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 |
| 0.968 | 0.337 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 12.73 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.57e-05 |
| Time: | 03:41:03 | Log-Likelihood: | -100.43 |
| No. Observations: | 23 | AIC: | 208.9 |
| Df Residuals: | 19 | BIC: | 213.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -32.4029 | 151.178 | -0.214 | 0.833 | -348.822 284.016 |
| C(dose)[T.1] | -55.5108 | 272.398 | -0.204 | 0.841 | -625.646 514.624 |
| expression | 9.4694 | 16.515 | 0.573 | 0.573 | -25.098 44.037 |
| expression:C(dose)[T.1] | 11.1018 | 29.001 | 0.383 | 0.706 | -49.598 71.802 |
| Omnibus: | 0.118 | Durbin-Watson: | 1.929 |
| Prob(Omnibus): | 0.943 | Jarque-Bera (JB): | 0.138 |
| Skew: | -0.124 | Prob(JB): | 0.934 |
| Kurtosis: | 2.714 | Cond. No. | 709. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.665 |
| Model: | OLS | Adj. R-squared: | 0.632 |
| Method: | Least Squares | F-statistic: | 19.87 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.77e-05 |
| Time: | 03:41:03 | Log-Likelihood: | -100.52 |
| No. Observations: | 23 | AIC: | 207.0 |
| Df Residuals: | 20 | BIC: | 210.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -65.3334 | 121.636 | -0.537 | 0.597 | -319.061 188.394 |
| C(dose)[T.1] | 48.6951 | 9.778 | 4.980 | 0.000 | 28.298 69.092 |
| expression | 13.0698 | 13.283 | 0.984 | 0.337 | -14.638 40.778 |
| Omnibus: | 0.043 | Durbin-Watson: | 1.898 |
| Prob(Omnibus): | 0.979 | Jarque-Bera (JB): | 0.145 |
| Skew: | -0.082 | Prob(JB): | 0.930 |
| Kurtosis: | 2.646 | Cond. No. | 269. |
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: | 03:41:03 | 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: | 0.250 |
| Model: | OLS | Adj. R-squared: | 0.214 |
| Method: | Least Squares | F-statistic: | 7.007 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0151 |
| Time: | 03:41:03 | Log-Likelihood: | -109.79 |
| No. Observations: | 23 | AIC: | 223.6 |
| Df Residuals: | 21 | BIC: | 225.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -339.3641 | 158.440 | -2.142 | 0.044 | -668.857 -9.871 |
| expression | 44.9839 | 16.994 | 2.647 | 0.015 | 9.644 80.324 |
| Omnibus: | 1.754 | Durbin-Watson: | 2.089 |
| Prob(Omnibus): | 0.416 | Jarque-Bera (JB): | 1.002 |
| Skew: | -0.011 | Prob(JB): | 0.606 |
| Kurtosis: | 1.978 | Cond. No. | 239. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.716 | 0.414 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.530 |
| Model: | OLS | Adj. R-squared: | 0.402 |
| Method: | Least Squares | F-statistic: | 4.131 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0345 |
| Time: | 03:41:03 | Log-Likelihood: | -69.641 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 11 | BIC: | 150.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -175.3428 | 179.459 | -0.977 | 0.350 | -570.330 219.644 |
| C(dose)[T.1] | 341.8195 | 274.403 | 1.246 | 0.239 | -262.138 945.777 |
| expression | 27.2914 | 20.136 | 1.355 | 0.202 | -17.027 71.609 |
| expression:C(dose)[T.1] | -32.7271 | 30.274 | -1.081 | 0.303 | -99.359 33.904 |
| Omnibus: | 2.546 | Durbin-Watson: | 1.017 |
| Prob(Omnibus): | 0.280 | Jarque-Bera (JB): | 1.305 |
| Skew: | -0.722 | Prob(JB): | 0.521 |
| Kurtosis: | 3.039 | Cond. No. | 430. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.480 |
| Model: | OLS | Adj. R-squared: | 0.393 |
| Method: | Least Squares | F-statistic: | 5.534 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0198 |
| Time: | 03:41:03 | Log-Likelihood: | -70.398 |
| No. Observations: | 15 | AIC: | 146.8 |
| Df Residuals: | 12 | BIC: | 148.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -46.5533 | 135.151 | -0.344 | 0.736 | -341.023 247.916 |
| C(dose)[T.1] | 45.6650 | 15.849 | 2.881 | 0.014 | 11.133 80.198 |
| expression | 12.8134 | 15.141 | 0.846 | 0.414 | -20.177 45.803 |
| Omnibus: | 2.552 | Durbin-Watson: | 0.863 |
| Prob(Omnibus): | 0.279 | Jarque-Bera (JB): | 1.872 |
| Skew: | -0.823 | Prob(JB): | 0.392 |
| Kurtosis: | 2.467 | Cond. No. | 163. |
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: | 03:41:03 | 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: | 0.120 |
| Model: | OLS | Adj. R-squared: | 0.052 |
| Method: | Least Squares | F-statistic: | 1.772 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.206 |
| Time: | 03:41:03 | Log-Likelihood: | -74.342 |
| No. Observations: | 15 | AIC: | 152.7 |
| Df Residuals: | 13 | BIC: | 154.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -126.0622 | 165.335 | -0.762 | 0.459 | -483.246 231.122 |
| expression | 24.2995 | 18.254 | 1.331 | 0.206 | -15.135 63.734 |
| Omnibus: | 3.626 | Durbin-Watson: | 1.953 |
| Prob(Omnibus): | 0.163 | Jarque-Bera (JB): | 1.561 |
| Skew: | 0.428 | Prob(JB): | 0.458 |
| Kurtosis: | 1.671 | Cond. No. | 159. |