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 |
| 2.160 | 0.157 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.700 |
| Model: | OLS | Adj. R-squared: | 0.652 |
| Method: | Least Squares | F-statistic: | 14.74 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.38e-05 |
| Time: | 05:15:38 | Log-Likelihood: | -99.278 |
| No. Observations: | 23 | AIC: | 206.6 |
| Df Residuals: | 19 | BIC: | 211.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 53.5286 | 73.609 | 0.727 | 0.476 | -100.537 207.594 |
| C(dose)[T.1] | 137.7481 | 87.478 | 1.575 | 0.132 | -45.346 320.843 |
| expression | 0.1119 | 12.079 | 0.009 | 0.993 | -25.170 25.394 |
| expression:C(dose)[T.1] | -14.8122 | 14.617 | -1.013 | 0.324 | -45.406 15.782 |
| Omnibus: | 1.383 | Durbin-Watson: | 1.653 |
| Prob(Omnibus): | 0.501 | Jarque-Bera (JB): | 0.453 |
| Skew: | -0.305 | Prob(JB): | 0.797 |
| Kurtosis: | 3.317 | Cond. No. | 180. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.683 |
| Model: | OLS | Adj. R-squared: | 0.652 |
| Method: | Least Squares | F-statistic: | 21.57 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.02e-05 |
| Time: | 05:15:38 | Log-Likelihood: | -99.883 |
| No. Observations: | 23 | AIC: | 205.8 |
| Df Residuals: | 20 | BIC: | 209.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 114.9807 | 41.751 | 2.754 | 0.012 | 27.890 202.071 |
| C(dose)[T.1] | 49.5427 | 8.722 | 5.680 | 0.000 | 31.348 67.737 |
| expression | -10.0034 | 6.807 | -1.470 | 0.157 | -24.202 4.195 |
| Omnibus: | 0.940 | Durbin-Watson: | 1.726 |
| Prob(Omnibus): | 0.625 | Jarque-Bera (JB): | 0.125 |
| Skew: | 0.059 | Prob(JB): | 0.940 |
| Kurtosis: | 3.341 | Cond. No. | 61.5 |
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: | 05:15:38 | 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: | 0.172 |
| Model: | OLS | Adj. R-squared: | 0.133 |
| Method: | Least Squares | F-statistic: | 4.373 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0489 |
| Time: | 05:15:38 | Log-Likelihood: | -110.93 |
| No. Observations: | 23 | AIC: | 225.9 |
| Df Residuals: | 21 | BIC: | 228.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 206.1220 | 60.805 | 3.390 | 0.003 | 79.671 332.573 |
| expression | -21.4471 | 10.256 | -2.091 | 0.049 | -42.777 -0.118 |
| Omnibus: | 10.685 | Durbin-Watson: | 2.143 |
| Prob(Omnibus): | 0.005 | Jarque-Bera (JB): | 2.217 |
| Skew: | 0.085 | Prob(JB): | 0.330 |
| Kurtosis: | 1.489 | Cond. No. | 56.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.409 | 0.534 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.482 |
| Model: | OLS | Adj. R-squared: | 0.341 |
| Method: | Least Squares | F-statistic: | 3.413 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0566 |
| Time: | 05:15:38 | Log-Likelihood: | -70.365 |
| No. Observations: | 15 | AIC: | 148.7 |
| Df Residuals: | 11 | BIC: | 151.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 83.5305 | 95.941 | 0.871 | 0.403 | -127.634 294.695 |
| C(dose)[T.1] | 148.6722 | 170.272 | 0.873 | 0.401 | -226.095 523.439 |
| expression | -2.6584 | 15.723 | -0.169 | 0.869 | -37.264 31.947 |
| expression:C(dose)[T.1] | -15.1582 | 26.732 | -0.567 | 0.582 | -73.995 43.679 |
| Omnibus: | 6.715 | Durbin-Watson: | 0.833 |
| Prob(Omnibus): | 0.035 | Jarque-Bera (JB): | 3.820 |
| Skew: | -1.186 | Prob(JB): | 0.148 |
| Kurtosis: | 3.700 | Cond. No. | 173. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.467 |
| Model: | OLS | Adj. R-squared: | 0.378 |
| Method: | Least Squares | F-statistic: | 5.256 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0229 |
| Time: | 05:15:38 | Log-Likelihood: | -70.581 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 115.2911 | 75.659 | 1.524 | 0.153 | -49.556 280.138 |
| C(dose)[T.1] | 52.5948 | 16.364 | 3.214 | 0.007 | 16.941 88.249 |
| expression | -7.9020 | 12.351 | -0.640 | 0.534 | -34.812 19.008 |
| Omnibus: | 3.940 | Durbin-Watson: | 0.933 |
| Prob(Omnibus): | 0.139 | Jarque-Bera (JB): | 2.390 |
| Skew: | -0.978 | Prob(JB): | 0.303 |
| Kurtosis: | 2.987 | Cond. No. | 63.8 |
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: | 05:15:38 | 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: | 0.008 |
| Model: | OLS | Adj. R-squared: | -0.068 |
| Method: | Least Squares | F-statistic: | 0.1059 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.750 |
| Time: | 05:15:38 | Log-Likelihood: | -75.239 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 62.3387 | 96.780 | 0.644 | 0.531 | -146.743 271.420 |
| expression | 4.9835 | 15.311 | 0.325 | 0.750 | -28.094 38.060 |
| Omnibus: | 0.607 | Durbin-Watson: | 1.551 |
| Prob(Omnibus): | 0.738 | Jarque-Bera (JB): | 0.585 |
| Skew: | 0.067 | Prob(JB): | 0.746 |
| Kurtosis: | 2.042 | Cond. No. | 61.9 |