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 |
| 2.908 | 0.104 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.787 |
| Model: | OLS | Adj. R-squared: | 0.754 |
| Method: | Least Squares | F-statistic: | 23.46 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.33e-06 |
| Time: | 03:36:27 | Log-Likelihood: | -95.299 |
| No. Observations: | 23 | AIC: | 198.6 |
| Df Residuals: | 19 | BIC: | 203.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 91.0387 | 61.844 | 1.472 | 0.157 | -38.403 220.480 |
| C(dose)[T.1] | -190.6530 | 86.165 | -2.213 | 0.039 | -370.999 -10.307 |
| expression | -5.4015 | 9.042 | -0.597 | 0.557 | -24.327 13.524 |
| expression:C(dose)[T.1] | 37.4473 | 12.934 | 2.895 | 0.009 | 10.376 64.518 |
| Omnibus: | 0.123 | Durbin-Watson: | 1.310 |
| Prob(Omnibus): | 0.940 | Jarque-Bera (JB): | 0.180 |
| Skew: | 0.141 | Prob(JB): | 0.914 |
| Kurtosis: | 2.670 | Cond. No. | 217. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.694 |
| Model: | OLS | Adj. R-squared: | 0.663 |
| Method: | Least Squares | F-statistic: | 22.64 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.29e-06 |
| Time: | 03:36:27 | Log-Likelihood: | -99.502 |
| No. Observations: | 23 | AIC: | 205.0 |
| Df Residuals: | 20 | BIC: | 208.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -33.7545 | 51.893 | -0.650 | 0.523 | -142.002 74.493 |
| C(dose)[T.1] | 57.9049 | 8.621 | 6.717 | 0.000 | 39.922 75.888 |
| expression | 12.9005 | 7.565 | 1.705 | 0.104 | -2.880 28.681 |
| Omnibus: | 0.962 | Durbin-Watson: | 1.809 |
| Prob(Omnibus): | 0.618 | Jarque-Bera (JB): | 0.881 |
| Skew: | 0.268 | Prob(JB): | 0.644 |
| Kurtosis: | 2.204 | Cond. No. | 86.8 |
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: | 03:36:27 | 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: | 0.05200 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.822 |
| Time: | 03:36:27 | 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 | 98.9162 | 84.499 | 1.171 | 0.255 | -76.809 274.642 |
| expression | -2.8874 | 12.662 | -0.228 | 0.822 | -29.219 23.444 |
| Omnibus: | 2.706 | Durbin-Watson: | 2.466 |
| Prob(Omnibus): | 0.258 | Jarque-Bera (JB): | 1.528 |
| Skew: | 0.342 | Prob(JB): | 0.466 |
| Kurtosis: | 1.938 | Cond. No. | 80.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.006 | 0.939 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.509 |
| Model: | OLS | Adj. R-squared: | 0.376 |
| Method: | Least Squares | F-statistic: | 3.807 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0429 |
| Time: | 03:36:27 | Log-Likelihood: | -69.959 |
| No. Observations: | 15 | AIC: | 147.9 |
| Df Residuals: | 11 | BIC: | 150.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -46.4012 | 135.477 | -0.343 | 0.738 | -344.585 251.782 |
| C(dose)[T.1] | 257.0616 | 179.152 | 1.435 | 0.179 | -137.248 651.372 |
| expression | 20.0883 | 23.825 | 0.843 | 0.417 | -32.350 72.527 |
| expression:C(dose)[T.1] | -36.0737 | 31.008 | -1.163 | 0.269 | -104.321 32.174 |
| Omnibus: | 1.469 | Durbin-Watson: | 1.017 |
| Prob(Omnibus): | 0.480 | Jarque-Bera (JB): | 0.861 |
| Skew: | -0.575 | Prob(JB): | 0.650 |
| Kurtosis: | 2.762 | Cond. No. | 192. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.890 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0280 |
| Time: | 03:36:27 | Log-Likelihood: | -70.829 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 74.2769 | 88.418 | 0.840 | 0.417 | -118.370 266.924 |
| C(dose)[T.1] | 49.4576 | 16.087 | 3.074 | 0.010 | 14.407 84.508 |
| expression | -1.2086 | 15.471 | -0.078 | 0.939 | -34.918 32.501 |
| Omnibus: | 2.620 | Durbin-Watson: | 0.830 |
| Prob(Omnibus): | 0.270 | Jarque-Bera (JB): | 1.799 |
| Skew: | -0.827 | Prob(JB): | 0.407 |
| Kurtosis: | 2.620 | Cond. No. | 67.6 |
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: | 03:36:27 | 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: | 0.015 |
| Model: | OLS | Adj. R-squared: | -0.061 |
| Method: | Least Squares | F-statistic: | 0.1992 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.663 |
| Time: | 03:36:27 | Log-Likelihood: | -75.186 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 43.4984 | 112.850 | 0.385 | 0.706 | -200.299 287.296 |
| expression | 8.6770 | 19.440 | 0.446 | 0.663 | -33.321 50.675 |
| Omnibus: | 1.295 | Durbin-Watson: | 1.518 |
| Prob(Omnibus): | 0.523 | Jarque-Bera (JB): | 0.803 |
| Skew: | 0.107 | Prob(JB): | 0.669 |
| Kurtosis: | 1.887 | Cond. No. | 66.9 |