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 |
| 0.288 | 0.597 | 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: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000103 |
| Time: | 06:25:20 | 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 | 98.1740 | 129.762 | 0.757 | 0.459 | -173.421 369.770 |
| C(dose)[T.1] | -37.4106 | 144.144 | -0.260 | 0.798 | -339.107 264.286 |
| expression | -8.1983 | 24.170 | -0.339 | 0.738 | -58.787 42.390 |
| expression:C(dose)[T.1] | 16.8954 | 26.813 | 0.630 | 0.536 | -39.224 73.015 |
| Omnibus: | 0.542 | Durbin-Watson: | 1.884 |
| Prob(Omnibus): | 0.763 | Jarque-Bera (JB): | 0.626 |
| Skew: | 0.169 | Prob(JB): | 0.731 |
| Kurtosis: | 2.265 | Cond. No. | 271. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 18.91 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.46e-05 |
| Time: | 06:25:20 | Log-Likelihood: | -100.90 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 24.5485 | 55.587 | 0.442 | 0.664 | -91.405 140.502 |
| C(dose)[T.1] | 53.2469 | 8.709 | 6.114 | 0.000 | 35.080 71.414 |
| expression | 5.5307 | 10.304 | 0.537 | 0.597 | -15.964 27.025 |
| Omnibus: | 0.729 | Durbin-Watson: | 2.005 |
| Prob(Omnibus): | 0.694 | Jarque-Bera (JB): | 0.752 |
| Skew: | 0.240 | Prob(JB): | 0.687 |
| Kurtosis: | 2.255 | Cond. No. | 71.5 |
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: | 06:25:20 | 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: | 0.007 |
| Model: | OLS | Adj. R-squared: | -0.040 |
| Method: | Least Squares | F-statistic: | 0.1570 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.696 |
| Time: | 06:25:20 | Log-Likelihood: | -113.02 |
| 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 | 43.4829 | 91.744 | 0.474 | 0.640 | -147.309 234.275 |
| expression | 6.7469 | 17.030 | 0.396 | 0.696 | -28.669 42.163 |
| Omnibus: | 2.884 | Durbin-Watson: | 2.546 |
| Prob(Omnibus): | 0.237 | Jarque-Bera (JB): | 1.500 |
| Skew: | 0.301 | Prob(JB): | 0.472 |
| Kurtosis: | 1.903 | Cond. No. | 71.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.488 | 0.246 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.628 |
| Model: | OLS | Adj. R-squared: | 0.526 |
| Method: | Least Squares | F-statistic: | 6.178 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0102 |
| Time: | 06:25:20 | Log-Likelihood: | -67.893 |
| No. Observations: | 15 | AIC: | 143.8 |
| Df Residuals: | 11 | BIC: | 146.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 23.2793 | 104.491 | 0.223 | 0.828 | -206.704 253.262 |
| C(dose)[T.1] | 291.8367 | 136.854 | 2.132 | 0.056 | -9.376 593.049 |
| expression | 8.4662 | 19.948 | 0.424 | 0.679 | -35.439 52.371 |
| expression:C(dose)[T.1] | -51.4738 | 27.579 | -1.866 | 0.089 | -112.175 9.227 |
| Omnibus: | 0.860 | Durbin-Watson: | 1.184 |
| Prob(Omnibus): | 0.650 | Jarque-Bera (JB): | 0.784 |
| Skew: | -0.450 | Prob(JB): | 0.676 |
| Kurtosis: | 2.334 | Cond. No. | 140. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.510 |
| Model: | OLS | Adj. R-squared: | 0.428 |
| Method: | Least Squares | F-statistic: | 6.235 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0139 |
| Time: | 06:25:20 | Log-Likelihood: | -69.956 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 12 | BIC: | 148.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 163.7091 | 79.657 | 2.055 | 0.062 | -9.849 337.267 |
| C(dose)[T.1] | 38.1273 | 17.399 | 2.191 | 0.049 | 0.219 76.036 |
| expression | -18.4630 | 15.133 | -1.220 | 0.246 | -51.435 14.509 |
| Omnibus: | 1.585 | Durbin-Watson: | 1.070 |
| Prob(Omnibus): | 0.453 | Jarque-Bera (JB): | 1.212 |
| Skew: | -0.637 | Prob(JB): | 0.545 |
| Kurtosis: | 2.437 | Cond. No. | 55.8 |
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: | 06:25:20 | 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: | 0.313 |
| Model: | OLS | Adj. R-squared: | 0.261 |
| Method: | Least Squares | F-statistic: | 5.933 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0300 |
| Time: | 06:25:20 | Log-Likelihood: | -72.481 |
| No. Observations: | 15 | AIC: | 149.0 |
| Df Residuals: | 13 | BIC: | 150.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 268.6939 | 72.351 | 3.714 | 0.003 | 112.389 424.999 |
| expression | -35.7560 | 14.680 | -2.436 | 0.030 | -67.470 -4.042 |
| Omnibus: | 0.541 | Durbin-Watson: | 1.533 |
| Prob(Omnibus): | 0.763 | Jarque-Bera (JB): | 0.481 |
| Skew: | -0.364 | Prob(JB): | 0.786 |
| Kurtosis: | 2.511 | Cond. No. | 44.1 |