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 |
| 0.628 | 0.437 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.709 |
| Model: | OLS | Adj. R-squared: | 0.663 |
| Method: | Least Squares | F-statistic: | 15.42 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.52e-05 |
| Time: | 04:53:32 | Log-Likelihood: | -98.917 |
| No. Observations: | 23 | AIC: | 205.8 |
| Df Residuals: | 19 | BIC: | 210.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 63.2267 | 40.400 | 1.565 | 0.134 | -21.332 147.785 |
| C(dose)[T.1] | -96.3159 | 82.517 | -1.167 | 0.258 | -269.026 76.394 |
| expression | -1.6582 | 7.355 | -0.225 | 0.824 | -17.052 13.736 |
| expression:C(dose)[T.1] | 25.1290 | 14.047 | 1.789 | 0.090 | -4.271 54.529 |
| Omnibus: | 0.031 | Durbin-Watson: | 1.691 |
| Prob(Omnibus): | 0.984 | Jarque-Bera (JB): | 0.222 |
| Skew: | -0.059 | Prob(JB): | 0.895 |
| Kurtosis: | 2.533 | Cond. No. | 142. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.660 |
| Model: | OLS | Adj. R-squared: | 0.626 |
| Method: | Least Squares | F-statistic: | 19.39 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.08e-05 |
| Time: | 04:53:32 | Log-Likelihood: | -100.71 |
| No. Observations: | 23 | AIC: | 207.4 |
| Df Residuals: | 20 | BIC: | 210.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 25.7578 | 36.398 | 0.708 | 0.487 | -50.167 101.683 |
| C(dose)[T.1] | 50.4428 | 9.376 | 5.380 | 0.000 | 30.885 70.001 |
| expression | 5.2312 | 6.602 | 0.792 | 0.437 | -8.540 19.002 |
| Omnibus: | 0.076 | Durbin-Watson: | 1.868 |
| Prob(Omnibus): | 0.963 | Jarque-Bera (JB): | 0.284 |
| Skew: | 0.077 | Prob(JB): | 0.867 |
| Kurtosis: | 2.478 | Cond. No. | 50.3 |
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: | 04:53:32 | 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: | 0.167 |
| Model: | OLS | Adj. R-squared: | 0.128 |
| Method: | Least Squares | F-statistic: | 4.220 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0526 |
| Time: | 04:53:32 | Log-Likelihood: | -111.00 |
| No. Observations: | 23 | AIC: | 226.0 |
| Df Residuals: | 21 | BIC: | 228.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -29.0306 | 53.348 | -0.544 | 0.592 | -139.973 81.912 |
| expression | 19.0678 | 9.282 | 2.054 | 0.053 | -0.236 38.372 |
| Omnibus: | 3.243 | Durbin-Watson: | 2.235 |
| Prob(Omnibus): | 0.198 | Jarque-Bera (JB): | 1.371 |
| Skew: | -0.127 | Prob(JB): | 0.504 |
| Kurtosis: | 1.831 | Cond. No. | 47.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.403 | 0.537 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.468 |
| Model: | OLS | Adj. R-squared: | 0.323 |
| Method: | Least Squares | F-statistic: | 3.231 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0647 |
| Time: | 04:53:33 | Log-Likelihood: | -70.561 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 152.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 27.6821 | 68.046 | 0.407 | 0.692 | -122.086 177.450 |
| C(dose)[T.1] | 68.2821 | 112.140 | 0.609 | 0.555 | -178.536 315.101 |
| expression | 8.1879 | 13.806 | 0.593 | 0.565 | -22.198 38.574 |
| expression:C(dose)[T.1] | -4.1570 | 22.099 | -0.188 | 0.854 | -52.797 44.483 |
| Omnibus: | 2.097 | Durbin-Watson: | 0.805 |
| Prob(Omnibus): | 0.351 | Jarque-Bera (JB): | 1.413 |
| Skew: | -0.729 | Prob(JB): | 0.493 |
| Kurtosis: | 2.635 | Cond. No. | 92.7 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.467 |
| Model: | OLS | Adj. R-squared: | 0.378 |
| Method: | Least Squares | F-statistic: | 5.251 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0230 |
| Time: | 04:53:33 | Log-Likelihood: | -70.585 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 35.5572 | 51.441 | 0.691 | 0.503 | -76.524 147.638 |
| C(dose)[T.1] | 47.4152 | 15.734 | 3.014 | 0.011 | 13.135 81.696 |
| expression | 6.5656 | 10.338 | 0.635 | 0.537 | -15.959 29.090 |
| Omnibus: | 2.235 | Durbin-Watson: | 0.848 |
| Prob(Omnibus): | 0.327 | Jarque-Bera (JB): | 1.419 |
| Skew: | -0.741 | Prob(JB): | 0.492 |
| Kurtosis: | 2.729 | Cond. No. | 35.2 |
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: | 04:53:33 | 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: | 0.063 |
| Model: | OLS | Adj. R-squared: | -0.009 |
| Method: | Least Squares | F-statistic: | 0.8753 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.367 |
| Time: | 04:53:33 | Log-Likelihood: | -74.811 |
| No. Observations: | 15 | AIC: | 153.6 |
| Df Residuals: | 13 | BIC: | 155.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 33.0825 | 65.500 | 0.505 | 0.622 | -108.421 174.586 |
| expression | 12.1192 | 12.954 | 0.936 | 0.367 | -15.866 40.105 |
| Omnibus: | 1.359 | Durbin-Watson: | 1.794 |
| Prob(Omnibus): | 0.507 | Jarque-Bera (JB): | 0.851 |
| Skew: | 0.184 | Prob(JB): | 0.653 |
| Kurtosis: | 1.892 | Cond. No. | 35.0 |