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 |
| 0.730 | 0.403 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.667 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 12.71 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.67e-05 |
| Time: | 04:47:13 | Log-Likelihood: | -100.44 |
| No. Observations: | 23 | AIC: | 208.9 |
| Df Residuals: | 19 | BIC: | 213.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 51.9143 | 53.815 | 0.965 | 0.347 | -60.723 164.551 |
| C(dose)[T.1] | 11.3843 | 69.313 | 0.164 | 0.871 | -133.689 156.457 |
| expression | 0.5598 | 13.049 | 0.043 | 0.966 | -26.752 27.871 |
| expression:C(dose)[T.1] | 9.6186 | 16.405 | 0.586 | 0.565 | -24.718 43.955 |
| Omnibus: | 1.137 | Durbin-Watson: | 1.950 |
| Prob(Omnibus): | 0.566 | Jarque-Bera (JB): | 1.050 |
| Skew: | 0.374 | Prob(JB): | 0.592 |
| Kurtosis: | 2.267 | Cond. No. | 97.9 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 19.53 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.98e-05 |
| Time: | 04:47:13 | Log-Likelihood: | -100.65 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 26.9760 | 32.424 | 0.832 | 0.415 | -40.660 94.612 |
| C(dose)[T.1] | 51.6814 | 8.829 | 5.853 | 0.000 | 33.264 70.099 |
| expression | 6.6453 | 7.778 | 0.854 | 0.403 | -9.579 22.869 |
| Omnibus: | 1.420 | Durbin-Watson: | 1.998 |
| Prob(Omnibus): | 0.492 | Jarque-Bera (JB): | 0.943 |
| Skew: | 0.124 | Prob(JB): | 0.624 |
| Kurtosis: | 2.040 | Cond. No. | 34.1 |
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: | 04:47:13 | 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: | 0.081 |
| Model: | OLS | Adj. R-squared: | 0.038 |
| Method: | Least Squares | F-statistic: | 1.860 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.187 |
| Time: | 04:47:13 | Log-Likelihood: | -112.13 |
| No. Observations: | 23 | AIC: | 228.3 |
| Df Residuals: | 21 | BIC: | 230.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 9.5565 | 51.901 | 0.184 | 0.856 | -98.377 117.490 |
| expression | 16.6371 | 12.197 | 1.364 | 0.187 | -8.729 42.003 |
| Omnibus: | 0.389 | Durbin-Watson: | 2.589 |
| Prob(Omnibus): | 0.823 | Jarque-Bera (JB): | 0.489 |
| Skew: | 0.256 | Prob(JB): | 0.783 |
| Kurtosis: | 2.503 | Cond. No. | 33.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.905 | 0.360 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.495 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 3.596 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0497 |
| Time: | 04:47:13 | Log-Likelihood: | -70.175 |
| No. Observations: | 15 | AIC: | 148.3 |
| Df Residuals: | 11 | BIC: | 151.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -33.0921 | 107.504 | -0.308 | 0.764 | -269.706 203.522 |
| C(dose)[T.1] | 112.0967 | 151.651 | 0.739 | 0.475 | -221.686 445.879 |
| expression | 20.2668 | 21.551 | 0.940 | 0.367 | -27.166 67.699 |
| expression:C(dose)[T.1] | -12.5426 | 30.690 | -0.409 | 0.691 | -80.091 55.006 |
| Omnibus: | 2.325 | Durbin-Watson: | 0.864 |
| Prob(Omnibus): | 0.313 | Jarque-Bera (JB): | 1.364 |
| Skew: | -0.735 | Prob(JB): | 0.506 |
| Kurtosis: | 2.842 | Cond. No. | 132. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.487 |
| Model: | OLS | Adj. R-squared: | 0.402 |
| Method: | Least Squares | F-statistic: | 5.706 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0181 |
| Time: | 04:47:13 | Log-Likelihood: | -70.288 |
| No. Observations: | 15 | AIC: | 146.6 |
| Df Residuals: | 12 | BIC: | 148.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -2.4174 | 74.245 | -0.033 | 0.975 | -164.184 159.349 |
| C(dose)[T.1] | 50.4561 | 15.235 | 3.312 | 0.006 | 17.261 83.651 |
| expression | 14.0822 | 14.801 | 0.951 | 0.360 | -18.167 46.332 |
| Omnibus: | 2.874 | Durbin-Watson: | 0.814 |
| Prob(Omnibus): | 0.238 | Jarque-Bera (JB): | 1.545 |
| Skew: | -0.786 | Prob(JB): | 0.462 |
| Kurtosis: | 3.032 | Cond. No. | 50.7 |
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: | 04:47:13 | 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: | 0.019 |
| Model: | OLS | Adj. R-squared: | -0.057 |
| Method: | Least Squares | F-statistic: | 0.2512 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.625 |
| Time: | 04:47:13 | Log-Likelihood: | -75.157 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 45.4175 | 96.801 | 0.469 | 0.647 | -163.708 254.543 |
| expression | 9.8224 | 19.600 | 0.501 | 0.625 | -32.520 52.165 |
| Omnibus: | 1.314 | Durbin-Watson: | 1.618 |
| Prob(Omnibus): | 0.519 | Jarque-Bera (JB): | 0.822 |
| Skew: | 0.147 | Prob(JB): | 0.663 |
| Kurtosis: | 1.891 | Cond. No. | 49.4 |