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 |
| 0.075 | 0.787 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.718 |
| Model: | OLS | Adj. R-squared: | 0.673 |
| Method: | Least Squares | F-statistic: | 16.09 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.89e-05 |
| Time: | 05:15:03 | Log-Likelihood: | -98.565 |
| No. Observations: | 23 | AIC: | 205.1 |
| Df Residuals: | 19 | BIC: | 209.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -27.7853 | 73.820 | -0.376 | 0.711 | -182.293 126.722 |
| C(dose)[T.1] | 291.2409 | 112.695 | 2.584 | 0.018 | 55.367 527.115 |
| expression | 12.7831 | 11.476 | 1.114 | 0.279 | -11.236 36.802 |
| expression:C(dose)[T.1] | -38.5924 | 18.151 | -2.126 | 0.047 | -76.583 -0.602 |
| Omnibus: | 0.656 | Durbin-Watson: | 1.491 |
| Prob(Omnibus): | 0.720 | Jarque-Bera (JB): | 0.710 |
| Skew: | 0.323 | Prob(JB): | 0.701 |
| Kurtosis: | 2.432 | Cond. No. | 222. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.60 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.73e-05 |
| Time: | 05:15:03 | Log-Likelihood: | -101.02 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 71.1641 | 62.142 | 1.145 | 0.266 | -58.461 200.789 |
| C(dose)[T.1] | 52.3502 | 9.465 | 5.531 | 0.000 | 32.607 72.093 |
| expression | -2.6435 | 9.642 | -0.274 | 0.787 | -22.756 17.469 |
| Omnibus: | 0.559 | Durbin-Watson: | 1.943 |
| Prob(Omnibus): | 0.756 | Jarque-Bera (JB): | 0.618 |
| Skew: | 0.111 | Prob(JB): | 0.734 |
| Kurtosis: | 2.228 | Cond. No. | 91.5 |
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: | 05:15:03 | 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: | 0.116 |
| Model: | OLS | Adj. R-squared: | 0.073 |
| Method: | Least Squares | F-statistic: | 2.744 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.112 |
| Time: | 05:15:03 | Log-Likelihood: | -111.69 |
| No. Observations: | 23 | AIC: | 227.4 |
| Df Residuals: | 21 | BIC: | 229.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 222.6866 | 86.575 | 2.572 | 0.018 | 42.644 402.729 |
| expression | -22.9277 | 13.841 | -1.656 | 0.112 | -51.712 5.856 |
| Omnibus: | 0.745 | Durbin-Watson: | 2.774 |
| Prob(Omnibus): | 0.689 | Jarque-Bera (JB): | 0.689 |
| Skew: | -0.068 | Prob(JB): | 0.709 |
| Kurtosis: | 2.163 | Cond. No. | 81.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.533 | 0.480 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.529 |
| Model: | OLS | Adj. R-squared: | 0.401 |
| Method: | Least Squares | F-statistic: | 4.124 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0347 |
| Time: | 05:15:03 | Log-Likelihood: | -69.648 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 11 | BIC: | 150.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 64.1115 | 99.513 | 0.644 | 0.533 | -154.915 283.138 |
| C(dose)[T.1] | 265.3965 | 184.660 | 1.437 | 0.178 | -141.038 671.831 |
| expression | 0.5605 | 16.709 | 0.034 | 0.974 | -36.217 37.338 |
| expression:C(dose)[T.1] | -34.4954 | 29.855 | -1.155 | 0.272 | -100.206 31.215 |
| Omnibus: | 0.527 | Durbin-Watson: | 1.226 |
| Prob(Omnibus): | 0.768 | Jarque-Bera (JB): | 0.537 |
| Skew: | -0.358 | Prob(JB): | 0.765 |
| Kurtosis: | 2.410 | Cond. No. | 189. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.472 |
| Model: | OLS | Adj. R-squared: | 0.384 |
| Method: | Least Squares | F-statistic: | 5.368 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0216 |
| Time: | 05:15:03 | Log-Likelihood: | -70.507 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 12 | BIC: | 149.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 128.0638 | 83.847 | 1.527 | 0.153 | -54.622 310.750 |
| C(dose)[T.1] | 52.8324 | 16.187 | 3.264 | 0.007 | 17.563 88.102 |
| expression | -10.2452 | 14.039 | -0.730 | 0.480 | -40.834 20.343 |
| Omnibus: | 1.800 | Durbin-Watson: | 0.757 |
| Prob(Omnibus): | 0.407 | Jarque-Bera (JB): | 1.332 |
| Skew: | -0.682 | Prob(JB): | 0.514 |
| Kurtosis: | 2.479 | Cond. No. | 69.1 |
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: | 05:15:03 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.073 |
| Method: | Least Squares | F-statistic: | 0.04788 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.830 |
| Time: | 05:15:03 | Log-Likelihood: | -75.272 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 70.1026 | 108.169 | 0.648 | 0.528 | -163.583 303.788 |
| expression | 3.8581 | 17.632 | 0.219 | 0.830 | -34.234 41.951 |
| Omnibus: | 0.626 | Durbin-Watson: | 1.554 |
| Prob(Omnibus): | 0.731 | Jarque-Bera (JB): | 0.589 |
| Skew: | 0.042 | Prob(JB): | 0.745 |
| Kurtosis: | 2.033 | Cond. No. | 67.1 |