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 |
| 3.655 | 0.070 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.712 |
| Model: | OLS | Adj. R-squared: | 0.666 |
| Method: | Least Squares | F-statistic: | 15.65 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.28e-05 |
| Time: | 03:55:57 | Log-Likelihood: | -98.792 |
| No. Observations: | 23 | AIC: | 205.6 |
| Df Residuals: | 19 | BIC: | 210.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 13.4690 | 41.714 | 0.323 | 0.750 | -73.838 100.776 |
| C(dose)[T.1] | 10.2867 | 63.131 | 0.163 | 0.872 | -121.848 142.422 |
| expression | 8.4279 | 8.550 | 0.986 | 0.337 | -9.468 26.324 |
| expression:C(dose)[T.1] | 10.2041 | 13.506 | 0.755 | 0.459 | -18.065 38.473 |
| Omnibus: | 0.506 | Durbin-Watson: | 1.688 |
| Prob(Omnibus): | 0.777 | Jarque-Bera (JB): | 0.595 |
| Skew: | 0.284 | Prob(JB): | 0.743 |
| Kurtosis: | 2.454 | Cond. No. | 94.8 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.703 |
| Model: | OLS | Adj. R-squared: | 0.674 |
| Method: | Least Squares | F-statistic: | 23.70 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.29e-06 |
| Time: | 03:55:57 | Log-Likelihood: | -99.133 |
| No. Observations: | 23 | AIC: | 204.3 |
| Df Residuals: | 20 | BIC: | 207.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -6.2980 | 32.137 | -0.196 | 0.847 | -73.335 60.739 |
| C(dose)[T.1] | 57.5526 | 8.360 | 6.884 | 0.000 | 40.114 74.991 |
| expression | 12.5171 | 6.547 | 1.912 | 0.070 | -1.141 26.175 |
| Omnibus: | 0.291 | Durbin-Watson: | 1.537 |
| Prob(Omnibus): | 0.865 | Jarque-Bera (JB): | 0.468 |
| Skew: | 0.096 | Prob(JB): | 0.791 |
| Kurtosis: | 2.328 | Cond. No. | 39.6 |
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: | 03:55:57 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.003085 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.956 |
| Time: | 03:55:57 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 76.7811 | 53.358 | 1.439 | 0.165 | -34.183 187.745 |
| expression | 0.6284 | 11.314 | 0.056 | 0.956 | -22.900 24.157 |
| Omnibus: | 3.262 | Durbin-Watson: | 2.482 |
| Prob(Omnibus): | 0.196 | Jarque-Bera (JB): | 1.560 |
| Skew: | 0.288 | Prob(JB): | 0.458 |
| Kurtosis: | 1.862 | Cond. No. | 36.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.566 | 0.466 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.474 |
| Model: | OLS | Adj. R-squared: | 0.330 |
| Method: | Least Squares | F-statistic: | 3.300 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0615 |
| Time: | 03:55:57 | Log-Likelihood: | -70.486 |
| No. Observations: | 15 | AIC: | 149.0 |
| Df Residuals: | 11 | BIC: | 151.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -14.1401 | 138.953 | -0.102 | 0.921 | -319.973 291.693 |
| C(dose)[T.1] | 50.1049 | 238.607 | 0.210 | 0.838 | -475.066 575.276 |
| expression | 17.3822 | 29.505 | 0.589 | 0.568 | -47.558 82.322 |
| expression:C(dose)[T.1] | -2.0507 | 47.176 | -0.043 | 0.966 | -105.884 101.783 |
| Omnibus: | 1.049 | Durbin-Watson: | 1.003 |
| Prob(Omnibus): | 0.592 | Jarque-Bera (JB): | 0.912 |
| Skew: | -0.498 | Prob(JB): | 0.634 |
| Kurtosis: | 2.315 | Cond. No. | 198. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.474 |
| Model: | OLS | Adj. R-squared: | 0.386 |
| Method: | Least Squares | F-statistic: | 5.398 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0213 |
| Time: | 03:55:57 | Log-Likelihood: | -70.488 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 12 | BIC: | 149.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -10.3760 | 104.053 | -0.100 | 0.922 | -237.088 216.336 |
| C(dose)[T.1] | 39.7722 | 19.839 | 2.005 | 0.068 | -3.453 82.998 |
| expression | 16.5801 | 22.044 | 0.752 | 0.466 | -31.450 64.610 |
| Omnibus: | 0.984 | Durbin-Watson: | 1.006 |
| Prob(Omnibus): | 0.612 | Jarque-Bera (JB): | 0.870 |
| Skew: | -0.480 | Prob(JB): | 0.647 |
| Kurtosis: | 2.315 | Cond. No. | 71.7 |
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: | 03:55:57 | 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: | 0.297 |
| Model: | OLS | Adj. R-squared: | 0.243 |
| Method: | Least Squares | F-statistic: | 5.500 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0355 |
| Time: | 03:55:57 | Log-Likelihood: | -72.654 |
| No. Observations: | 15 | AIC: | 149.3 |
| Df Residuals: | 13 | BIC: | 150.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -128.6047 | 95.162 | -1.351 | 0.200 | -334.189 76.980 |
| expression | 44.4917 | 18.972 | 2.345 | 0.036 | 3.505 85.478 |
| Omnibus: | 1.086 | Durbin-Watson: | 1.490 |
| Prob(Omnibus): | 0.581 | Jarque-Bera (JB): | 0.800 |
| Skew: | 0.519 | Prob(JB): | 0.670 |
| Kurtosis: | 2.551 | Cond. No. | 58.2 |