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 |
| 0.713 | 0.408 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.608 |
| Method: | Least Squares | F-statistic: | 12.36 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.000103 |
| Time: | 11:41:45 | Log-Likelihood: | -100.66 |
| No. Observations: | 23 | AIC: | 209.3 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 0.6617 | 86.941 | 0.008 | 0.994 | -181.308 182.632 |
| C(dose)[T.1] | 47.7190 | 138.927 | 0.343 | 0.735 | -243.059 338.497 |
| expression | 7.9682 | 12.906 | 0.617 | 0.544 | -19.044 34.980 |
| expression:C(dose)[T.1] | 1.0636 | 20.958 | 0.051 | 0.960 | -42.802 44.929 |
| Omnibus: | 1.343 | Durbin-Watson: | 1.937 |
| Prob(Omnibus): | 0.511 | Jarque-Bera (JB): | 0.887 |
| Skew: | 0.014 | Prob(JB): | 0.642 |
| Kurtosis: | 2.038 | Cond. No. | 263. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.627 |
| Method: | Least Squares | F-statistic: | 19.51 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 2.00e-05 |
| Time: | 11:41:45 | Log-Likelihood: | -100.66 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -2.0486 | 66.873 | -0.031 | 0.976 | -141.543 137.446 |
| C(dose)[T.1] | 54.7548 | 8.779 | 6.237 | 0.000 | 36.441 73.068 |
| expression | 8.3716 | 9.912 | 0.845 | 0.408 | -12.304 29.047 |
| Omnibus: | 1.355 | Durbin-Watson: | 1.950 |
| Prob(Omnibus): | 0.508 | Jarque-Bera (JB): | 0.890 |
| Skew: | 0.012 | Prob(JB): | 0.641 |
| Kurtosis: | 2.036 | Cond. No. | 106. |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 11:41:45 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.045 |
| Method: | Least Squares | F-statistic: | 0.04476 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.834 |
| Time: | 11:41:45 | Log-Likelihood: | -113.08 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 102.6017 | 108.409 | 0.946 | 0.355 | -122.847 328.050 |
| expression | -3.4470 | 16.293 | -0.212 | 0.834 | -37.330 30.436 |
| Omnibus: | 3.109 | Durbin-Watson: | 2.427 |
| Prob(Omnibus): | 0.211 | Jarque-Bera (JB): | 1.519 |
| Skew: | 0.282 | Prob(JB): | 0.468 |
| Kurtosis: | 1.875 | Cond. No. | 102. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.053 | 0.325 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.507 |
| Model: | OLS | Adj. R-squared: | 0.372 |
| Method: | Least Squares | F-statistic: | 3.769 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0441 |
| Time: | 11:41:45 | Log-Likelihood: | -69.998 |
| No. Observations: | 15 | AIC: | 148.0 |
| Df Residuals: | 11 | BIC: | 150.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 210.5956 | 130.088 | 1.619 | 0.134 | -75.727 496.918 |
| C(dose)[T.1] | -55.4957 | 191.597 | -0.290 | 0.777 | -477.197 366.205 |
| expression | -20.2059 | 18.290 | -1.105 | 0.293 | -60.462 20.050 |
| expression:C(dose)[T.1] | 14.8115 | 26.856 | 0.552 | 0.592 | -44.298 73.921 |
| Omnibus: | 2.959 | Durbin-Watson: | 1.085 |
| Prob(Omnibus): | 0.228 | Jarque-Bera (JB): | 1.900 |
| Skew: | -0.864 | Prob(JB): | 0.387 |
| Kurtosis: | 2.768 | Cond. No. | 235. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.493 |
| Model: | OLS | Adj. R-squared: | 0.409 |
| Method: | Least Squares | F-statistic: | 5.840 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0169 |
| Time: | 11:41:45 | Log-Likelihood: | -70.202 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 12 | BIC: | 148.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 161.9210 | 92.758 | 1.746 | 0.106 | -40.182 364.024 |
| C(dose)[T.1] | 49.8233 | 15.104 | 3.299 | 0.006 | 16.914 82.732 |
| expression | -13.3362 | 12.999 | -1.026 | 0.325 | -41.658 14.986 |
| Omnibus: | 3.442 | Durbin-Watson: | 0.963 |
| Prob(Omnibus): | 0.179 | Jarque-Bera (JB): | 2.183 |
| Skew: | -0.931 | Prob(JB): | 0.336 |
| Kurtosis: | 2.838 | Cond. No. | 89.9 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 11:41:45 | 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: | 0.034 |
| Model: | OLS | Adj. R-squared: | -0.041 |
| Method: | Least Squares | F-statistic: | 0.4533 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.513 |
| Time: | 11:41:45 | Log-Likelihood: | -75.043 |
| No. Observations: | 15 | AIC: | 154.1 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 176.1595 | 122.928 | 1.433 | 0.175 | -89.411 441.730 |
| expression | -11.6016 | 17.231 | -0.673 | 0.513 | -48.827 25.624 |
| Omnibus: | 1.796 | Durbin-Watson: | 1.882 |
| Prob(Omnibus): | 0.407 | Jarque-Bera (JB): | 0.945 |
| Skew: | 0.164 | Prob(JB): | 0.623 |
| Kurtosis: | 1.815 | Cond. No. | 89.5 |