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 |
| 3.932 | 0.061 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.709 |
| Model: | OLS | Adj. R-squared: | 0.663 |
| Method: | Least Squares | F-statistic: | 15.44 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.50e-05 |
| Time: | 23:03:01 | Log-Likelihood: | -98.906 |
| No. Observations: | 23 | AIC: | 205.8 |
| Df Residuals: | 19 | BIC: | 210.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -227.8659 | 203.434 | -1.120 | 0.277 | -653.658 197.926 |
| C(dose)[T.1] | -99.3790 | 368.965 | -0.269 | 0.791 | -871.631 672.873 |
| expression | 29.8028 | 21.486 | 1.387 | 0.181 | -15.167 74.773 |
| expression:C(dose)[T.1] | 15.0742 | 38.349 | 0.393 | 0.699 | -65.192 95.340 |
| Omnibus: | 0.791 | Durbin-Watson: | 2.084 |
| Prob(Omnibus): | 0.673 | Jarque-Bera (JB): | 0.731 |
| Skew: | 0.140 | Prob(JB): | 0.694 |
| Kurtosis: | 2.173 | Cond. No. | 1.05e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.707 |
| Model: | OLS | Adj. R-squared: | 0.677 |
| Method: | Least Squares | F-statistic: | 24.10 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 4.71e-06 |
| Time: | 23:03:01 | Log-Likelihood: | -98.999 |
| No. Observations: | 23 | AIC: | 204.0 |
| Df Residuals: | 20 | BIC: | 207.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -272.6502 | 164.936 | -1.653 | 0.114 | -616.702 71.401 |
| C(dose)[T.1] | 45.6085 | 8.914 | 5.116 | 0.000 | 27.013 64.204 |
| expression | 34.5345 | 17.417 | 1.983 | 0.061 | -1.796 70.865 |
| Omnibus: | 0.867 | Durbin-Watson: | 2.058 |
| Prob(Omnibus): | 0.648 | Jarque-Bera (JB): | 0.730 |
| Skew: | 0.028 | Prob(JB): | 0.694 |
| Kurtosis: | 2.129 | Cond. No. | 399. |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 23:03:01 | 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: | 0.323 |
| Model: | OLS | Adj. R-squared: | 0.291 |
| Method: | Least Squares | F-statistic: | 10.01 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00468 |
| Time: | 23:03:01 | Log-Likelihood: | -108.62 |
| No. Observations: | 23 | AIC: | 221.2 |
| Df Residuals: | 21 | BIC: | 223.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -623.7654 | 222.400 | -2.805 | 0.011 | -1086.272 -161.259 |
| expression | 73.4959 | 23.227 | 3.164 | 0.005 | 25.193 121.799 |
| Omnibus: | 0.146 | Durbin-Watson: | 2.662 |
| Prob(Omnibus): | 0.930 | Jarque-Bera (JB): | 0.194 |
| Skew: | 0.154 | Prob(JB): | 0.907 |
| Kurtosis: | 2.672 | Cond. No. | 363. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 5.311 | 0.040 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.621 |
| Model: | OLS | Adj. R-squared: | 0.517 |
| Method: | Least Squares | F-statistic: | 6.001 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0112 |
| Time: | 23:03:01 | Log-Likelihood: | -68.029 |
| No. Observations: | 15 | AIC: | 144.1 |
| Df Residuals: | 11 | BIC: | 146.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -388.4489 | 295.508 | -1.315 | 0.215 | -1038.858 261.960 |
| C(dose)[T.1] | -97.0906 | 476.085 | -0.204 | 0.842 | -1144.946 950.764 |
| expression | 47.7275 | 30.920 | 1.544 | 0.151 | -20.328 115.783 |
| expression:C(dose)[T.1] | 14.0649 | 49.217 | 0.286 | 0.780 | -94.262 122.392 |
| Omnibus: | 1.134 | Durbin-Watson: | 1.330 |
| Prob(Omnibus): | 0.567 | Jarque-Bera (JB): | 0.982 |
| Skew: | -0.488 | Prob(JB): | 0.612 |
| Kurtosis: | 2.214 | Cond. No. | 868. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.618 |
| Model: | OLS | Adj. R-squared: | 0.554 |
| Method: | Least Squares | F-statistic: | 9.703 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00311 |
| Time: | 23:03:01 | Log-Likelihood: | -68.085 |
| No. Observations: | 15 | AIC: | 142.2 |
| Df Residuals: | 12 | BIC: | 144.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -441.4723 | 221.021 | -1.997 | 0.069 | -923.035 40.091 |
| C(dose)[T.1] | 38.8985 | 13.845 | 2.810 | 0.016 | 8.732 69.065 |
| expression | 53.2787 | 23.118 | 2.305 | 0.040 | 2.909 103.648 |
| Omnibus: | 1.455 | Durbin-Watson: | 1.386 |
| Prob(Omnibus): | 0.483 | Jarque-Bera (JB): | 1.144 |
| Skew: | -0.496 | Prob(JB): | 0.564 |
| Kurtosis: | 2.080 | Cond. No. | 331. |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 23:03:01 | 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: | 0.367 |
| Model: | OLS | Adj. R-squared: | 0.318 |
| Method: | Least Squares | F-statistic: | 7.523 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0168 |
| Time: | 23:03:02 | Log-Likelihood: | -71.876 |
| No. Observations: | 15 | AIC: | 147.8 |
| Df Residuals: | 13 | BIC: | 149.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -623.1027 | 261.453 | -2.383 | 0.033 | -1187.938 -58.268 |
| expression | 74.2400 | 27.067 | 2.743 | 0.017 | 15.765 132.715 |
| Omnibus: | 0.996 | Durbin-Watson: | 1.892 |
| Prob(Omnibus): | 0.608 | Jarque-Bera (JB): | 0.801 |
| Skew: | 0.279 | Prob(JB): | 0.670 |
| Kurtosis: | 2.016 | Cond. No. | 316. |