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 |
| 3.243 | 0.087 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.710 |
| Model: | OLS | Adj. R-squared: | 0.665 |
| Method: | Least Squares | F-statistic: | 15.54 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.39e-05 |
| Time: | 04:24:24 | Log-Likelihood: | -98.853 |
| No. Observations: | 23 | AIC: | 205.7 |
| Df Residuals: | 19 | BIC: | 210.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 79.8969 | 67.206 | 1.189 | 0.249 | -60.767 220.561 |
| C(dose)[T.1] | 136.5106 | 87.222 | 1.565 | 0.134 | -46.048 319.069 |
| expression | -5.4684 | 14.256 | -0.384 | 0.706 | -35.306 24.369 |
| expression:C(dose)[T.1] | -16.2446 | 18.022 | -0.901 | 0.379 | -53.966 21.477 |
| Omnibus: | 0.360 | Durbin-Watson: | 1.759 |
| Prob(Omnibus): | 0.835 | Jarque-Bera (JB): | 0.005 |
| Skew: | 0.036 | Prob(JB): | 0.997 |
| Kurtosis: | 3.018 | Cond. No. | 149. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.698 |
| Model: | OLS | Adj. R-squared: | 0.668 |
| Method: | Least Squares | F-statistic: | 23.11 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.31e-06 |
| Time: | 04:24:24 | Log-Likelihood: | -99.335 |
| No. Observations: | 23 | AIC: | 204.7 |
| Df Residuals: | 20 | BIC: | 208.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 127.6434 | 41.166 | 3.101 | 0.006 | 41.773 213.514 |
| C(dose)[T.1] | 58.2775 | 8.585 | 6.788 | 0.000 | 40.369 76.186 |
| expression | -15.6323 | 8.681 | -1.801 | 0.087 | -33.740 2.476 |
| Omnibus: | 0.477 | Durbin-Watson: | 1.653 |
| Prob(Omnibus): | 0.788 | Jarque-Bera (JB): | 0.011 |
| Skew: | 0.023 | Prob(JB): | 0.995 |
| Kurtosis: | 3.096 | Cond. No. | 51.8 |
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: | 04:24:24 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.045 |
| Method: | Least Squares | F-statistic: | 0.04804 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.829 |
| Time: | 04:24:24 | Log-Likelihood: | -113.08 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 64.2103 | 71.116 | 0.903 | 0.377 | -83.684 212.105 |
| expression | 3.1981 | 14.591 | 0.219 | 0.829 | -27.146 33.542 |
| Omnibus: | 2.823 | Durbin-Watson: | 2.508 |
| Prob(Omnibus): | 0.244 | Jarque-Bera (JB): | 1.530 |
| Skew: | 0.327 | Prob(JB): | 0.465 |
| Kurtosis: | 1.919 | Cond. No. | 50.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.264 | 0.617 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.464 |
| Model: | OLS | Adj. R-squared: | 0.318 |
| Method: | Least Squares | F-statistic: | 3.173 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0675 |
| Time: | 04:24:24 | Log-Likelihood: | -70.624 |
| No. Observations: | 15 | AIC: | 149.2 |
| Df Residuals: | 11 | BIC: | 152.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 88.3255 | 75.882 | 1.164 | 0.269 | -78.689 255.340 |
| C(dose)[T.1] | 81.8964 | 134.853 | 0.607 | 0.556 | -214.912 378.705 |
| expression | -3.7466 | 13.438 | -0.279 | 0.786 | -33.324 25.831 |
| expression:C(dose)[T.1] | -6.5776 | 25.242 | -0.261 | 0.799 | -62.135 48.980 |
| Omnibus: | 3.867 | Durbin-Watson: | 0.873 |
| Prob(Omnibus): | 0.145 | Jarque-Bera (JB): | 2.328 |
| Skew: | -0.965 | Prob(JB): | 0.312 |
| Kurtosis: | 2.991 | Cond. No. | 113. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.461 |
| Model: | OLS | Adj. R-squared: | 0.371 |
| Method: | Least Squares | F-statistic: | 5.124 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0246 |
| Time: | 04:24:24 | Log-Likelihood: | -70.670 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 12 | BIC: | 149.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 98.7237 | 61.986 | 1.593 | 0.137 | -36.331 233.778 |
| C(dose)[T.1] | 47.0296 | 16.131 | 2.915 | 0.013 | 11.883 82.176 |
| expression | -5.6109 | 10.925 | -0.514 | 0.617 | -29.414 18.192 |
| Omnibus: | 3.379 | Durbin-Watson: | 0.799 |
| Prob(Omnibus): | 0.185 | Jarque-Bera (JB): | 2.160 |
| Skew: | -0.925 | Prob(JB): | 0.340 |
| Kurtosis: | 2.816 | Cond. No. | 45.1 |
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: | 04:24:24 | 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: | 0.079 |
| Model: | OLS | Adj. R-squared: | 0.008 |
| Method: | Least Squares | F-statistic: | 1.109 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.312 |
| Time: | 04:24:24 | Log-Likelihood: | -74.686 |
| No. Observations: | 15 | AIC: | 153.4 |
| Df Residuals: | 13 | BIC: | 154.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 168.5537 | 71.793 | 2.348 | 0.035 | 13.454 323.653 |
| expression | -13.9414 | 13.241 | -1.053 | 0.312 | -42.548 14.665 |
| Omnibus: | 0.500 | Durbin-Watson: | 1.864 |
| Prob(Omnibus): | 0.779 | Jarque-Bera (JB): | 0.549 |
| Skew: | -0.107 | Prob(JB): | 0.760 |
| Kurtosis: | 2.088 | Cond. No. | 41.2 |