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 |
| 0.609 | 0.444 | 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: | 03:53:14 | 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 | 47.4180 | 49.349 | 0.961 | 0.349 | -55.871 150.707 |
| C(dose)[T.1] | 6.6244 | 73.361 | 0.090 | 0.929 | -146.922 160.171 |
| expression | 1.6309 | 11.763 | 0.139 | 0.891 | -22.989 26.251 |
| expression:C(dose)[T.1] | 11.2636 | 17.527 | 0.643 | 0.528 | -25.420 47.947 |
| Omnibus: | 0.082 | Durbin-Watson: | 1.841 |
| Prob(Omnibus): | 0.960 | Jarque-Bera (JB): | 0.052 |
| Skew: | -0.035 | Prob(JB): | 0.974 |
| Kurtosis: | 2.777 | Cond. No. | 93.9 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.625 |
| Method: | Least Squares | F-statistic: | 19.36 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.10e-05 |
| Time: | 03:53:14 | Log-Likelihood: | -100.72 |
| No. Observations: | 23 | AIC: | 207.4 |
| Df Residuals: | 20 | BIC: | 210.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 26.2940 | 36.265 | 0.725 | 0.477 | -49.354 101.942 |
| C(dose)[T.1] | 53.4328 | 8.640 | 6.184 | 0.000 | 35.410 71.456 |
| expression | 6.7045 | 8.591 | 0.780 | 0.444 | -11.216 24.625 |
| Omnibus: | 0.042 | Durbin-Watson: | 1.891 |
| Prob(Omnibus): | 0.979 | Jarque-Bera (JB): | 0.079 |
| Skew: | 0.031 | Prob(JB): | 0.961 |
| Kurtosis: | 2.721 | Cond. No. | 37.4 |
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: | 03:53:14 | 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: | 0.008 |
| Model: | OLS | Adj. R-squared: | -0.039 |
| Method: | Least Squares | F-statistic: | 0.1730 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.682 |
| Time: | 03:53:14 | Log-Likelihood: | -113.01 |
| No. Observations: | 23 | AIC: | 230.0 |
| Df Residuals: | 21 | BIC: | 232.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 54.9810 | 59.900 | 0.918 | 0.369 | -69.588 179.550 |
| expression | 5.9509 | 14.306 | 0.416 | 0.682 | -23.801 35.703 |
| Omnibus: | 3.285 | Durbin-Watson: | 2.484 |
| Prob(Omnibus): | 0.194 | Jarque-Bera (JB): | 1.510 |
| Skew: | 0.252 | Prob(JB): | 0.470 |
| Kurtosis: | 1.850 | Cond. No. | 36.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.006 | 0.941 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.472 |
| Model: | OLS | Adj. R-squared: | 0.328 |
| Method: | Least Squares | F-statistic: | 3.281 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0623 |
| Time: | 03:53:14 | Log-Likelihood: | -70.507 |
| No. Observations: | 15 | AIC: | 149.0 |
| Df Residuals: | 11 | BIC: | 151.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 10.7532 | 98.794 | 0.109 | 0.915 | -206.690 228.197 |
| C(dose)[T.1] | 144.8282 | 140.125 | 1.034 | 0.324 | -163.585 453.241 |
| expression | 11.9001 | 20.597 | 0.578 | 0.575 | -33.433 57.233 |
| expression:C(dose)[T.1] | -18.8080 | 27.036 | -0.696 | 0.501 | -78.313 40.697 |
| Omnibus: | 2.093 | Durbin-Watson: | 0.875 |
| Prob(Omnibus): | 0.351 | Jarque-Bera (JB): | 1.500 |
| Skew: | -0.738 | Prob(JB): | 0.472 |
| Kurtosis: | 2.529 | Cond. No. | 132. |
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: | 4.890 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0280 |
| Time: | 03:53:14 | Log-Likelihood: | -70.829 |
| 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 | 62.7412 | 63.214 | 0.993 | 0.341 | -74.991 200.473 |
| C(dose)[T.1] | 48.3335 | 19.457 | 2.484 | 0.029 | 5.940 90.727 |
| expression | 0.9842 | 13.052 | 0.075 | 0.941 | -27.453 29.422 |
| Omnibus: | 2.745 | Durbin-Watson: | 0.802 |
| Prob(Omnibus): | 0.253 | Jarque-Bera (JB): | 1.887 |
| Skew: | -0.848 | Prob(JB): | 0.389 |
| Kurtosis: | 2.624 | Cond. No. | 44.9 |
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: | 03:53:14 | 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: | 0.166 |
| Model: | OLS | Adj. R-squared: | 0.102 |
| Method: | Least Squares | F-statistic: | 2.582 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.132 |
| Time: | 03:53:14 | Log-Likelihood: | -73.941 |
| No. Observations: | 15 | AIC: | 151.9 |
| Df Residuals: | 13 | BIC: | 153.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -11.2177 | 65.928 | -0.170 | 0.868 | -153.646 131.211 |
| expression | 20.0536 | 12.480 | 1.607 | 0.132 | -6.907 47.014 |
| Omnibus: | 0.725 | Durbin-Watson: | 1.214 |
| Prob(Omnibus): | 0.696 | Jarque-Bera (JB): | 0.629 |
| Skew: | 0.081 | Prob(JB): | 0.730 |
| Kurtosis: | 2.010 | Cond. No. | 38.8 |