: | 21 | BIC: | 229.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -51.4580 | 75.580 | -0.681 | 0.503 | -208.636 105.720 |
| expression | 20.9736 | 12.036 | 1.743 | 0.096 | -4.057 46.004 |
| Omnibus: | 0.441 | Durbin-Watson: | 2.322 |
| Prob(Omnibus): | 0.802 | Jarque-Bera (JB): | 0.512 |
| Skew: | -0.279 | Prob(JB): | 0.774 |
| Kurtosis: | 2.527 | Cond. No. | 72.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.010 | 0.921 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.481 |
| Model: | OLS | Adj. R-squared: | 0.339 |
| Method: | Least Squares | F-statistic: | 3.397 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0573 |
| Time: | 04:52:50 | Log-Likelihood: | -70.382 |
| No. Observations: | 15 | AIC: | 148.8 |
| Df Residuals: | 11 | BIC: | 151.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 117.8831 | 115.497 | 1.021 | 0.329 | -136.324 372.090 |
| C(dose)[T.1] | -92.0206 | 174.147 | -0.528 | 0.608 | -475.316 291.275 |
| expression | -7.7310 | 17.607 | -0.439 | 0.669 | -46.484 31.022 |
| expression:C(dose)[T.1] | 22.8756 | 27.923 | 0.819 | 0.430 | -38.582 84.333 |
| Omnibus: | 3.834 | Durbin-Watson: | 0.998 |
| Prob(Omnibus): | 0.147 | Jarque-Bera (JB): | 2.165 |
| Skew: | -0.929 | Prob(JB): | 0.339 |
| Kurtosis: | 3.108 | Cond. No. | 179. |
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: | 4.894 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0279 |
| Time: | 04:52:50 | Log-Likelihood: | -70.827 |
| 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 | 58.5229 | 88.701 | 0.660 | 0.522 | -134.740 251.786 |
| C(dose)[T.1] | 49.9240 | 17.296 | 2.886 | 0.014 | 12.239 87.609 |
| expression | 1.3646 | 13.477 | 0.101 | 0.921 | -27.999 30.728 |
| Omnibus: | 2.751 | Durbin-Watson: | 0.779 |
| Prob(Omnibus): | 0.253 | Jarque-Bera (JB): | 1.871 |
| Skew: | -0.847 | Prob(JB): | 0.392 |
| Kurtosis: | 2.645 | Cond. No. | 73.2 |
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: | 04:52:50 | 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: | 0.067 |
| Model: | OLS | Adj. R-squared: | -0.005 |
| Method: | Least Squares | F-statistic: | 0.9315 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.352 |
| Time: | 04:52:50 | Log-Likelihood: | -74.781 |
| No. Observations: | 15 | AIC: | 153.6 |
| Df Residuals: | 13 | BIC: | 155.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 186.0250 | 96.194 | 1.934 | 0.075 | -21.790 393.839 |
| expression | -14.7966 | 15.331 | -0.965 | 0.352 | -47.916 18.323 |
| Omnibus: | 0.600 | Durbin-Watson: | 1.681 |
| Prob(Omnibus): | 0.741 | Jarque-Bera (JB): | 0.597 |
| Skew: | 0.144 | Prob(JB): | 0.742 |
| Kurtosis: | 2.066 | Cond. No. | 63.0 |