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 |
| 1.183 | 0.290 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.672 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 12.97 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.64e-05 |
| Time: | 04:56:44 | Log-Likelihood: | -100.29 |
| No. Observations: | 23 | AIC: | 208.6 |
| Df Residuals: | 19 | BIC: | 213.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 195.3830 | 130.664 | 1.495 | 0.151 | -78.099 468.865 |
| C(dose)[T.1] | -34.9828 | 187.200 | -0.187 | 0.854 | -426.797 356.831 |
| expression | -18.5233 | 17.126 | -1.082 | 0.293 | -54.368 17.322 |
| expression:C(dose)[T.1] | 11.1057 | 25.426 | 0.437 | 0.667 | -42.111 64.322 |
| Omnibus: | 0.928 | Durbin-Watson: | 1.898 |
| Prob(Omnibus): | 0.629 | Jarque-Bera (JB): | 0.753 |
| Skew: | 0.030 | Prob(JB): | 0.686 |
| Kurtosis: | 2.116 | Cond. No. | 412. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.669 |
| Model: | OLS | Adj. R-squared: | 0.636 |
| Method: | Least Squares | F-statistic: | 20.18 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.60e-05 |
| Time: | 04:56:44 | Log-Likelihood: | -100.40 |
| No. Observations: | 23 | AIC: | 206.8 |
| Df Residuals: | 20 | BIC: | 210.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 156.9813 | 94.686 | 1.658 | 0.113 | -40.529 354.492 |
| C(dose)[T.1] | 46.6501 | 10.508 | 4.439 | 0.000 | 24.730 68.570 |
| expression | -13.4846 | 12.399 | -1.088 | 0.290 | -39.349 12.380 |
| Omnibus: | 0.846 | Durbin-Watson: | 1.960 |
| Prob(Omnibus): | 0.655 | Jarque-Bera (JB): | 0.732 |
| Skew: | 0.081 | Prob(JB): | 0.693 |
| Kurtosis: | 2.141 | Cond. No. | 168. |
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: | 04:56:44 | 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: | 0.342 |
| Model: | OLS | Adj. R-squared: | 0.311 |
| Method: | Least Squares | F-statistic: | 10.92 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00337 |
| Time: | 04:56:44 | Log-Likelihood: | -108.29 |
| No. Observations: | 23 | AIC: | 220.6 |
| Df Residuals: | 21 | BIC: | 222.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 417.1367 | 102.267 | 4.079 | 0.001 | 204.460 629.813 |
| expression | -45.6941 | 13.827 | -3.305 | 0.003 | -74.448 -16.940 |
| Omnibus: | 0.495 | Durbin-Watson: | 2.201 |
| Prob(Omnibus): | 0.781 | Jarque-Bera (JB): | 0.585 |
| Skew: | 0.282 | Prob(JB): | 0.746 |
| Kurtosis: | 2.459 | Cond. No. | 132. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.641 | 0.439 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.484 |
| Model: | OLS | Adj. R-squared: | 0.343 |
| Method: | Least Squares | F-statistic: | 3.435 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0557 |
| Time: | 04:56:44 | Log-Likelihood: | -70.342 |
| No. Observations: | 15 | AIC: | 148.7 |
| Df Residuals: | 11 | BIC: | 151.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -15.0696 | 194.443 | -0.078 | 0.940 | -443.035 412.895 |
| C(dose)[T.1] | -95.6227 | 360.182 | -0.265 | 0.796 | -888.377 697.132 |
| expression | 10.1139 | 23.795 | 0.425 | 0.679 | -42.259 62.487 |
| expression:C(dose)[T.1] | 16.3864 | 42.590 | 0.385 | 0.708 | -77.353 110.126 |
| Omnibus: | 2.580 | Durbin-Watson: | 0.700 |
| Prob(Omnibus): | 0.275 | Jarque-Bera (JB): | 1.816 |
| Skew: | -0.825 | Prob(JB): | 0.403 |
| Kurtosis: | 2.568 | Cond. No. | 477. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.477 |
| Model: | OLS | Adj. R-squared: | 0.390 |
| Method: | Least Squares | F-statistic: | 5.466 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0205 |
| Time: | 04:56:44 | Log-Likelihood: | -70.443 |
| No. Observations: | 15 | AIC: | 146.9 |
| Df Residuals: | 12 | BIC: | 149.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -56.7927 | 155.560 | -0.365 | 0.721 | -395.728 282.143 |
| C(dose)[T.1] | 42.7849 | 17.300 | 2.473 | 0.029 | 5.090 80.479 |
| expression | 15.2290 | 19.021 | 0.801 | 0.439 | -26.215 56.673 |
| Omnibus: | 2.920 | Durbin-Watson: | 0.742 |
| Prob(Omnibus): | 0.232 | Jarque-Bera (JB): | 2.029 |
| Skew: | -0.880 | Prob(JB): | 0.362 |
| Kurtosis: | 2.610 | Cond. No. | 174. |
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: | 04:56:44 | 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: | 0.210 |
| Model: | OLS | Adj. R-squared: | 0.149 |
| Method: | Least Squares | F-statistic: | 3.456 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0858 |
| Time: | 04:56:44 | Log-Likelihood: | -73.532 |
| No. Observations: | 15 | AIC: | 151.1 |
| Df Residuals: | 13 | BIC: | 152.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -216.4787 | 167.068 | -1.296 | 0.218 | -577.408 144.450 |
| expression | 37.0038 | 19.904 | 1.859 | 0.086 | -5.996 80.004 |
| Omnibus: | 0.410 | Durbin-Watson: | 1.374 |
| Prob(Omnibus): | 0.815 | Jarque-Bera (JB): | 0.504 |
| Skew: | -0.049 | Prob(JB): | 0.777 |
| Kurtosis: | 2.108 | Cond. No. | 157. |