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 |
| 0.251 | 0.622 | 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: | 15.62 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.30e-05 |
| Time: | 03:48:10 | Log-Likelihood: | -98.807 |
| No. Observations: | 23 | AIC: | 205.6 |
| Df Residuals: | 19 | BIC: | 210.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 126.0676 | 80.326 | 1.569 | 0.133 | -42.057 294.193 |
| C(dose)[T.1] | -174.6211 | 117.650 | -1.484 | 0.154 | -420.866 71.624 |
| expression | -28.2743 | 31.528 | -0.897 | 0.381 | -94.263 37.714 |
| expression:C(dose)[T.1] | 93.0854 | 47.558 | 1.957 | 0.065 | -6.456 192.626 |
| Omnibus: | 1.021 | Durbin-Watson: | 1.696 |
| Prob(Omnibus): | 0.600 | Jarque-Bera (JB): | 0.927 |
| Skew: | -0.292 | Prob(JB): | 0.629 |
| Kurtosis: | 2.209 | Cond. No. | 105. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 18.85 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.50e-05 |
| Time: | 03:48:10 | Log-Likelihood: | -100.92 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 22.0986 | 64.379 | 0.343 | 0.735 | -112.193 156.390 |
| C(dose)[T.1] | 55.0172 | 9.338 | 5.892 | 0.000 | 35.538 74.497 |
| expression | 12.6341 | 25.220 | 0.501 | 0.622 | -39.973 65.241 |
| Omnibus: | 0.514 | Durbin-Watson: | 1.895 |
| Prob(Omnibus): | 0.773 | Jarque-Bera (JB): | 0.600 |
| Skew: | 0.125 | Prob(JB): | 0.741 |
| Kurtosis: | 2.249 | Cond. No. | 43.2 |
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: | 03:48:10 | 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: | 0.052 |
| Model: | OLS | Adj. R-squared: | 0.007 |
| Method: | Least Squares | F-statistic: | 1.149 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.296 |
| Time: | 03:48:10 | Log-Likelihood: | -112.49 |
| No. Observations: | 23 | AIC: | 229.0 |
| Df Residuals: | 21 | BIC: | 231.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 180.6353 | 94.400 | 1.914 | 0.069 | -15.679 376.950 |
| expression | -40.7271 | 37.991 | -1.072 | 0.296 | -119.733 38.279 |
| Omnibus: | 1.214 | Durbin-Watson: | 2.521 |
| Prob(Omnibus): | 0.545 | Jarque-Bera (JB): | 1.069 |
| Skew: | 0.475 | Prob(JB): | 0.586 |
| Kurtosis: | 2.538 | Cond. No. | 38.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.284 | 0.604 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.464 |
| Model: | OLS | Adj. R-squared: | 0.318 |
| Method: | Least Squares | F-statistic: | 3.171 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0676 |
| Time: | 03:48:10 | Log-Likelihood: | -70.626 |
| No. Observations: | 15 | AIC: | 149.3 |
| Df Residuals: | 11 | BIC: | 152.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 41.5404 | 282.243 | 0.147 | 0.886 | -579.672 662.753 |
| C(dose)[T.1] | 117.5952 | 292.966 | 0.401 | 0.696 | -527.219 762.409 |
| expression | 10.6542 | 116.054 | 0.092 | 0.929 | -244.779 266.088 |
| expression:C(dose)[T.1] | -25.6102 | 119.234 | -0.215 | 0.834 | -288.043 236.823 |
| Omnibus: | 1.691 | Durbin-Watson: | 0.784 |
| Prob(Omnibus): | 0.429 | Jarque-Bera (JB): | 1.319 |
| Skew: | -0.655 | Prob(JB): | 0.517 |
| Kurtosis: | 2.371 | Cond. No. | 184. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.462 |
| Model: | OLS | Adj. R-squared: | 0.372 |
| Method: | Least Squares | F-statistic: | 5.142 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0244 |
| Time: | 03:48:10 | Log-Likelihood: | -70.658 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 12 | BIC: | 149.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 100.4942 | 63.101 | 1.593 | 0.137 | -36.992 237.980 |
| C(dose)[T.1] | 54.8101 | 18.790 | 2.917 | 0.013 | 13.870 95.750 |
| expression | -13.6081 | 25.545 | -0.533 | 0.604 | -69.265 42.049 |
| Omnibus: | 1.858 | Durbin-Watson: | 0.800 |
| Prob(Omnibus): | 0.395 | Jarque-Bera (JB): | 1.425 |
| Skew: | -0.691 | Prob(JB): | 0.490 |
| Kurtosis: | 2.390 | Cond. No. | 25.7 |
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: | 03:48:10 | 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: | 0.080 |
| Model: | OLS | Adj. R-squared: | 0.009 |
| Method: | Least Squares | F-statistic: | 1.125 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.308 |
| Time: | 03:48:10 | Log-Likelihood: | -74.677 |
| No. Observations: | 15 | AIC: | 153.4 |
| Df Residuals: | 13 | BIC: | 154.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 18.9893 | 71.063 | 0.267 | 0.793 | -134.534 172.512 |
| expression | 28.1816 | 26.564 | 1.061 | 0.308 | -29.207 85.570 |
| Omnibus: | 0.484 | Durbin-Watson: | 1.454 |
| Prob(Omnibus): | 0.785 | Jarque-Bera (JB): | 0.535 |
| Skew: | -0.050 | Prob(JB): | 0.765 |
| Kurtosis: | 2.080 | Cond. No. | 22.2 |