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 |
| 0.174 | 0.681 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.597 |
| Method: | Least Squares | F-statistic: | 11.88 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000131 |
| Time: | 03:43:07 | Log-Likelihood: | -100.96 |
| No. Observations: | 23 | AIC: | 209.9 |
| Df Residuals: | 19 | BIC: | 214.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 79.7383 | 66.072 | 1.207 | 0.242 | -58.552 218.029 |
| C(dose)[T.1] | 43.5875 | 127.748 | 0.341 | 0.737 | -223.792 310.968 |
| expression | -4.6893 | 12.083 | -0.388 | 0.702 | -29.978 20.600 |
| expression:C(dose)[T.1] | 1.8087 | 23.301 | 0.078 | 0.939 | -46.960 50.577 |
| Omnibus: | 0.715 | Durbin-Watson: | 1.873 |
| Prob(Omnibus): | 0.699 | Jarque-Bera (JB): | 0.670 |
| Skew: | 0.019 | Prob(JB): | 0.715 |
| Kurtosis: | 2.165 | Cond. No. | 191. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.617 |
| Method: | Least Squares | F-statistic: | 18.74 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.60e-05 |
| Time: | 03:43:07 | Log-Likelihood: | -100.96 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 77.0904 | 55.162 | 1.398 | 0.178 | -37.975 192.156 |
| C(dose)[T.1] | 53.4797 | 8.739 | 6.120 | 0.000 | 35.251 71.708 |
| expression | -4.2029 | 10.071 | -0.417 | 0.681 | -25.211 16.805 |
| Omnibus: | 0.703 | Durbin-Watson: | 1.886 |
| Prob(Omnibus): | 0.704 | Jarque-Bera (JB): | 0.667 |
| Skew: | 0.031 | Prob(JB): | 0.717 |
| Kurtosis: | 2.168 | Cond. No. | 71.8 |
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: | 03:43:07 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.01161 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.915 |
| Time: | 03:43:07 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 89.5104 | 91.179 | 0.982 | 0.337 | -100.107 279.127 |
| expression | -1.7934 | 16.645 | -0.108 | 0.915 | -36.410 32.823 |
| Omnibus: | 3.208 | Durbin-Watson: | 2.492 |
| Prob(Omnibus): | 0.201 | Jarque-Bera (JB): | 1.552 |
| Skew: | 0.290 | Prob(JB): | 0.460 |
| Kurtosis: | 1.867 | Cond. No. | 71.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.026 | 0.875 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.476 |
| Model: | OLS | Adj. R-squared: | 0.333 |
| Method: | Least Squares | F-statistic: | 3.330 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0601 |
| Time: | 03:43:07 | Log-Likelihood: | -70.454 |
| No. Observations: | 15 | AIC: | 148.9 |
| Df Residuals: | 11 | BIC: | 151.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 89.5448 | 99.370 | 0.901 | 0.387 | -129.167 308.257 |
| C(dose)[T.1] | -96.8679 | 198.876 | -0.487 | 0.636 | -534.591 340.855 |
| expression | -4.6984 | 20.963 | -0.224 | 0.827 | -50.838 41.441 |
| expression:C(dose)[T.1] | 31.5976 | 42.797 | 0.738 | 0.476 | -62.597 125.792 |
| Omnibus: | 2.800 | Durbin-Watson: | 0.924 |
| Prob(Omnibus): | 0.247 | Jarque-Bera (JB): | 2.028 |
| Skew: | -0.866 | Prob(JB): | 0.363 |
| Kurtosis: | 2.504 | Cond. No. | 146. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.450 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.908 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0277 |
| Time: | 03:43:07 | Log-Likelihood: | -70.817 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 53.8573 | 85.160 | 0.632 | 0.539 | -131.691 239.406 |
| C(dose)[T.1] | 49.4828 | 15.823 | 3.127 | 0.009 | 15.007 83.959 |
| expression | 2.8831 | 17.926 | 0.161 | 0.875 | -36.175 41.941 |
| Omnibus: | 2.471 | Durbin-Watson: | 0.804 |
| Prob(Omnibus): | 0.291 | Jarque-Bera (JB): | 1.746 |
| Skew: | -0.806 | Prob(JB): | 0.418 |
| Kurtosis: | 2.557 | Cond. No. | 53.4 |
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: | 03:43:07 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.075 |
| Method: | Least Squares | F-statistic: | 0.02207 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.884 |
| Time: | 03:43:07 | Log-Likelihood: | -75.287 |
| 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 | 109.6084 | 107.785 | 1.017 | 0.328 | -123.247 342.464 |
| expression | -3.4252 | 23.056 | -0.149 | 0.884 | -53.234 46.383 |
| Omnibus: | 0.424 | Durbin-Watson: | 1.644 |
| Prob(Omnibus): | 0.809 | Jarque-Bera (JB): | 0.510 |
| Skew: | 0.044 | Prob(JB): | 0.775 |
| Kurtosis: | 2.101 | Cond. No. | 51.9 |