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 |
| 0.589 | 0.452 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.700 |
| Model: | OLS | Adj. R-squared: | 0.653 |
| Method: | Least Squares | F-statistic: | 14.78 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.32e-05 |
| Time: | 05:01:43 | Log-Likelihood: | -99.256 |
| No. Observations: | 23 | AIC: | 206.5 |
| Df Residuals: | 19 | BIC: | 211.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 174.4338 | 115.284 | 1.513 | 0.147 | -66.859 415.727 |
| C(dose)[T.1] | -166.3472 | 133.962 | -1.242 | 0.229 | -446.733 114.039 |
| expression | -29.4326 | 28.188 | -1.044 | 0.310 | -88.430 29.565 |
| expression:C(dose)[T.1] | 51.5190 | 31.974 | 1.611 | 0.124 | -15.404 118.441 |
| Omnibus: | 2.995 | Durbin-Watson: | 1.731 |
| Prob(Omnibus): | 0.224 | Jarque-Bera (JB): | 1.281 |
| Skew: | -0.029 | Prob(JB): | 0.527 |
| Kurtosis: | 1.845 | Cond. No. | 216. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.625 |
| Method: | Least Squares | F-statistic: | 19.33 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.12e-05 |
| Time: | 05:01:43 | Log-Likelihood: | -100.73 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 10.8800 | 56.793 | 0.192 | 0.850 | -107.588 129.348 |
| C(dose)[T.1] | 48.8992 | 10.401 | 4.702 | 0.000 | 27.204 70.595 |
| expression | 10.6073 | 13.826 | 0.767 | 0.452 | -18.234 39.448 |
| Omnibus: | 0.312 | Durbin-Watson: | 1.863 |
| Prob(Omnibus): | 0.855 | Jarque-Bera (JB): | 0.468 |
| Skew: | 0.202 | Prob(JB): | 0.791 |
| Kurtosis: | 2.429 | Cond. No. | 60.4 |
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: | 05:01:43 | 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: | 0.282 |
| Model: | OLS | Adj. R-squared: | 0.248 |
| Method: | Least Squares | F-statistic: | 8.260 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00908 |
| Time: | 05:01:43 | Log-Likelihood: | -109.29 |
| No. Observations: | 23 | AIC: | 222.6 |
| Df Residuals: | 21 | BIC: | 224.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -120.6520 | 69.983 | -1.724 | 0.099 | -266.189 24.885 |
| expression | 46.7620 | 16.270 | 2.874 | 0.009 | 12.926 80.597 |
| Omnibus: | 1.881 | Durbin-Watson: | 2.052 |
| Prob(Omnibus): | 0.390 | Jarque-Bera (JB): | 1.499 |
| Skew: | 0.463 | Prob(JB): | 0.473 |
| Kurtosis: | 2.159 | Cond. No. | 51.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.872 | 0.369 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.610 |
| Model: | OLS | Adj. R-squared: | 0.504 |
| Method: | Least Squares | F-statistic: | 5.733 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0130 |
| Time: | 05:01:43 | Log-Likelihood: | -68.240 |
| No. Observations: | 15 | AIC: | 144.5 |
| Df Residuals: | 11 | BIC: | 147.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 53.2024 | 63.907 | 0.832 | 0.423 | -87.456 193.860 |
| C(dose)[T.1] | 256.8004 | 111.786 | 2.297 | 0.042 | 10.761 502.839 |
| expression | 3.8913 | 17.261 | 0.225 | 0.826 | -34.099 41.882 |
| expression:C(dose)[T.1] | -56.4541 | 30.213 | -1.869 | 0.089 | -122.953 10.045 |
| Omnibus: | 0.478 | Durbin-Watson: | 0.929 |
| Prob(Omnibus): | 0.787 | Jarque-Bera (JB): | 0.314 |
| Skew: | -0.313 | Prob(JB): | 0.855 |
| Kurtosis: | 2.666 | Cond. No. | 79.4 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.486 |
| Model: | OLS | Adj. R-squared: | 0.400 |
| Method: | Least Squares | F-statistic: | 5.675 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0184 |
| Time: | 05:01:43 | Log-Likelihood: | -70.307 |
| No. Observations: | 15 | AIC: | 146.6 |
| Df Residuals: | 12 | BIC: | 148.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 120.5644 | 57.987 | 2.079 | 0.060 | -5.778 246.907 |
| C(dose)[T.1] | 49.5320 | 15.202 | 3.258 | 0.007 | 16.410 82.654 |
| expression | -14.5343 | 15.568 | -0.934 | 0.369 | -48.454 19.385 |
| Omnibus: | 1.569 | Durbin-Watson: | 0.946 |
| Prob(Omnibus): | 0.456 | Jarque-Bera (JB): | 1.255 |
| Skew: | -0.621 | Prob(JB): | 0.534 |
| Kurtosis: | 2.319 | Cond. No. | 30.6 |
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: | 05:01:43 | 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: | 0.031 |
| Model: | OLS | Adj. R-squared: | -0.043 |
| Method: | Least Squares | F-statistic: | 0.4220 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.527 |
| Time: | 05:01:43 | Log-Likelihood: | -75.060 |
| No. Observations: | 15 | AIC: | 154.1 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 142.5817 | 75.963 | 1.877 | 0.083 | -21.527 306.691 |
| expression | -13.3348 | 20.528 | -0.650 | 0.527 | -57.684 31.014 |
| Omnibus: | 0.982 | Durbin-Watson: | 1.806 |
| Prob(Omnibus): | 0.612 | Jarque-Bera (JB): | 0.706 |
| Skew: | -0.057 | Prob(JB): | 0.703 |
| Kurtosis: | 1.943 | Cond. No. | 30.1 |