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 |
| 0.327 | 0.574 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.669 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 12.78 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.38e-05 |
| Time: | 04:51:45 | Log-Likelihood: | -100.40 |
| No. Observations: | 23 | AIC: | 208.8 |
| Df Residuals: | 19 | BIC: | 213.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -110.2565 | 157.909 | -0.698 | 0.493 | -440.764 220.251 |
| C(dose)[T.1] | 244.9848 | 214.158 | 1.144 | 0.267 | -203.253 693.222 |
| expression | 19.1308 | 18.355 | 1.042 | 0.310 | -19.286 57.548 |
| expression:C(dose)[T.1] | -22.3184 | 24.983 | -0.893 | 0.383 | -74.608 29.971 |
| Omnibus: | 0.214 | Durbin-Watson: | 1.951 |
| Prob(Omnibus): | 0.899 | Jarque-Bera (JB): | 0.415 |
| Skew: | 0.024 | Prob(JB): | 0.812 |
| Kurtosis: | 2.343 | Cond. No. | 562. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.655 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 18.96 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.41e-05 |
| Time: | 04:51:45 | Log-Likelihood: | -100.88 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -6.6888 | 106.674 | -0.063 | 0.951 | -229.206 215.828 |
| C(dose)[T.1] | 53.8266 | 8.741 | 6.158 | 0.000 | 35.593 72.060 |
| expression | 7.0837 | 12.389 | 0.572 | 0.574 | -18.759 32.926 |
| Omnibus: | 1.610 | Durbin-Watson: | 2.021 |
| Prob(Omnibus): | 0.447 | Jarque-Bera (JB): | 1.030 |
| Skew: | 0.172 | Prob(JB): | 0.598 |
| Kurtosis: | 2.022 | Cond. No. | 214. |
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: | 04:51:45 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.048 |
| Method: | Least Squares | F-statistic: | 0.0003590 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.985 |
| Time: | 04:51:45 | 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 | 83.0399 | 175.497 | 0.473 | 0.641 | -281.927 448.007 |
| expression | -0.3880 | 20.476 | -0.019 | 0.985 | -42.969 42.193 |
| Omnibus: | 3.311 | Durbin-Watson: | 2.483 |
| Prob(Omnibus): | 0.191 | Jarque-Bera (JB): | 1.565 |
| Skew: | 0.285 | Prob(JB): | 0.457 |
| Kurtosis: | 1.857 | Cond. No. | 211. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 10.953 | 0.006 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.792 |
| Model: | OLS | Adj. R-squared: | 0.735 |
| Method: | Least Squares | F-statistic: | 13.96 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000455 |
| Time: | 04:51:45 | Log-Likelihood: | -63.525 |
| No. Observations: | 15 | AIC: | 135.0 |
| Df Residuals: | 11 | BIC: | 137.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -265.3279 | 247.726 | -1.071 | 0.307 | -810.569 279.913 |
| C(dose)[T.1] | -697.9069 | 364.385 | -1.915 | 0.082 | -1499.912 104.098 |
| expression | 35.6764 | 26.548 | 1.344 | 0.206 | -22.756 94.108 |
| expression:C(dose)[T.1] | 80.5766 | 39.139 | 2.059 | 0.064 | -5.567 166.721 |
| Omnibus: | 1.003 | Durbin-Watson: | 0.814 |
| Prob(Omnibus): | 0.606 | Jarque-Bera (JB): | 0.881 |
| Skew: | -0.486 | Prob(JB): | 0.644 |
| Kurtosis: | 2.319 | Cond. No. | 889. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.712 |
| Model: | OLS | Adj. R-squared: | 0.664 |
| Method: | Least Squares | F-statistic: | 14.82 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000573 |
| Time: | 04:51:45 | Log-Likelihood: | -65.969 |
| No. Observations: | 15 | AIC: | 137.9 |
| Df Residuals: | 12 | BIC: | 140.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -611.1117 | 205.197 | -2.978 | 0.012 | -1058.198 -164.025 |
| C(dose)[T.1] | 51.9747 | 11.412 | 4.555 | 0.001 | 27.111 76.839 |
| expression | 72.7496 | 21.982 | 3.309 | 0.006 | 24.855 120.645 |
| Omnibus: | 4.374 | Durbin-Watson: | 0.692 |
| Prob(Omnibus): | 0.112 | Jarque-Bera (JB): | 1.420 |
| Skew: | 0.231 | Prob(JB): | 0.492 |
| Kurtosis: | 1.565 | Cond. No. | 341. |
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: | 04:51:45 | 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: | 0.214 |
| Model: | OLS | Adj. R-squared: | 0.153 |
| Method: | Least Squares | F-statistic: | 3.532 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0828 |
| Time: | 04:51:45 | Log-Likelihood: | -73.498 |
| No. Observations: | 15 | AIC: | 151.0 |
| Df Residuals: | 13 | BIC: | 152.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -514.8454 | 323.928 | -1.589 | 0.136 | -1214.650 184.959 |
| expression | 65.3843 | 34.792 | 1.879 | 0.083 | -9.780 140.549 |
| Omnibus: | 1.358 | Durbin-Watson: | 2.205 |
| Prob(Omnibus): | 0.507 | Jarque-Bera (JB): | 0.810 |
| Skew: | -0.072 | Prob(JB): | 0.667 |
| Kurtosis: | 1.871 | Cond. No. | 339. |