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 |
| 0.000 | 0.997 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.660 |
| Model: | OLS | Adj. R-squared: | 0.607 |
| Method: | Least Squares | F-statistic: | 12.30 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000106 |
| Time: | 03:57:38 | Log-Likelihood: | -100.69 |
| No. Observations: | 23 | AIC: | 209.4 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 25.4631 | 51.742 | 0.492 | 0.628 | -82.834 133.760 |
| C(dose)[T.1] | 111.4348 | 74.250 | 1.501 | 0.150 | -43.971 266.841 |
| expression | 5.6217 | 10.048 | 0.559 | 0.582 | -15.409 26.652 |
| expression:C(dose)[T.1] | -11.2095 | 14.221 | -0.788 | 0.440 | -40.975 18.556 |
| Omnibus: | 0.158 | Durbin-Watson: | 1.824 |
| Prob(Omnibus): | 0.924 | Jarque-Bera (JB): | 0.356 |
| Skew: | 0.124 | Prob(JB): | 0.837 |
| Kurtosis: | 2.443 | Cond. No. | 118. |
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: | 18.49 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.83e-05 |
| Time: | 03:57:39 | Log-Likelihood: | -101.06 |
| 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 | 54.0754 | 36.520 | 1.481 | 0.154 | -22.105 130.256 |
| C(dose)[T.1] | 53.3335 | 8.825 | 6.044 | 0.000 | 34.925 71.742 |
| expression | 0.0260 | 7.043 | 0.004 | 0.997 | -14.666 14.718 |
| Omnibus: | 0.320 | Durbin-Watson: | 1.888 |
| Prob(Omnibus): | 0.852 | Jarque-Bera (JB): | 0.484 |
| Skew: | 0.059 | Prob(JB): | 0.785 |
| Kurtosis: | 2.300 | Cond. No. | 45.3 |
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: | 03:57:39 | 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: | 0.1726 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.682 |
| Time: | 03:57:39 | 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 | 55.0070 | 59.916 | 0.918 | 0.369 | -69.595 179.609 |
| expression | 4.7703 | 11.483 | 0.415 | 0.682 | -19.110 28.651 |
| Omnibus: | 3.514 | Durbin-Watson: | 2.426 |
| Prob(Omnibus): | 0.173 | Jarque-Bera (JB): | 1.666 |
| Skew: | 0.321 | Prob(JB): | 0.435 |
| Kurtosis: | 1.849 | Cond. No. | 45.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.410 | 0.534 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.568 |
| Model: | OLS | Adj. R-squared: | 0.451 |
| Method: | Least Squares | F-statistic: | 4.829 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0221 |
| Time: | 03:57:39 | Log-Likelihood: | -68.998 |
| No. Observations: | 15 | AIC: | 146.0 |
| Df Residuals: | 11 | BIC: | 148.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 136.5268 | 124.378 | 1.098 | 0.296 | -137.228 410.282 |
| C(dose)[T.1] | -291.5760 | 206.200 | -1.414 | 0.185 | -745.420 162.268 |
| expression | -11.4215 | 20.484 | -0.558 | 0.588 | -56.506 33.663 |
| expression:C(dose)[T.1] | 49.6054 | 30.854 | 1.608 | 0.136 | -18.304 117.514 |
| Omnibus: | 0.185 | Durbin-Watson: | 1.360 |
| Prob(Omnibus): | 0.911 | Jarque-Bera (JB): | 0.378 |
| Skew: | -0.145 | Prob(JB): | 0.828 |
| Kurtosis: | 2.278 | Cond. No. | 251. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.467 |
| Model: | OLS | Adj. R-squared: | 0.378 |
| Method: | Least Squares | F-statistic: | 5.257 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0229 |
| Time: | 03:57:39 | Log-Likelihood: | -70.581 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 4.2532 | 99.249 | 0.043 | 0.967 | -211.992 220.499 |
| C(dose)[T.1] | 38.0750 | 23.256 | 1.637 | 0.128 | -12.596 88.746 |
| expression | 10.4424 | 16.298 | 0.641 | 0.534 | -25.069 45.954 |
| Omnibus: | 2.280 | Durbin-Watson: | 0.690 |
| Prob(Omnibus): | 0.320 | Jarque-Bera (JB): | 1.700 |
| Skew: | -0.774 | Prob(JB): | 0.427 |
| Kurtosis: | 2.430 | Cond. No. | 89.1 |
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: | 03:57:39 | 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: | 0.348 |
| Model: | OLS | Adj. R-squared: | 0.298 |
| Method: | Least Squares | F-statistic: | 6.937 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0206 |
| Time: | 03:57:39 | Log-Likelihood: | -72.093 |
| No. Observations: | 15 | AIC: | 148.2 |
| Df Residuals: | 13 | BIC: | 149.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -107.2458 | 76.721 | -1.398 | 0.186 | -272.991 58.500 |
| expression | 30.3591 | 11.527 | 2.634 | 0.021 | 5.458 55.261 |
| Omnibus: | 1.772 | Durbin-Watson: | 0.919 |
| Prob(Omnibus): | 0.412 | Jarque-Bera (JB): | 0.942 |
| Skew: | -0.611 | Prob(JB): | 0.624 |
| Kurtosis: | 2.882 | Cond. No. | 63.6 |