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 |
| 7.404 | 0.013 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.747 |
| Model: | OLS | Adj. R-squared: | 0.707 |
| Method: | Least Squares | F-statistic: | 18.69 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.80e-06 |
| Time: | 04:28:10 | Log-Likelihood: | -97.303 |
| No. Observations: | 23 | AIC: | 202.6 |
| Df Residuals: | 19 | BIC: | 207.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 253.9029 | 181.920 | 1.396 | 0.179 | -126.860 634.666 |
| C(dose)[T.1] | 171.9275 | 222.756 | 0.772 | 0.450 | -294.305 638.160 |
| expression | -28.4444 | 25.902 | -1.098 | 0.286 | -82.657 25.768 |
| expression:C(dose)[T.1] | -14.9662 | 31.269 | -0.479 | 0.638 | -80.412 50.480 |
| Omnibus: | 0.989 | Durbin-Watson: | 1.630 |
| Prob(Omnibus): | 0.610 | Jarque-Bera (JB): | 0.842 |
| Skew: | 0.428 | Prob(JB): | 0.656 |
| Kurtosis: | 2.620 | Cond. No. | 603. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.744 |
| Model: | OLS | Adj. R-squared: | 0.718 |
| Method: | Least Squares | F-statistic: | 29.04 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.21e-06 |
| Time: | 04:28:10 | Log-Likelihood: | -97.441 |
| No. Observations: | 23 | AIC: | 200.9 |
| Df Residuals: | 20 | BIC: | 204.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 326.0002 | 100.020 | 3.259 | 0.004 | 117.362 534.639 |
| C(dose)[T.1] | 65.3939 | 8.704 | 7.513 | 0.000 | 47.237 83.551 |
| expression | -38.7139 | 14.228 | -2.721 | 0.013 | -68.392 -9.035 |
| Omnibus: | 0.893 | Durbin-Watson: | 1.658 |
| Prob(Omnibus): | 0.640 | Jarque-Bera (JB): | 0.893 |
| Skew: | 0.354 | Prob(JB): | 0.640 |
| Kurtosis: | 2.344 | Cond. No. | 196. |
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: | 04:28:10 | 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: | 0.021 |
| Model: | OLS | Adj. R-squared: | -0.026 |
| Method: | Least Squares | F-statistic: | 0.4515 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.509 |
| Time: | 04:28:10 | Log-Likelihood: | -112.86 |
| No. Observations: | 23 | AIC: | 229.7 |
| Df Residuals: | 21 | BIC: | 232.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -32.8399 | 167.665 | -0.196 | 0.847 | -381.519 315.839 |
| expression | 15.6995 | 23.365 | 0.672 | 0.509 | -32.890 64.289 |
| Omnibus: | 1.843 | Durbin-Watson: | 2.416 |
| Prob(Omnibus): | 0.398 | Jarque-Bera (JB): | 1.310 |
| Skew: | 0.354 | Prob(JB): | 0.520 |
| Kurtosis: | 2.069 | Cond. No. | 172. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.058 | 0.813 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.458 |
| Model: | OLS | Adj. R-squared: | 0.311 |
| Method: | Least Squares | F-statistic: | 3.103 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0711 |
| Time: | 04:28:10 | Log-Likelihood: | -70.701 |
| No. Observations: | 15 | AIC: | 149.4 |
| Df Residuals: | 11 | BIC: | 152.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 229.9469 | 368.794 | 0.624 | 0.546 | -581.763 1041.657 |
| C(dose)[T.1] | -109.8142 | 424.284 | -0.259 | 0.801 | -1043.656 824.028 |
| expression | -23.5862 | 53.495 | -0.441 | 0.668 | -141.328 94.156 |
| expression:C(dose)[T.1] | 23.0772 | 61.531 | 0.375 | 0.715 | -112.352 158.506 |
| Omnibus: | 2.636 | Durbin-Watson: | 0.839 |
| Prob(Omnibus): | 0.268 | Jarque-Bera (JB): | 1.874 |
| Skew: | -0.836 | Prob(JB): | 0.392 |
| Kurtosis: | 2.547 | Cond. No. | 547. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 4.938 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0273 |
| Time: | 04:28:10 | Log-Likelihood: | -70.797 |
| 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 | 109.7573 | 175.860 | 0.624 | 0.544 | -273.409 492.924 |
| C(dose)[T.1] | 49.1959 | 15.702 | 3.133 | 0.009 | 14.985 83.407 |
| expression | -6.1432 | 25.468 | -0.241 | 0.813 | -61.634 49.347 |
| Omnibus: | 2.807 | Durbin-Watson: | 0.796 |
| Prob(Omnibus): | 0.246 | Jarque-Bera (JB): | 2.010 |
| Skew: | -0.866 | Prob(JB): | 0.366 |
| Kurtosis: | 2.534 | Cond. No. | 159. |
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: | 04:28:10 | 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: | 0.003 |
| Model: | OLS | Adj. R-squared: | -0.074 |
| Method: | Least Squares | F-statistic: | 0.03479 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.855 |
| Time: | 04:28:10 | Log-Likelihood: | -75.280 |
| 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 | 136.0714 | 227.560 | 0.598 | 0.560 | -355.542 627.684 |
| expression | -6.1542 | 32.993 | -0.187 | 0.855 | -77.431 65.123 |
| Omnibus: | 0.809 | Durbin-Watson: | 1.662 |
| Prob(Omnibus): | 0.667 | Jarque-Bera (JB): | 0.652 |
| Skew: | 0.051 | Prob(JB): | 0.722 |
| Kurtosis: | 1.984 | Cond. No. | 158. |