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 |
| 0.007 | 0.932 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.679 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 13.41 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.21e-05 |
| Time: | 04:57:18 | Log-Likelihood: | -100.03 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 19 | BIC: | 212.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 104.2344 | 50.696 | 2.056 | 0.054 | -1.873 210.341 |
| C(dose)[T.1] | -32.8815 | 65.252 | -0.504 | 0.620 | -169.455 103.692 |
| expression | -15.2059 | 15.303 | -0.994 | 0.333 | -47.235 16.823 |
| expression:C(dose)[T.1] | 25.9047 | 19.450 | 1.332 | 0.199 | -14.804 66.613 |
| Omnibus: | 0.348 | Durbin-Watson: | 1.763 |
| Prob(Omnibus): | 0.840 | Jarque-Bera (JB): | 0.505 |
| Skew: | -0.190 | Prob(JB): | 0.777 |
| Kurtosis: | 2.382 | Cond. No. | 76.0 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.51 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.82e-05 |
| Time: | 04:57:19 | Log-Likelihood: | -101.06 |
| 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 | 51.4764 | 32.244 | 1.596 | 0.126 | -15.784 118.737 |
| C(dose)[T.1] | 53.2599 | 8.814 | 6.043 | 0.000 | 34.875 71.645 |
| expression | 0.8304 | 9.626 | 0.086 | 0.932 | -19.249 20.910 |
| Omnibus: | 0.307 | Durbin-Watson: | 1.881 |
| Prob(Omnibus): | 0.858 | Jarque-Bera (JB): | 0.475 |
| Skew: | 0.048 | Prob(JB): | 0.788 |
| Kurtosis: | 2.302 | Cond. No. | 27.2 |
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: | 04:57:19 | 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: | 0.009 |
| Model: | OLS | Adj. R-squared: | -0.039 |
| Method: | Least Squares | F-statistic: | 0.1839 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.672 |
| Time: | 04:57:19 | Log-Likelihood: | -113.00 |
| No. Observations: | 23 | AIC: | 230.0 |
| Df Residuals: | 21 | BIC: | 232.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 57.2557 | 52.873 | 1.083 | 0.291 | -52.701 167.212 |
| expression | 6.7364 | 15.710 | 0.429 | 0.672 | -25.934 39.407 |
| Omnibus: | 4.207 | Durbin-Watson: | 2.454 |
| Prob(Omnibus): | 0.122 | Jarque-Bera (JB): | 1.613 |
| Skew: | 0.205 | Prob(JB): | 0.446 |
| Kurtosis: | 1.769 | Cond. No. | 26.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.127 | 0.728 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.507 |
| Model: | OLS | Adj. R-squared: | 0.373 |
| Method: | Least Squares | F-statistic: | 3.776 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0438 |
| Time: | 04:57:19 | Log-Likelihood: | -69.990 |
| No. Observations: | 15 | AIC: | 148.0 |
| Df Residuals: | 11 | BIC: | 150.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 116.3234 | 101.269 | 1.149 | 0.275 | -106.569 339.216 |
| C(dose)[T.1] | -107.9913 | 145.968 | -0.740 | 0.475 | -429.265 213.283 |
| expression | -13.1320 | 27.027 | -0.486 | 0.637 | -72.619 46.355 |
| expression:C(dose)[T.1] | 42.6918 | 39.313 | 1.086 | 0.301 | -43.835 129.218 |
| Omnibus: | 2.937 | Durbin-Watson: | 0.827 |
| Prob(Omnibus): | 0.230 | Jarque-Bera (JB): | 1.726 |
| Skew: | -0.830 | Prob(JB): | 0.422 |
| Kurtosis: | 2.910 | Cond. No. | 98.3 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.364 |
| Method: | Least Squares | F-statistic: | 5.000 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0263 |
| Time: | 04:57:19 | Log-Likelihood: | -70.754 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 41.1929 | 74.504 | 0.553 | 0.590 | -121.137 203.523 |
| C(dose)[T.1] | 49.6178 | 15.702 | 3.160 | 0.008 | 15.407 83.829 |
| expression | 7.0463 | 19.773 | 0.356 | 0.728 | -36.035 50.128 |
| Omnibus: | 3.250 | Durbin-Watson: | 0.786 |
| Prob(Omnibus): | 0.197 | Jarque-Bera (JB): | 2.189 |
| Skew: | -0.924 | Prob(JB): | 0.335 |
| Kurtosis: | 2.699 | Cond. No. | 38.3 |
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: | 04:57:19 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.076 |
| Method: | Least Squares | F-statistic: | 0.008335 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.929 |
| Time: | 04:57:19 | Log-Likelihood: | -75.295 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 85.0255 | 95.196 | 0.893 | 0.388 | -120.633 290.684 |
| expression | 2.3409 | 25.641 | 0.091 | 0.929 | -53.053 57.735 |
| Omnibus: | 0.536 | Durbin-Watson: | 1.624 |
| Prob(Omnibus): | 0.765 | Jarque-Bera (JB): | 0.554 |
| Skew: | 0.013 | Prob(JB): | 0.758 |
| Kurtosis: | 2.059 | Cond. No. | 37.3 |