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 |
| 0.369 | 0.551 | 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: | 12.59 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.21e-05 |
| Time: | 06:23:50 | Log-Likelihood: | -100.52 |
| No. Observations: | 23 | AIC: | 209.0 |
| Df Residuals: | 19 | BIC: | 213.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 10.4492 | 50.868 | 0.205 | 0.839 | -96.018 116.917 |
| C(dose)[T.1] | 137.1100 | 110.233 | 1.244 | 0.229 | -93.611 367.831 |
| expression | 17.3346 | 20.006 | 0.866 | 0.397 | -24.539 59.208 |
| expression:C(dose)[T.1] | -34.2321 | 45.810 | -0.747 | 0.464 | -130.114 61.650 |
| Omnibus: | 0.246 | Durbin-Watson: | 2.175 |
| Prob(Omnibus): | 0.884 | Jarque-Bera (JB): | 0.438 |
| Skew: | -0.081 | Prob(JB): | 0.803 |
| Kurtosis: | 2.344 | Cond. No. | 82.9 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.655 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 19.02 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.36e-05 |
| Time: | 06:23:50 | Log-Likelihood: | -100.85 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 26.9306 | 45.329 | 0.594 | 0.559 | -67.623 121.484 |
| C(dose)[T.1] | 55.0265 | 9.125 | 6.030 | 0.000 | 35.993 74.060 |
| expression | 10.8057 | 17.798 | 0.607 | 0.551 | -26.320 47.931 |
| Omnibus: | 0.226 | Durbin-Watson: | 1.968 |
| Prob(Omnibus): | 0.893 | Jarque-Bera (JB): | 0.424 |
| Skew: | -0.025 | Prob(JB): | 0.809 |
| Kurtosis: | 2.337 | Cond. No. | 30.3 |
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: | 06:23:50 | 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: | 0.029 |
| Model: | OLS | Adj. R-squared: | -0.017 |
| Method: | Least Squares | F-statistic: | 0.6232 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.439 |
| Time: | 06:23:50 | Log-Likelihood: | -112.77 |
| No. Observations: | 23 | AIC: | 229.5 |
| Df Residuals: | 21 | BIC: | 231.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 133.4198 | 68.396 | 1.951 | 0.065 | -8.818 275.658 |
| expression | -21.9229 | 27.770 | -0.789 | 0.439 | -79.674 35.828 |
| Omnibus: | 2.460 | Durbin-Watson: | 2.345 |
| Prob(Omnibus): | 0.292 | Jarque-Bera (JB): | 1.237 |
| Skew: | 0.162 | Prob(JB): | 0.539 |
| Kurtosis: | 1.911 | Cond. No. | 27.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.492 | 0.497 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.553 |
| Model: | OLS | Adj. R-squared: | 0.432 |
| Method: | Least Squares | F-statistic: | 4.545 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0264 |
| Time: | 06:23:50 | Log-Likelihood: | -69.253 |
| No. Observations: | 15 | AIC: | 146.5 |
| Df Residuals: | 11 | BIC: | 149.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 16.0480 | 42.848 | 0.375 | 0.715 | -78.261 110.357 |
| C(dose)[T.1] | 202.6187 | 109.152 | 1.856 | 0.090 | -37.624 442.862 |
| expression | 17.1732 | 13.859 | 1.239 | 0.241 | -13.329 47.676 |
| expression:C(dose)[T.1] | -49.8663 | 34.873 | -1.430 | 0.181 | -126.620 26.888 |
| Omnibus: | 1.671 | Durbin-Watson: | 1.052 |
| Prob(Omnibus): | 0.434 | Jarque-Bera (JB): | 1.326 |
| Skew: | -0.595 | Prob(JB): | 0.515 |
| Kurtosis: | 2.160 | Cond. No. | 59.9 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.470 |
| Model: | OLS | Adj. R-squared: | 0.382 |
| Method: | Least Squares | F-statistic: | 5.331 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0220 |
| Time: | 06:23:50 | Log-Likelihood: | -70.532 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 12 | BIC: | 149.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 39.6106 | 41.239 | 0.961 | 0.356 | -50.242 129.463 |
| C(dose)[T.1] | 47.9942 | 15.522 | 3.092 | 0.009 | 14.175 81.813 |
| expression | 9.2978 | 13.259 | 0.701 | 0.497 | -19.592 38.187 |
| Omnibus: | 3.870 | Durbin-Watson: | 0.953 |
| Prob(Omnibus): | 0.144 | Jarque-Bera (JB): | 2.209 |
| Skew: | -0.939 | Prob(JB): | 0.331 |
| Kurtosis: | 3.093 | Cond. No. | 18.6 |
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: | 06:23:50 | 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: | 0.049 |
| Model: | OLS | Adj. R-squared: | -0.025 |
| Method: | Least Squares | F-statistic: | 0.6637 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.430 |
| Time: | 06:23:50 | Log-Likelihood: | -74.927 |
| No. Observations: | 15 | AIC: | 153.9 |
| Df Residuals: | 13 | BIC: | 155.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 51.3458 | 52.884 | 0.971 | 0.349 | -62.903 165.595 |
| expression | 13.8264 | 16.971 | 0.815 | 0.430 | -22.838 50.491 |
| Omnibus: | 1.039 | Durbin-Watson: | 1.779 |
| Prob(Omnibus): | 0.595 | Jarque-Bera (JB): | 0.789 |
| Skew: | 0.236 | Prob(JB): | 0.674 |
| Kurtosis: | 1.980 | Cond. No. | 18.3 |