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 |
| 3.445 | 0.078 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.701 |
| Model: | OLS | Adj. R-squared: | 0.653 |
| Method: | Least Squares | F-statistic: | 14.83 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.26e-05 |
| Time: | 04:00:53 | Log-Likelihood: | -99.233 |
| No. Observations: | 23 | AIC: | 206.5 |
| Df Residuals: | 19 | BIC: | 211.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 0.6627 | 48.663 | 0.014 | 0.989 | -101.191 102.516 |
| C(dose)[T.1] | 60.0732 | 58.938 | 1.019 | 0.321 | -63.285 183.431 |
| expression | 9.9773 | 9.004 | 1.108 | 0.282 | -8.869 28.823 |
| expression:C(dose)[T.1] | -0.6417 | 11.118 | -0.058 | 0.955 | -23.912 22.629 |
| Omnibus: | 1.307 | Durbin-Watson: | 1.860 |
| Prob(Omnibus): | 0.520 | Jarque-Bera (JB): | 1.185 |
| Skew: | 0.482 | Prob(JB): | 0.553 |
| Kurtosis: | 2.445 | Cond. No. | 107. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.701 |
| Model: | OLS | Adj. R-squared: | 0.671 |
| Method: | Least Squares | F-statistic: | 23.40 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.78e-06 |
| Time: | 04:00:53 | Log-Likelihood: | -99.235 |
| No. Observations: | 23 | AIC: | 204.5 |
| Df Residuals: | 20 | BIC: | 207.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 2.9216 | 28.194 | 0.104 | 0.918 | -55.891 61.734 |
| C(dose)[T.1] | 56.7069 | 8.301 | 6.831 | 0.000 | 39.391 74.022 |
| expression | 9.5564 | 5.149 | 1.856 | 0.078 | -1.184 20.297 |
| Omnibus: | 1.354 | Durbin-Watson: | 1.872 |
| Prob(Omnibus): | 0.508 | Jarque-Bera (JB): | 1.214 |
| Skew: | 0.494 | Prob(JB): | 0.545 |
| Kurtosis: | 2.461 | Cond. No. | 38.2 |
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: | 04:00:53 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.045 |
| Method: | Least Squares | F-statistic: | 0.04332 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.837 |
| Time: | 04:00:53 | Log-Likelihood: | -113.08 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 70.0319 | 47.087 | 1.487 | 0.152 | -27.891 167.955 |
| expression | 1.8633 | 8.952 | 0.208 | 0.837 | -16.753 20.479 |
| Omnibus: | 3.286 | Durbin-Watson: | 2.488 |
| Prob(Omnibus): | 0.193 | Jarque-Bera (JB): | 1.575 |
| Skew: | 0.295 | Prob(JB): | 0.455 |
| Kurtosis: | 1.862 | Cond. No. | 35.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.482 | 0.247 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.544 |
| Model: | OLS | Adj. R-squared: | 0.420 |
| Method: | Least Squares | F-statistic: | 4.373 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0294 |
| Time: | 04:00:53 | Log-Likelihood: | -69.412 |
| No. Observations: | 15 | AIC: | 146.8 |
| Df Residuals: | 11 | BIC: | 149.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 42.2789 | 77.268 | 0.547 | 0.595 | -127.786 212.344 |
| C(dose)[T.1] | -70.2187 | 125.016 | -0.562 | 0.586 | -345.378 204.940 |
| expression | 4.5919 | 13.966 | 0.329 | 0.748 | -26.147 35.331 |
| expression:C(dose)[T.1] | 19.6886 | 21.554 | 0.913 | 0.381 | -27.751 67.129 |
| Omnibus: | 0.073 | Durbin-Watson: | 1.241 |
| Prob(Omnibus): | 0.964 | Jarque-Bera (JB): | 0.073 |
| Skew: | -0.002 | Prob(JB): | 0.964 |
| Kurtosis: | 2.657 | Cond. No. | 128. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.509 |
| Model: | OLS | Adj. R-squared: | 0.428 |
| Method: | Least Squares | F-statistic: | 6.229 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0140 |
| Time: | 04:00:53 | Log-Likelihood: | -69.960 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 12 | BIC: | 148.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -2.9955 | 58.866 | -0.051 | 0.960 | -131.254 125.263 |
| C(dose)[T.1] | 43.0635 | 15.681 | 2.746 | 0.018 | 8.897 77.230 |
| expression | 12.8582 | 10.564 | 1.217 | 0.247 | -10.159 35.875 |
| Omnibus: | 0.935 | Durbin-Watson: | 0.996 |
| Prob(Omnibus): | 0.626 | Jarque-Bera (JB): | 0.785 |
| Skew: | -0.290 | Prob(JB): | 0.675 |
| Kurtosis: | 2.041 | Cond. No. | 47.6 |
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: | 04:00:53 | 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: | 0.201 |
| Model: | OLS | Adj. R-squared: | 0.140 |
| Method: | Least Squares | F-statistic: | 3.270 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0937 |
| Time: | 04:00:53 | Log-Likelihood: | -73.617 |
| No. Observations: | 15 | AIC: | 151.2 |
| Df Residuals: | 13 | BIC: | 152.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -33.4544 | 70.880 | -0.472 | 0.645 | -186.582 119.673 |
| expression | 22.1799 | 12.265 | 1.808 | 0.094 | -4.317 48.677 |
| Omnibus: | 0.173 | Durbin-Watson: | 1.634 |
| Prob(Omnibus): | 0.917 | Jarque-Bera (JB): | 0.320 |
| Skew: | 0.199 | Prob(JB): | 0.852 |
| Kurtosis: | 2.405 | Cond. No. | 46.4 |