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 |
| 0.005 | 0.946 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.602 |
| Method: | Least Squares | F-statistic: | 12.11 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000117 |
| Time: | 05:07:20 | Log-Likelihood: | -100.81 |
| No. Observations: | 23 | AIC: | 209.6 |
| Df Residuals: | 19 | BIC: | 214.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 76.0901 | 51.622 | 1.474 | 0.157 | -31.957 184.137 |
| C(dose)[T.1] | 12.9060 | 64.473 | 0.200 | 0.843 | -122.037 147.849 |
| expression | -4.4004 | 10.307 | -0.427 | 0.674 | -25.973 17.173 |
| expression:C(dose)[T.1] | 9.0423 | 14.038 | 0.644 | 0.527 | -20.340 38.425 |
| Omnibus: | 0.679 | Durbin-Watson: | 2.133 |
| Prob(Omnibus): | 0.712 | Jarque-Bera (JB): | 0.658 |
| Skew: | 0.044 | Prob(JB): | 0.720 |
| Kurtosis: | 2.176 | Cond. No. | 91.8 |
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: | 18.50 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.83e-05 |
| Time: | 05:07:20 | 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 | 51.8513 | 34.817 | 1.489 | 0.152 | -20.775 124.478 |
| C(dose)[T.1] | 53.8000 | 11.056 | 4.866 | 0.000 | 30.738 76.862 |
| expression | 0.4740 | 6.895 | 0.069 | 0.946 | -13.908 14.856 |
| Omnibus: | 0.294 | Durbin-Watson: | 1.871 |
| Prob(Omnibus): | 0.863 | Jarque-Bera (JB): | 0.468 |
| Skew: | 0.067 | Prob(JB): | 0.791 |
| Kurtosis: | 2.314 | Cond. No. | 39.0 |
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: | 05:07:20 | 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: | 0.234 |
| Model: | OLS | Adj. R-squared: | 0.197 |
| Method: | Least Squares | F-statistic: | 6.405 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0194 |
| Time: | 05:07:20 | Log-Likelihood: | -110.04 |
| No. Observations: | 23 | AIC: | 224.1 |
| Df Residuals: | 21 | BIC: | 226.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 169.6472 | 36.092 | 4.700 | 0.000 | 94.590 244.704 |
| expression | -19.9595 | 7.887 | -2.531 | 0.019 | -36.361 -3.558 |
| Omnibus: | 0.746 | Durbin-Watson: | 2.674 |
| Prob(Omnibus): | 0.689 | Jarque-Bera (JB): | 0.767 |
| Skew: | 0.253 | Prob(JB): | 0.681 |
| Kurtosis: | 2.262 | Cond. No. | 27.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.087 | 0.773 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.503 |
| Model: | OLS | Adj. R-squared: | 0.367 |
| Method: | Least Squares | F-statistic: | 3.706 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0460 |
| Time: | 05:07:20 | Log-Likelihood: | -70.061 |
| No. Observations: | 15 | AIC: | 148.1 |
| Df Residuals: | 11 | BIC: | 151.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -36.9844 | 192.476 | -0.192 | 0.851 | -460.621 386.652 |
| C(dose)[T.1] | 354.7556 | 286.838 | 1.237 | 0.242 | -276.571 986.082 |
| expression | 16.1423 | 29.705 | 0.543 | 0.598 | -49.237 81.521 |
| expression:C(dose)[T.1] | -43.7262 | 41.603 | -1.051 | 0.316 | -135.293 47.841 |
| Omnibus: | 1.392 | Durbin-Watson: | 0.873 |
| Prob(Omnibus): | 0.499 | Jarque-Bera (JB): | 1.150 |
| Skew: | -0.549 | Prob(JB): | 0.563 |
| Kurtosis: | 2.205 | Cond. No. | 348. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.453 |
| Model: | OLS | Adj. R-squared: | 0.362 |
| Method: | Least Squares | F-statistic: | 4.963 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0269 |
| Time: | 05:07:20 | Log-Likelihood: | -70.779 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 107.2052 | 135.594 | 0.791 | 0.445 | -188.228 402.638 |
| C(dose)[T.1] | 54.2626 | 23.283 | 2.331 | 0.038 | 3.534 104.991 |
| expression | -6.1495 | 20.888 | -0.294 | 0.773 | -51.660 39.361 |
| Omnibus: | 2.107 | Durbin-Watson: | 0.857 |
| Prob(Omnibus): | 0.349 | Jarque-Bera (JB): | 1.581 |
| Skew: | -0.741 | Prob(JB): | 0.454 |
| Kurtosis: | 2.423 | Cond. No. | 124. |
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: | 05:07:20 | 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: | 0.205 |
| Model: | OLS | Adj. R-squared: | 0.144 |
| Method: | Least Squares | F-statistic: | 3.352 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0901 |
| Time: | 05:07:20 | Log-Likelihood: | -73.579 |
| No. Observations: | 15 | AIC: | 151.2 |
| Df Residuals: | 13 | BIC: | 152.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -112.3956 | 112.908 | -0.995 | 0.338 | -356.319 131.527 |
| expression | 29.8309 | 16.293 | 1.831 | 0.090 | -5.367 65.029 |
| Omnibus: | 0.278 | Durbin-Watson: | 1.111 |
| Prob(Omnibus): | 0.870 | Jarque-Bera (JB): | 0.345 |
| Skew: | -0.261 | Prob(JB): | 0.842 |
| Kurtosis: | 2.471 | Cond. No. | 88.1 |