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 |
| 3.949 | 0.061 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.716 |
| Model: | OLS | Adj. R-squared: | 0.671 |
| Method: | Least Squares | F-statistic: | 15.98 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 1.98e-05 |
| Time: | 11:48:24 | Log-Likelihood: | -98.623 |
| No. Observations: | 23 | AIC: | 205.2 |
| Df Residuals: | 19 | BIC: | 209.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 9.4192 | 24.485 | 0.385 | 0.705 | -41.828 60.666 |
| C(dose)[T.1] | 76.4636 | 33.501 | 2.282 | 0.034 | 6.346 146.582 |
| expression | 11.0669 | 5.890 | 1.879 | 0.076 | -1.260 23.394 |
| expression:C(dose)[T.1] | -6.1037 | 7.768 | -0.786 | 0.442 | -22.362 10.155 |
| Omnibus: | 0.004 | Durbin-Watson: | 2.127 |
| Prob(Omnibus): | 0.998 | Jarque-Bera (JB): | 0.150 |
| Skew: | 0.027 | Prob(JB): | 0.928 |
| Kurtosis: | 2.608 | Cond. No. | 50.6 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.707 |
| Model: | OLS | Adj. R-squared: | 0.678 |
| Method: | Least Squares | F-statistic: | 24.12 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 4.67e-06 |
| Time: | 11:48:24 | Log-Likelihood: | -98.990 |
| No. Observations: | 23 | AIC: | 204.0 |
| Df Residuals: | 20 | BIC: | 207.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 23.6205 | 16.359 | 1.444 | 0.164 | -10.505 57.746 |
| C(dose)[T.1] | 50.9373 | 8.105 | 6.285 | 0.000 | 34.031 67.843 |
| expression | 7.5579 | 3.803 | 1.987 | 0.061 | -0.375 15.491 |
| Omnibus: | 0.583 | Durbin-Watson: | 2.155 |
| Prob(Omnibus): | 0.747 | Jarque-Bera (JB): | 0.389 |
| Skew: | 0.301 | Prob(JB): | 0.823 |
| Kurtosis: | 2.791 | Cond. No. | 18.7 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 11:48:24 | 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: | 0.128 |
| Model: | OLS | Adj. R-squared: | 0.087 |
| Method: | Least Squares | F-statistic: | 3.086 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0936 |
| Time: | 11:48:24 | Log-Likelihood: | -111.53 |
| No. Observations: | 23 | AIC: | 227.1 |
| Df Residuals: | 21 | BIC: | 229.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 33.0267 | 27.422 | 1.204 | 0.242 | -24.000 90.053 |
| expression | 11.1195 | 6.330 | 1.757 | 0.094 | -2.045 24.284 |
| Omnibus: | 1.668 | Durbin-Watson: | 2.464 |
| Prob(Omnibus): | 0.434 | Jarque-Bera (JB): | 1.371 |
| Skew: | 0.564 | Prob(JB): | 0.504 |
| Kurtosis: | 2.603 | Cond. No. | 18.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.179 | 0.680 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.494 |
| Model: | OLS | Adj. R-squared: | 0.356 |
| Method: | Least Squares | F-statistic: | 3.576 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0504 |
| Time: | 11:48:24 | Log-Likelihood: | -70.195 |
| No. Observations: | 15 | AIC: | 148.4 |
| Df Residuals: | 11 | BIC: | 151.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 130.2632 | 68.754 | 1.895 | 0.085 | -21.064 281.590 |
| C(dose)[T.1] | -34.1363 | 91.744 | -0.372 | 0.717 | -236.064 167.792 |
| expression | -9.0975 | 9.814 | -0.927 | 0.374 | -30.699 12.503 |
| expression:C(dose)[T.1] | 12.5122 | 13.982 | 0.895 | 0.390 | -18.262 43.287 |
| Omnibus: | 1.872 | Durbin-Watson: | 1.155 |
| Prob(Omnibus): | 0.392 | Jarque-Bera (JB): | 1.212 |
| Skew: | -0.678 | Prob(JB): | 0.546 |
| Kurtosis: | 2.682 | Cond. No. | 103. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.457 |
| Model: | OLS | Adj. R-squared: | 0.366 |
| Method: | Least Squares | F-statistic: | 5.047 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0257 |
| Time: | 11:48:24 | Log-Likelihood: | -70.722 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 87.6860 | 49.219 | 1.782 | 0.100 | -19.553 194.924 |
| C(dose)[T.1] | 46.5457 | 16.833 | 2.765 | 0.017 | 9.870 83.221 |
| expression | -2.9330 | 6.932 | -0.423 | 0.680 | -18.037 12.171 |
| Omnibus: | 2.638 | Durbin-Watson: | 0.798 |
| Prob(Omnibus): | 0.267 | Jarque-Bera (JB): | 1.827 |
| Skew: | -0.832 | Prob(JB): | 0.401 |
| Kurtosis: | 2.604 | Cond. No. | 43.0 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 11:48:24 | 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: | 0.111 |
| Model: | OLS | Adj. R-squared: | 0.042 |
| Method: | Least Squares | F-statistic: | 1.620 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.225 |
| Time: | 11:48:24 | Log-Likelihood: | -74.419 |
| No. Observations: | 15 | AIC: | 152.8 |
| Df Residuals: | 13 | BIC: | 154.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 158.3457 | 51.712 | 3.062 | 0.009 | 46.628 270.063 |
| expression | -10.0672 | 7.910 | -1.273 | 0.225 | -27.155 7.020 |
| Omnibus: | 3.289 | Durbin-Watson: | 1.277 |
| Prob(Omnibus): | 0.193 | Jarque-Bera (JB): | 1.256 |
| Skew: | 0.219 | Prob(JB): | 0.534 |
| Kurtosis: | 1.652 | Cond. No. | 36.1 |