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 |
| 0.691 | 0.416 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.664 |
| Model: | OLS | Adj. R-squared: | 0.611 |
| Method: | Least Squares | F-statistic: | 12.53 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.48e-05 |
| Time: | 04:13:28 | Log-Likelihood: | -100.56 |
| No. Observations: | 23 | AIC: | 209.1 |
| Df Residuals: | 19 | BIC: | 213.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 26.6167 | 111.994 | 0.238 | 0.815 | -207.790 261.023 |
| C(dose)[T.1] | -15.6728 | 155.923 | -0.101 | 0.921 | -342.022 310.677 |
| expression | 4.3242 | 17.526 | 0.247 | 0.808 | -32.358 41.006 |
| expression:C(dose)[T.1] | 10.6890 | 24.299 | 0.440 | 0.665 | -40.169 61.547 |
| Omnibus: | 0.573 | Durbin-Watson: | 2.034 |
| Prob(Omnibus): | 0.751 | Jarque-Bera (JB): | 0.628 |
| Skew: | 0.313 | Prob(JB): | 0.730 |
| Kurtosis: | 2.486 | Cond. No. | 305. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.627 |
| Method: | Least Squares | F-statistic: | 19.48 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.02e-05 |
| Time: | 04:13:28 | Log-Likelihood: | -100.67 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 20 | BIC: | 210.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -8.8639 | 76.116 | -0.116 | 0.908 | -167.640 149.912 |
| C(dose)[T.1] | 52.8068 | 8.646 | 6.108 | 0.000 | 34.772 70.842 |
| expression | 9.8847 | 11.892 | 0.831 | 0.416 | -14.922 34.692 |
| Omnibus: | 0.482 | Durbin-Watson: | 1.949 |
| Prob(Omnibus): | 0.786 | Jarque-Bera (JB): | 0.572 |
| Skew: | 0.280 | Prob(JB): | 0.751 |
| Kurtosis: | 2.468 | Cond. No. | 116. |
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: | 04:13:28 | 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: | 0.028 |
| Model: | OLS | Adj. R-squared: | -0.018 |
| Method: | Least Squares | F-statistic: | 0.6055 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.445 |
| Time: | 04:13:28 | Log-Likelihood: | -112.78 |
| No. Observations: | 23 | AIC: | 229.6 |
| Df Residuals: | 21 | BIC: | 231.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -17.9492 | 125.714 | -0.143 | 0.888 | -279.385 243.487 |
| expression | 15.2450 | 19.592 | 0.778 | 0.445 | -25.498 55.988 |
| Omnibus: | 2.373 | Durbin-Watson: | 2.673 |
| Prob(Omnibus): | 0.305 | Jarque-Bera (JB): | 1.393 |
| Skew: | 0.310 | Prob(JB): | 0.498 |
| Kurtosis: | 1.966 | Cond. No. | 116. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 5.248 | 0.041 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.617 |
| Model: | OLS | Adj. R-squared: | 0.512 |
| Method: | Least Squares | F-statistic: | 5.899 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0119 |
| Time: | 04:13:28 | Log-Likelihood: | -68.108 |
| No. Observations: | 15 | AIC: | 144.2 |
| Df Residuals: | 11 | BIC: | 147.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -232.0861 | 230.838 | -1.005 | 0.336 | -740.157 275.985 |
| C(dose)[T.1] | 75.8699 | 277.747 | 0.273 | 0.790 | -535.447 687.186 |
| expression | 42.3652 | 32.620 | 1.299 | 0.221 | -29.432 114.162 |
| expression:C(dose)[T.1] | -2.9009 | 39.514 | -0.073 | 0.943 | -89.871 84.069 |
| Omnibus: | 0.001 | Durbin-Watson: | 1.353 |
| Prob(Omnibus): | 1.000 | Jarque-Bera (JB): | 0.187 |
| Skew: | 0.008 | Prob(JB): | 0.911 |
| Kurtosis: | 2.454 | Cond. No. | 418. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.616 |
| Model: | OLS | Adj. R-squared: | 0.553 |
| Method: | Least Squares | F-statistic: | 9.645 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00318 |
| Time: | 04:13:28 | Log-Likelihood: | -68.112 |
| No. Observations: | 15 | AIC: | 142.2 |
| Df Residuals: | 12 | BIC: | 144.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -218.1090 | 125.009 | -1.745 | 0.107 | -490.480 54.262 |
| C(dose)[T.1] | 55.5051 | 13.414 | 4.138 | 0.001 | 26.278 84.732 |
| expression | 40.3882 | 17.630 | 2.291 | 0.041 | 1.976 78.801 |
| Omnibus: | 0.002 | Durbin-Watson: | 1.339 |
| Prob(Omnibus): | 0.999 | Jarque-Bera (JB): | 0.159 |
| Skew: | -0.000 | Prob(JB): | 0.924 |
| Kurtosis: | 2.495 | Cond. No. | 137. |
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: | 04:13:28 | 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: | 0.069 |
| Model: | OLS | Adj. R-squared: | -0.002 |
| Method: | Least Squares | F-statistic: | 0.9683 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.343 |
| Time: | 04:13:28 | Log-Likelihood: | -74.761 |
| No. Observations: | 15 | AIC: | 153.5 |
| Df Residuals: | 13 | BIC: | 154.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -83.8767 | 180.691 | -0.464 | 0.650 | -474.235 306.482 |
| expression | 25.4123 | 25.825 | 0.984 | 0.343 | -30.379 81.203 |
| Omnibus: | 0.609 | Durbin-Watson: | 1.615 |
| Prob(Omnibus): | 0.738 | Jarque-Bera (JB): | 0.643 |
| Skew: | 0.288 | Prob(JB): | 0.725 |
| Kurtosis: | 2.165 | Cond. No. | 132. |