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 |
| 1.902 | 0.183 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.688 |
| Model: | OLS | Adj. R-squared: | 0.639 |
| Method: | Least Squares | F-statistic: | 13.97 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.77e-05 |
| Time: | 04:33:37 | Log-Likelihood: | -99.706 |
| No. Observations: | 23 | AIC: | 207.4 |
| Df Residuals: | 19 | BIC: | 212.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 38.5164 | 39.211 | 0.982 | 0.338 | -43.553 120.585 |
| C(dose)[T.1] | 10.7883 | 55.734 | 0.194 | 0.849 | -105.864 127.440 |
| expression | 3.0248 | 7.473 | 0.405 | 0.690 | -12.617 18.667 |
| expression:C(dose)[T.1] | 7.4013 | 10.243 | 0.723 | 0.479 | -14.038 28.840 |
| Omnibus: | 0.098 | Durbin-Watson: | 1.835 |
| Prob(Omnibus): | 0.952 | Jarque-Bera (JB): | 0.303 |
| Skew: | 0.095 | Prob(JB): | 0.859 |
| Kurtosis: | 2.471 | Cond. No. | 97.3 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.680 |
| Model: | OLS | Adj. R-squared: | 0.647 |
| Method: | Least Squares | F-statistic: | 21.21 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.14e-05 |
| Time: | 04:33:37 | Log-Likelihood: | -100.02 |
| No. Observations: | 23 | AIC: | 206.0 |
| Df Residuals: | 20 | BIC: | 209.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 18.0782 | 26.828 | 0.674 | 0.508 | -37.885 74.041 |
| C(dose)[T.1] | 50.5627 | 8.618 | 5.867 | 0.000 | 32.585 68.540 |
| expression | 6.9646 | 5.049 | 1.379 | 0.183 | -3.568 17.498 |
| Omnibus: | 0.185 | Durbin-Watson: | 1.852 |
| Prob(Omnibus): | 0.912 | Jarque-Bera (JB): | 0.261 |
| Skew: | 0.181 | Prob(JB): | 0.877 |
| Kurtosis: | 2.624 | Cond. No. | 36.2 |
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: | 04:33:37 | 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: | 0.128 |
| Model: | OLS | Adj. R-squared: | 0.086 |
| Method: | Least Squares | F-statistic: | 3.083 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0937 |
| Time: | 04:33:37 | Log-Likelihood: | -111.53 |
| No. Observations: | 23 | AIC: | 227.1 |
| Df Residuals: | 21 | BIC: | 229.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 5.0744 | 43.041 | 0.118 | 0.907 | -84.434 94.582 |
| expression | 13.8788 | 7.904 | 1.756 | 0.094 | -2.559 30.316 |
| Omnibus: | 4.977 | Durbin-Watson: | 2.010 |
| Prob(Omnibus): | 0.083 | Jarque-Bera (JB): | 1.728 |
| Skew: | 0.203 | Prob(JB): | 0.422 |
| Kurtosis: | 1.720 | Cond. No. | 35.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.045 | 0.836 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.302 |
| Method: | Least Squares | F-statistic: | 3.019 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0757 |
| Time: | 04:33:37 | Log-Likelihood: | -70.794 |
| No. Observations: | 15 | AIC: | 149.6 |
| Df Residuals: | 11 | BIC: | 152.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 85.8378 | 78.204 | 1.098 | 0.296 | -86.289 257.965 |
| C(dose)[T.1] | 30.1248 | 140.788 | 0.214 | 0.834 | -279.748 339.998 |
| expression | -2.9135 | 12.231 | -0.238 | 0.816 | -29.834 24.007 |
| expression:C(dose)[T.1] | 3.0321 | 24.183 | 0.125 | 0.902 | -50.195 56.259 |
| Omnibus: | 2.554 | Durbin-Watson: | 0.854 |
| Prob(Omnibus): | 0.279 | Jarque-Bera (JB): | 1.793 |
| Skew: | -0.820 | Prob(JB): | 0.408 |
| Kurtosis: | 2.574 | Cond. No. | 126. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.359 |
| Method: | Least Squares | F-statistic: | 4.925 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0274 |
| Time: | 04:33:37 | Log-Likelihood: | -70.805 |
| 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 | 80.9372 | 64.899 | 1.247 | 0.236 | -60.465 222.340 |
| C(dose)[T.1] | 47.6298 | 17.369 | 2.742 | 0.018 | 9.785 85.474 |
| expression | -2.1379 | 10.109 | -0.211 | 0.836 | -24.164 19.888 |
| Omnibus: | 2.235 | Durbin-Watson: | 0.858 |
| Prob(Omnibus): | 0.327 | Jarque-Bera (JB): | 1.635 |
| Skew: | -0.766 | Prob(JB): | 0.442 |
| Kurtosis: | 2.482 | Cond. No. | 51.6 |
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: | 04:33:37 | 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: | 0.107 |
| Model: | OLS | Adj. R-squared: | 0.038 |
| Method: | Least Squares | F-statistic: | 1.553 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.235 |
| Time: | 04:33:37 | Log-Likelihood: | -74.454 |
| No. Observations: | 15 | AIC: | 152.9 |
| Df Residuals: | 13 | BIC: | 154.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 176.4246 | 67.108 | 2.629 | 0.021 | 31.447 321.402 |
| expression | -13.9611 | 11.204 | -1.246 | 0.235 | -38.167 10.245 |
| Omnibus: | 0.554 | Durbin-Watson: | 1.469 |
| Prob(Omnibus): | 0.758 | Jarque-Bera (JB): | 0.584 |
| Skew: | 0.165 | Prob(JB): | 0.747 |
| Kurtosis: | 2.092 | Cond. No. | 43.0 |