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 |
| 0.037 | 0.849 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.691 |
| Model: | OLS | Adj. R-squared: | 0.642 |
| Method: | Least Squares | F-statistic: | 14.16 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.38e-05 |
| Time: | 04:13:22 | Log-Likelihood: | -99.600 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 19 | BIC: | 211.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -30.8979 | 114.116 | -0.271 | 0.789 | -269.746 207.950 |
| C(dose)[T.1] | 390.3224 | 210.916 | 1.851 | 0.080 | -51.131 831.775 |
| expression | 11.3229 | 15.163 | 0.747 | 0.464 | -20.413 43.059 |
| expression:C(dose)[T.1] | -42.2234 | 26.512 | -1.593 | 0.128 | -97.713 13.267 |
| Omnibus: | 1.149 | Durbin-Watson: | 1.621 |
| Prob(Omnibus): | 0.563 | Jarque-Bera (JB): | 1.044 |
| Skew: | 0.457 | Prob(JB): | 0.593 |
| Kurtosis: | 2.496 | Cond. No. | 483. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.55 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.78e-05 |
| Time: | 04:13:22 | Log-Likelihood: | -101.04 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 72.9091 | 97.202 | 0.750 | 0.462 | -129.850 275.668 |
| C(dose)[T.1] | 54.9170 | 11.997 | 4.577 | 0.000 | 29.891 79.943 |
| expression | -2.4880 | 12.907 | -0.193 | 0.849 | -29.412 24.435 |
| Omnibus: | 0.665 | Durbin-Watson: | 1.901 |
| Prob(Omnibus): | 0.717 | Jarque-Bera (JB): | 0.660 |
| Skew: | 0.085 | Prob(JB): | 0.719 |
| Kurtosis: | 2.188 | Cond. No. | 178. |
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: | 04:13:22 | 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: | 0.283 |
| Model: | OLS | Adj. R-squared: | 0.249 |
| Method: | Least Squares | F-statistic: | 8.278 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00902 |
| Time: | 04:13:22 | Log-Likelihood: | -109.28 |
| No. Observations: | 23 | AIC: | 222.6 |
| Df Residuals: | 21 | BIC: | 224.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -216.4369 | 103.116 | -2.099 | 0.048 | -430.877 -1.996 |
| expression | 37.8715 | 13.163 | 2.877 | 0.009 | 10.498 65.245 |
| Omnibus: | 2.680 | Durbin-Watson: | 1.743 |
| Prob(Omnibus): | 0.262 | Jarque-Bera (JB): | 1.534 |
| Skew: | 0.349 | Prob(JB): | 0.464 |
| Kurtosis: | 1.945 | Cond. No. | 134. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.585 | 0.232 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.516 |
| Model: | OLS | Adj. R-squared: | 0.384 |
| Method: | Least Squares | F-statistic: | 3.909 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0400 |
| Time: | 04:13:22 | Log-Likelihood: | -69.857 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 11 | BIC: | 150.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 176.5614 | 93.410 | 1.890 | 0.085 | -29.032 382.154 |
| C(dose)[T.1] | 0.8342 | 186.165 | 0.004 | 0.997 | -408.913 410.581 |
| expression | -17.6603 | 15.006 | -1.177 | 0.264 | -50.688 15.367 |
| expression:C(dose)[T.1] | 7.7811 | 30.126 | 0.258 | 0.801 | -58.526 74.088 |
| Omnibus: | 2.233 | Durbin-Watson: | 1.006 |
| Prob(Omnibus): | 0.327 | Jarque-Bera (JB): | 1.512 |
| Skew: | -0.755 | Prob(JB): | 0.470 |
| Kurtosis: | 2.632 | Cond. No. | 185. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.513 |
| Model: | OLS | Adj. R-squared: | 0.432 |
| Method: | Least Squares | F-statistic: | 6.322 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0133 |
| Time: | 04:13:22 | Log-Likelihood: | -69.903 |
| No. Observations: | 15 | AIC: | 145.8 |
| Df Residuals: | 12 | BIC: | 147.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 164.6316 | 77.970 | 2.111 | 0.056 | -5.249 334.513 |
| C(dose)[T.1] | 48.7527 | 14.797 | 3.295 | 0.006 | 16.512 80.993 |
| expression | -15.7298 | 12.496 | -1.259 | 0.232 | -42.955 11.496 |
| Omnibus: | 2.264 | Durbin-Watson: | 0.953 |
| Prob(Omnibus): | 0.322 | Jarque-Bera (JB): | 1.564 |
| Skew: | -0.765 | Prob(JB): | 0.458 |
| Kurtosis: | 2.598 | Cond. No. | 67.4 |
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: | 04:13:22 | 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: | 0.073 |
| Model: | OLS | Adj. R-squared: | 0.001 |
| Method: | Least Squares | F-statistic: | 1.018 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.331 |
| Time: | 04:13:22 | Log-Likelihood: | -74.735 |
| No. Observations: | 15 | AIC: | 153.5 |
| Df Residuals: | 13 | BIC: | 154.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 196.6792 | 102.574 | 1.917 | 0.077 | -24.918 418.277 |
| expression | -16.7106 | 16.564 | -1.009 | 0.331 | -52.494 19.073 |
| Omnibus: | 3.683 | Durbin-Watson: | 1.805 |
| Prob(Omnibus): | 0.159 | Jarque-Bera (JB): | 1.376 |
| Skew: | 0.283 | Prob(JB): | 0.503 |
| Kurtosis: | 1.628 | Cond. No. | 66.6 |