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 |
| 0.539 | 0.471 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.707 |
| Model: | OLS | Adj. R-squared: | 0.661 |
| Method: | Least Squares | F-statistic: | 15.31 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.63e-05 |
| Time: | 05:17:39 | Log-Likelihood: | -98.972 |
| No. Observations: | 23 | AIC: | 205.9 |
| Df Residuals: | 19 | BIC: | 210.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -817.0148 | 447.708 | -1.825 | 0.084 | -1754.079 120.050 |
| C(dose)[T.1] | 919.0908 | 483.441 | 1.901 | 0.073 | -92.762 1930.944 |
| expression | 92.5152 | 47.538 | 1.946 | 0.067 | -6.984 192.014 |
| expression:C(dose)[T.1] | -91.9238 | 51.463 | -1.786 | 0.090 | -199.638 15.791 |
| Omnibus: | 0.553 | Durbin-Watson: | 1.779 |
| Prob(Omnibus): | 0.759 | Jarque-Bera (JB): | 0.632 |
| Skew: | -0.296 | Prob(JB): | 0.729 |
| Kurtosis: | 2.445 | Cond. No. | 1.68e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 19.26 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.17e-05 |
| Time: | 05:17:39 | Log-Likelihood: | -100.76 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -78.3737 | 180.725 | -0.434 | 0.669 | -455.360 298.612 |
| C(dose)[T.1] | 55.7140 | 9.240 | 6.030 | 0.000 | 36.440 74.988 |
| expression | 14.0789 | 19.181 | 0.734 | 0.471 | -25.931 54.089 |
| Omnibus: | 0.131 | Durbin-Watson: | 1.805 |
| Prob(Omnibus): | 0.936 | Jarque-Bera (JB): | 0.349 |
| Skew: | -0.064 | Prob(JB): | 0.840 |
| Kurtosis: | 2.410 | Cond. No. | 395. |
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: | 05:17:39 | 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: | 0.037 |
| Model: | OLS | Adj. R-squared: | -0.009 |
| Method: | Least Squares | F-statistic: | 0.8079 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.379 |
| Time: | 05:17:39 | Log-Likelihood: | -112.67 |
| No. Observations: | 23 | AIC: | 229.3 |
| Df Residuals: | 21 | BIC: | 231.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 326.6745 | 274.848 | 1.189 | 0.248 | -244.903 898.252 |
| expression | -26.4512 | 29.429 | -0.899 | 0.379 | -87.652 34.749 |
| Omnibus: | 1.790 | Durbin-Watson: | 2.551 |
| Prob(Omnibus): | 0.409 | Jarque-Bera (JB): | 1.256 |
| Skew: | 0.328 | Prob(JB): | 0.534 |
| Kurtosis: | 2.062 | Cond. No. | 367. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.164 | 0.693 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.489 |
| Model: | OLS | Adj. R-squared: | 0.349 |
| Method: | Least Squares | F-statistic: | 3.503 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0531 |
| Time: | 05:17:39 | Log-Likelihood: | -70.271 |
| No. Observations: | 15 | AIC: | 148.5 |
| Df Residuals: | 11 | BIC: | 151.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 188.9057 | 144.374 | 1.308 | 0.217 | -128.860 506.671 |
| C(dose)[T.1] | -141.0657 | 232.295 | -0.607 | 0.556 | -652.344 370.212 |
| expression | -16.4708 | 19.512 | -0.844 | 0.417 | -59.417 26.476 |
| expression:C(dose)[T.1] | 25.2043 | 30.211 | 0.834 | 0.422 | -41.290 91.699 |
| Omnibus: | 3.863 | Durbin-Watson: | 1.082 |
| Prob(Omnibus): | 0.145 | Jarque-Bera (JB): | 2.375 |
| Skew: | -0.974 | Prob(JB): | 0.305 |
| Kurtosis: | 2.949 | Cond. No. | 294. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.456 |
| Model: | OLS | Adj. R-squared: | 0.366 |
| Method: | Least Squares | F-statistic: | 5.034 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0259 |
| Time: | 05:17:39 | Log-Likelihood: | -70.731 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 111.3635 | 109.067 | 1.021 | 0.327 | -126.273 349.000 |
| C(dose)[T.1] | 52.1791 | 17.281 | 3.020 | 0.011 | 14.528 89.830 |
| expression | -5.9570 | 14.707 | -0.405 | 0.693 | -38.001 26.087 |
| Omnibus: | 2.625 | Durbin-Watson: | 0.938 |
| Prob(Omnibus): | 0.269 | Jarque-Bera (JB): | 1.770 |
| Skew: | -0.824 | Prob(JB): | 0.413 |
| Kurtosis: | 2.655 | Cond. No. | 110. |
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: | 05:17:39 | 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: | 0.043 |
| Model: | OLS | Adj. R-squared: | -0.031 |
| Method: | Least Squares | F-statistic: | 0.5847 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.458 |
| Time: | 05:17:39 | Log-Likelihood: | -74.970 |
| No. Observations: | 15 | AIC: | 153.9 |
| Df Residuals: | 13 | BIC: | 155.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -5.4253 | 129.975 | -0.042 | 0.967 | -286.219 275.368 |
| expression | 12.9662 | 16.957 | 0.765 | 0.458 | -23.668 49.600 |
| Omnibus: | 0.741 | Durbin-Watson: | 1.330 |
| Prob(Omnibus): | 0.690 | Jarque-Bera (JB): | 0.645 |
| Skew: | -0.130 | Prob(JB): | 0.724 |
| Kurtosis: | 2.017 | Cond. No. | 102. |