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 |
| 0.003 | 0.953 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.665 |
| Model: | OLS | Adj. R-squared: | 0.612 |
| Method: | Least Squares | F-statistic: | 12.57 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.28e-05 |
| Time: | 04:04:44 | Log-Likelihood: | -100.53 |
| No. Observations: | 23 | AIC: | 209.1 |
| Df Residuals: | 19 | BIC: | 213.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 106.8627 | 93.365 | 1.145 | 0.267 | -88.552 302.278 |
| C(dose)[T.1] | -88.7266 | 149.881 | -0.592 | 0.561 | -402.430 224.977 |
| expression | -7.8949 | 13.969 | -0.565 | 0.579 | -37.133 21.343 |
| expression:C(dose)[T.1] | 20.5811 | 21.705 | 0.948 | 0.355 | -24.847 66.009 |
| Omnibus: | 0.039 | Durbin-Watson: | 2.030 |
| Prob(Omnibus): | 0.981 | Jarque-Bera (JB): | 0.246 |
| Skew: | -0.048 | Prob(JB): | 0.884 |
| Kurtosis: | 2.502 | Cond. No. | 299. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.50 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.83e-05 |
| Time: | 04:04:44 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 50.0042 | 71.384 | 0.700 | 0.492 | -98.900 198.908 |
| C(dose)[T.1] | 53.0986 | 9.653 | 5.501 | 0.000 | 32.963 73.234 |
| expression | 0.6304 | 10.664 | 0.059 | 0.953 | -21.615 22.876 |
| Omnibus: | 0.304 | Durbin-Watson: | 1.898 |
| Prob(Omnibus): | 0.859 | Jarque-Bera (JB): | 0.475 |
| Skew: | 0.062 | Prob(JB): | 0.789 |
| Kurtosis: | 2.307 | Cond. No. | 115. |
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: | 04:04:44 | 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: | 0.118 |
| Model: | OLS | Adj. R-squared: | 0.076 |
| Method: | Least Squares | F-statistic: | 2.816 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.108 |
| Time: | 04:04:44 | Log-Likelihood: | -111.66 |
| No. Observations: | 23 | AIC: | 227.3 |
| Df Residuals: | 21 | BIC: | 229.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -92.5869 | 102.894 | -0.900 | 0.378 | -306.566 121.392 |
| expression | 25.1525 | 14.988 | 1.678 | 0.108 | -6.016 56.321 |
| Omnibus: | 1.944 | Durbin-Watson: | 2.566 |
| Prob(Omnibus): | 0.378 | Jarque-Bera (JB): | 1.056 |
| Skew: | 0.050 | Prob(JB): | 0.590 |
| Kurtosis: | 1.955 | Cond. No. | 106. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.313 | 0.094 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.569 |
| Model: | OLS | Adj. R-squared: | 0.451 |
| Method: | Least Squares | F-statistic: | 4.841 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0219 |
| Time: | 04:04:44 | Log-Likelihood: | -68.988 |
| No. Observations: | 15 | AIC: | 146.0 |
| Df Residuals: | 11 | BIC: | 148.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -144.9769 | 134.989 | -1.074 | 0.306 | -442.085 152.132 |
| C(dose)[T.1] | 93.7744 | 259.101 | 0.362 | 0.724 | -476.503 664.052 |
| expression | 35.6034 | 22.557 | 1.578 | 0.143 | -14.044 85.250 |
| expression:C(dose)[T.1] | -6.8776 | 44.032 | -0.156 | 0.879 | -103.792 90.037 |
| Omnibus: | 3.360 | Durbin-Watson: | 1.520 |
| Prob(Omnibus): | 0.186 | Jarque-Bera (JB): | 2.047 |
| Skew: | -0.904 | Prob(JB): | 0.359 |
| Kurtosis: | 2.903 | Cond. No. | 262. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.568 |
| Model: | OLS | Adj. R-squared: | 0.496 |
| Method: | Least Squares | F-statistic: | 7.890 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00650 |
| Time: | 04:04:44 | Log-Likelihood: | -69.004 |
| No. Observations: | 15 | AIC: | 144.0 |
| Df Residuals: | 12 | BIC: | 146.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -134.2093 | 111.241 | -1.206 | 0.251 | -376.583 108.164 |
| C(dose)[T.1] | 53.3698 | 14.121 | 3.780 | 0.003 | 22.604 84.136 |
| expression | 33.7985 | 18.568 | 1.820 | 0.094 | -6.658 74.255 |
| Omnibus: | 3.555 | Durbin-Watson: | 1.467 |
| Prob(Omnibus): | 0.169 | Jarque-Bera (JB): | 2.183 |
| Skew: | -0.934 | Prob(JB): | 0.336 |
| Kurtosis: | 2.912 | Cond. No. | 97.7 |
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: | 04:04:44 | 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: | 0.054 |
| Model: | OLS | Adj. R-squared: | -0.019 |
| Method: | Least Squares | F-statistic: | 0.7395 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.405 |
| Time: | 04:04:44 | Log-Likelihood: | -74.885 |
| No. Observations: | 15 | AIC: | 153.8 |
| Df Residuals: | 13 | BIC: | 155.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -38.5142 | 154.027 | -0.250 | 0.806 | -371.270 294.242 |
| expression | 22.4035 | 26.052 | 0.860 | 0.405 | -33.879 78.686 |
| Omnibus: | 3.379 | Durbin-Watson: | 1.867 |
| Prob(Omnibus): | 0.185 | Jarque-Bera (JB): | 1.350 |
| Skew: | 0.301 | Prob(JB): | 0.509 |
| Kurtosis: | 1.659 | Cond. No. | 94.8 |