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 |
| 0.557 | 0.464 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.674 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 13.10 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.17e-05 |
| Time: | 05:15:10 | Log-Likelihood: | -100.21 |
| No. Observations: | 23 | AIC: | 208.4 |
| Df Residuals: | 19 | BIC: | 213.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 112.7034 | 49.394 | 2.282 | 0.034 | 9.321 216.086 |
| C(dose)[T.1] | -15.0112 | 70.558 | -0.213 | 0.834 | -162.691 132.669 |
| expression | -18.1590 | 15.220 | -1.193 | 0.248 | -50.015 13.697 |
| expression:C(dose)[T.1] | 21.4202 | 22.483 | 0.953 | 0.353 | -25.636 68.477 |
| Omnibus: | 0.434 | Durbin-Watson: | 1.728 |
| Prob(Omnibus): | 0.805 | Jarque-Bera (JB): | 0.544 |
| Skew: | -0.020 | Prob(JB): | 0.762 |
| Kurtosis: | 2.248 | Cond. No. | 71.9 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 19.29 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.15e-05 |
| Time: | 05:15:10 | Log-Likelihood: | -100.75 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 81.0803 | 36.495 | 2.222 | 0.038 | 4.953 157.208 |
| C(dose)[T.1] | 51.6697 | 8.934 | 5.783 | 0.000 | 33.034 70.306 |
| expression | -8.3420 | 11.176 | -0.746 | 0.464 | -31.655 14.971 |
| Omnibus: | 0.093 | Durbin-Watson: | 1.882 |
| Prob(Omnibus): | 0.954 | Jarque-Bera (JB): | 0.316 |
| Skew: | -0.039 | Prob(JB): | 0.854 |
| Kurtosis: | 2.431 | Cond. No. | 29.5 |
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: | 05:15:11 | 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: | 0.088 |
| Model: | OLS | Adj. R-squared: | 0.044 |
| Method: | Least Squares | F-statistic: | 2.015 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.170 |
| Time: | 05:15:11 | Log-Likelihood: | -112.05 |
| No. Observations: | 23 | AIC: | 228.1 |
| Df Residuals: | 21 | BIC: | 230.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 156.3115 | 54.399 | 2.873 | 0.009 | 43.182 269.441 |
| expression | -24.5049 | 17.264 | -1.419 | 0.170 | -60.407 11.397 |
| Omnibus: | 2.017 | Durbin-Watson: | 2.328 |
| Prob(Omnibus): | 0.365 | Jarque-Bera (JB): | 1.481 |
| Skew: | 0.423 | Prob(JB): | 0.477 |
| Kurtosis: | 2.090 | Cond. No. | 27.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.627 | 0.081 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.585 |
| Model: | OLS | Adj. R-squared: | 0.472 |
| Method: | Least Squares | F-statistic: | 5.165 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0181 |
| Time: | 05:15:11 | Log-Likelihood: | -68.707 |
| No. Observations: | 15 | AIC: | 145.4 |
| Df Residuals: | 11 | BIC: | 148.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 137.6360 | 41.241 | 3.337 | 0.007 | 46.866 228.406 |
| C(dose)[T.1] | 14.6372 | 65.524 | 0.223 | 0.827 | -129.580 158.854 |
| expression | -16.7831 | 9.539 | -1.759 | 0.106 | -37.778 4.212 |
| expression:C(dose)[T.1] | 7.4871 | 16.150 | 0.464 | 0.652 | -28.059 43.033 |
| Omnibus: | 3.728 | Durbin-Watson: | 1.348 |
| Prob(Omnibus): | 0.155 | Jarque-Bera (JB): | 1.913 |
| Skew: | -0.865 | Prob(JB): | 0.384 |
| Kurtosis: | 3.262 | Cond. No. | 49.6 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.577 |
| Model: | OLS | Adj. R-squared: | 0.506 |
| Method: | Least Squares | F-statistic: | 8.175 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00575 |
| Time: | 05:15:11 | Log-Likelihood: | -68.852 |
| No. Observations: | 15 | AIC: | 143.7 |
| Df Residuals: | 12 | BIC: | 145.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 126.7101 | 32.717 | 3.873 | 0.002 | 55.425 197.995 |
| C(dose)[T.1] | 44.2585 | 14.034 | 3.154 | 0.008 | 13.680 74.837 |
| expression | -14.1713 | 7.441 | -1.904 | 0.081 | -30.384 2.042 |
| Omnibus: | 3.534 | Durbin-Watson: | 1.221 |
| Prob(Omnibus): | 0.171 | Jarque-Bera (JB): | 1.786 |
| Skew: | -0.836 | Prob(JB): | 0.409 |
| Kurtosis: | 3.248 | Cond. No. | 21.0 |
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: | 05:15:11 | 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: | 0.226 |
| Model: | OLS | Adj. R-squared: | 0.166 |
| Method: | Least Squares | F-statistic: | 3.794 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0734 |
| Time: | 05:15:11 | Log-Likelihood: | -73.380 |
| No. Observations: | 15 | AIC: | 150.8 |
| Df Residuals: | 13 | BIC: | 152.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 167.6452 | 39.020 | 4.296 | 0.001 | 83.348 251.942 |
| expression | -18.5067 | 9.502 | -1.948 | 0.073 | -39.034 2.020 |
| Omnibus: | 3.470 | Durbin-Watson: | 1.900 |
| Prob(Omnibus): | 0.176 | Jarque-Bera (JB): | 1.604 |
| Skew: | 0.469 | Prob(JB): | 0.448 |
| Kurtosis: | 1.702 | Cond. No. | 18.9 |