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: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.599 |
| Method: | Least Squares | F-statistic: | 11.97 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000125 |
| Time: | 04:42:05 | Log-Likelihood: | -100.90 |
| No. Observations: | 23 | AIC: | 209.8 |
| Df Residuals: | 19 | BIC: | 214.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 57.6626 | 53.214 | 1.084 | 0.292 | -53.715 169.040 |
| C(dose)[T.1] | 74.9587 | 72.285 | 1.037 | 0.313 | -76.335 226.253 |
| expression | -0.5421 | 8.295 | -0.065 | 0.949 | -17.904 16.820 |
| expression:C(dose)[T.1] | -3.8517 | 11.875 | -0.324 | 0.749 | -28.706 21.003 |
| Omnibus: | 0.281 | Durbin-Watson: | 1.961 |
| Prob(Omnibus): | 0.869 | Jarque-Bera (JB): | 0.459 |
| Skew: | 0.004 | Prob(JB): | 0.795 |
| Kurtosis: | 2.308 | Cond. No. | 131. |
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: | 04:42:05 | 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 | 69.6379 | 37.455 | 1.859 | 0.078 | -8.492 147.768 |
| C(dose)[T.1] | 51.7276 | 9.545 | 5.419 | 0.000 | 31.816 71.639 |
| expression | -2.4216 | 5.801 | -0.417 | 0.681 | -14.523 9.680 |
| Omnibus: | 0.341 | Durbin-Watson: | 1.932 |
| Prob(Omnibus): | 0.843 | Jarque-Bera (JB): | 0.496 |
| Skew: | 0.058 | Prob(JB): | 0.780 |
| Kurtosis: | 2.290 | Cond. No. | 54.4 |
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: | 04:42:05 | 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: | 0.141 |
| Model: | OLS | Adj. R-squared: | 0.100 |
| Method: | Least Squares | F-statistic: | 3.454 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0772 |
| Time: | 04:42:06 | Log-Likelihood: | -111.35 |
| No. Observations: | 23 | AIC: | 226.7 |
| Df Residuals: | 21 | BIC: | 229.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 171.2619 | 49.711 | 3.445 | 0.002 | 67.882 274.642 |
| expression | -15.1217 | 8.137 | -1.858 | 0.077 | -32.043 1.800 |
| Omnibus: | 2.561 | Durbin-Watson: | 2.439 |
| Prob(Omnibus): | 0.278 | Jarque-Bera (JB): | 1.214 |
| Skew: | 0.094 | Prob(JB): | 0.545 |
| Kurtosis: | 1.890 | Cond. No. | 46.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.507 | 0.490 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.512 |
| Model: | OLS | Adj. R-squared: | 0.378 |
| Method: | Least Squares | F-statistic: | 3.842 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0419 |
| Time: | 04:42:06 | Log-Likelihood: | -69.924 |
| No. Observations: | 15 | AIC: | 147.8 |
| Df Residuals: | 11 | BIC: | 150.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 228.9672 | 140.531 | 1.629 | 0.132 | -80.340 538.274 |
| C(dose)[T.1] | -161.1093 | 217.466 | -0.741 | 0.474 | -639.748 317.529 |
| expression | -21.5007 | 18.644 | -1.153 | 0.273 | -62.536 19.535 |
| expression:C(dose)[T.1] | 28.2576 | 29.567 | 0.956 | 0.360 | -36.818 93.333 |
| Omnibus: | 3.575 | Durbin-Watson: | 1.082 |
| Prob(Omnibus): | 0.167 | Jarque-Bera (JB): | 1.929 |
| Skew: | -0.875 | Prob(JB): | 0.381 |
| Kurtosis: | 3.145 | Cond. No. | 269. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.471 |
| Model: | OLS | Adj. R-squared: | 0.383 |
| Method: | Least Squares | F-statistic: | 5.345 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0219 |
| Time: | 04:42:06 | Log-Likelihood: | -70.523 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 12 | BIC: | 149.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 144.5496 | 108.908 | 1.327 | 0.209 | -92.740 381.839 |
| C(dose)[T.1] | 46.1605 | 15.996 | 2.886 | 0.014 | 11.308 81.013 |
| expression | -10.2648 | 14.418 | -0.712 | 0.490 | -41.679 21.149 |
| Omnibus: | 2.238 | Durbin-Watson: | 0.925 |
| Prob(Omnibus): | 0.327 | Jarque-Bera (JB): | 1.690 |
| Skew: | -0.764 | Prob(JB): | 0.430 |
| Kurtosis: | 2.391 | Cond. No. | 107. |
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: | 04:42:06 | 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: | 0.104 |
| Model: | OLS | Adj. R-squared: | 0.035 |
| Method: | Least Squares | F-statistic: | 1.510 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.241 |
| Time: | 04:42:06 | Log-Likelihood: | -74.476 |
| No. Observations: | 15 | AIC: | 153.0 |
| Df Residuals: | 13 | BIC: | 154.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 250.7499 | 128.173 | 1.956 | 0.072 | -26.151 527.650 |
| expression | -21.3561 | 17.376 | -1.229 | 0.241 | -58.896 16.183 |
| Omnibus: | 2.636 | Durbin-Watson: | 1.746 |
| Prob(Omnibus): | 0.268 | Jarque-Bera (JB): | 1.299 |
| Skew: | 0.367 | Prob(JB): | 0.522 |
| Kurtosis: | 1.759 | Cond. No. | 100. |