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 |
| 0.477 | 0.498 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.604 |
| Method: | Least Squares | F-statistic: | 12.20 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000112 |
| Time: | 22:59:13 | Log-Likelihood: | -100.76 |
| No. Observations: | 23 | AIC: | 209.5 |
| Df Residuals: | 19 | BIC: | 214.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -2.9962 | 86.094 | -0.035 | 0.973 | -183.193 177.201 |
| C(dose)[T.1] | 85.6870 | 129.899 | 0.660 | 0.517 | -186.194 357.568 |
| expression | 7.4312 | 11.156 | 0.666 | 0.513 | -15.918 30.780 |
| expression:C(dose)[T.1] | -4.0566 | 17.266 | -0.235 | 0.817 | -40.194 32.081 |
| Omnibus: | 0.309 | Durbin-Watson: | 1.834 |
| Prob(Omnibus): | 0.857 | Jarque-Bera (JB): | 0.477 |
| Skew: | -0.048 | Prob(JB): | 0.788 |
| Kurtosis: | 2.301 | Cond. No. | 282. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 19.17 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.24e-05 |
| Time: | 22:59:13 | Log-Likelihood: | -100.79 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 10.0401 | 64.257 | 0.156 | 0.877 | -123.997 144.077 |
| C(dose)[T.1] | 55.2458 | 9.097 | 6.073 | 0.000 | 36.269 74.223 |
| expression | 5.7377 | 8.311 | 0.690 | 0.498 | -11.598 23.074 |
| Omnibus: | 0.114 | Durbin-Watson: | 1.813 |
| Prob(Omnibus): | 0.945 | Jarque-Bera (JB): | 0.337 |
| Skew: | -0.028 | Prob(JB): | 0.845 |
| Kurtosis: | 2.409 | Cond. No. | 114. |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:59:13 | 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: | 0.025 |
| Model: | OLS | Adj. R-squared: | -0.021 |
| Method: | Least Squares | F-statistic: | 0.5427 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.469 |
| Time: | 22:59:14 | Log-Likelihood: | -112.81 |
| No. Observations: | 23 | AIC: | 229.6 |
| Df Residuals: | 21 | BIC: | 231.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 152.0862 | 98.494 | 1.544 | 0.137 | -52.743 356.915 |
| expression | -9.5995 | 13.031 | -0.737 | 0.469 | -36.698 17.499 |
| Omnibus: | 2.730 | Durbin-Watson: | 2.403 |
| Prob(Omnibus): | 0.255 | Jarque-Bera (JB): | 1.387 |
| Skew: | 0.246 | Prob(JB): | 0.500 |
| Kurtosis: | 1.902 | Cond. No. | 106. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 5.665 | 0.035 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.689 |
| Model: | OLS | Adj. R-squared: | 0.604 |
| Method: | Least Squares | F-statistic: | 8.110 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00395 |
| Time: | 22:59:14 | Log-Likelihood: | -66.549 |
| No. Observations: | 15 | AIC: | 141.1 |
| Df Residuals: | 11 | BIC: | 143.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -250.2534 | 241.023 | -1.038 | 0.321 | -780.742 280.235 |
| C(dose)[T.1] | -625.7569 | 452.163 | -1.384 | 0.194 | -1620.961 369.447 |
| expression | 37.1122 | 28.137 | 1.319 | 0.214 | -24.817 99.041 |
| expression:C(dose)[T.1] | 78.8549 | 52.805 | 1.493 | 0.163 | -37.367 195.077 |
| Omnibus: | 1.124 | Durbin-Watson: | 1.158 |
| Prob(Omnibus): | 0.570 | Jarque-Bera (JB): | 0.917 |
| Skew: | -0.531 | Prob(JB): | 0.632 |
| Kurtosis: | 2.416 | Cond. No. | 775. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.626 |
| Model: | OLS | Adj. R-squared: | 0.563 |
| Method: | Least Squares | F-statistic: | 10.02 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00276 |
| Time: | 22:59:14 | Log-Likelihood: | -67.933 |
| No. Observations: | 15 | AIC: | 141.9 |
| Df Residuals: | 12 | BIC: | 144.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -441.9072 | 214.213 | -2.063 | 0.061 | -908.638 24.824 |
| C(dose)[T.1] | 49.2208 | 12.973 | 3.794 | 0.003 | 20.955 77.486 |
| expression | 59.5016 | 25.000 | 2.380 | 0.035 | 5.031 113.973 |
| Omnibus: | 1.191 | Durbin-Watson: | 1.309 |
| Prob(Omnibus): | 0.551 | Jarque-Bera (JB): | 0.751 |
| Skew: | -0.525 | Prob(JB): | 0.687 |
| Kurtosis: | 2.687 | Cond. No. | 288. |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:59:14 | 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: | 0.176 |
| Model: | OLS | Adj. R-squared: | 0.113 |
| Method: | Least Squares | F-statistic: | 2.783 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.119 |
| Time: | 22:59:14 | Log-Likelihood: | -73.845 |
| No. Observations: | 15 | AIC: | 151.7 |
| Df Residuals: | 13 | BIC: | 153.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -415.0143 | 305.072 | -1.360 | 0.197 | -1074.083 244.054 |
| expression | 59.4266 | 35.624 | 1.668 | 0.119 | -17.534 136.387 |
| Omnibus: | 6.222 | Durbin-Watson: | 1.793 |
| Prob(Omnibus): | 0.045 | Jarque-Bera (JB): | 1.534 |
| Skew: | 0.086 | Prob(JB): | 0.464 |
| Kurtosis: | 1.443 | Cond. No. | 287. |