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 |
| 1.478 | 0.238 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.673 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 13.05 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.37e-05 |
| Time: | 04:04:14 | Log-Likelihood: | -100.24 |
| No. Observations: | 23 | AIC: | 208.5 |
| Df Residuals: | 19 | BIC: | 213.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 144.5630 | 130.069 | 1.111 | 0.280 | -127.674 416.800 |
| C(dose)[T.1] | 41.1541 | 153.609 | 0.268 | 0.792 | -280.353 362.661 |
| expression | -11.9716 | 17.215 | -0.695 | 0.495 | -48.003 24.060 |
| expression:C(dose)[T.1] | 0.3070 | 21.075 | 0.015 | 0.989 | -43.804 44.418 |
| Omnibus: | 0.349 | Durbin-Watson: | 1.995 |
| Prob(Omnibus): | 0.840 | Jarque-Bera (JB): | 0.499 |
| Skew: | 0.041 | Prob(JB): | 0.779 |
| Kurtosis: | 2.283 | Cond. No. | 360. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.673 |
| Model: | OLS | Adj. R-squared: | 0.641 |
| Method: | Least Squares | F-statistic: | 20.60 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.39e-05 |
| Time: | 04:04:14 | Log-Likelihood: | -100.24 |
| No. Observations: | 23 | AIC: | 206.5 |
| Df Residuals: | 20 | BIC: | 209.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 143.0170 | 73.290 | 1.951 | 0.065 | -9.862 295.896 |
| C(dose)[T.1] | 43.3848 | 11.775 | 3.685 | 0.001 | 18.823 67.946 |
| expression | -11.7668 | 9.680 | -1.216 | 0.238 | -31.958 8.424 |
| Omnibus: | 0.342 | Durbin-Watson: | 1.989 |
| Prob(Omnibus): | 0.843 | Jarque-Bera (JB): | 0.496 |
| Skew: | 0.043 | Prob(JB): | 0.781 |
| Kurtosis: | 2.286 | Cond. No. | 128. |
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: | 04:04:14 | 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: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.425 |
| Method: | Least Squares | F-statistic: | 17.28 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000447 |
| Time: | 04:04:14 | Log-Likelihood: | -106.20 |
| No. Observations: | 23 | AIC: | 216.4 |
| Df Residuals: | 21 | BIC: | 218.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 340.8938 | 63.061 | 5.406 | 0.000 | 209.751 472.037 |
| expression | -36.5644 | 8.797 | -4.157 | 0.000 | -54.858 -18.271 |
| Omnibus: | 0.296 | Durbin-Watson: | 2.123 |
| Prob(Omnibus): | 0.862 | Jarque-Bera (JB): | 0.311 |
| Skew: | 0.228 | Prob(JB): | 0.856 |
| Kurtosis: | 2.658 | Cond. No. | 86.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.245 | 0.286 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.503 |
| Model: | OLS | Adj. R-squared: | 0.367 |
| Method: | Least Squares | F-statistic: | 3.707 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0460 |
| Time: | 04:04:14 | Log-Likelihood: | -70.061 |
| No. Observations: | 15 | AIC: | 148.1 |
| Df Residuals: | 11 | BIC: | 151.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 201.8230 | 149.312 | 1.352 | 0.204 | -126.811 530.457 |
| C(dose)[T.1] | -0.7523 | 203.119 | -0.004 | 0.997 | -447.815 446.310 |
| expression | -16.3402 | 18.101 | -0.903 | 0.386 | -56.180 23.500 |
| expression:C(dose)[T.1] | 5.4863 | 25.279 | 0.217 | 0.832 | -50.153 61.126 |
| Omnibus: | 1.861 | Durbin-Watson: | 0.861 |
| Prob(Omnibus): | 0.394 | Jarque-Bera (JB): | 1.187 |
| Skew: | -0.417 | Prob(JB): | 0.552 |
| Kurtosis: | 1.904 | Cond. No. | 284. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.501 |
| Model: | OLS | Adj. R-squared: | 0.417 |
| Method: | Least Squares | F-statistic: | 6.014 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0155 |
| Time: | 04:04:14 | Log-Likelihood: | -70.093 |
| No. Observations: | 15 | AIC: | 146.2 |
| Df Residuals: | 12 | BIC: | 148.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 178.6878 | 100.312 | 1.781 | 0.100 | -39.872 397.248 |
| C(dose)[T.1] | 43.1827 | 15.922 | 2.712 | 0.019 | 8.492 77.873 |
| expression | -13.5274 | 12.124 | -1.116 | 0.286 | -39.942 12.888 |
| Omnibus: | 2.013 | Durbin-Watson: | 0.848 |
| Prob(Omnibus): | 0.366 | Jarque-Bera (JB): | 1.204 |
| Skew: | -0.400 | Prob(JB): | 0.548 |
| Kurtosis: | 1.866 | Cond. No. | 110. |
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: | 04:04:14 | 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: | 0.194 |
| Model: | OLS | Adj. R-squared: | 0.132 |
| Method: | Least Squares | F-statistic: | 3.138 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0999 |
| Time: | 04:04:14 | Log-Likelihood: | -73.678 |
| No. Observations: | 15 | AIC: | 151.4 |
| Df Residuals: | 13 | BIC: | 152.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 290.6272 | 111.561 | 2.605 | 0.022 | 49.614 531.641 |
| expression | -24.6581 | 13.920 | -1.771 | 0.100 | -54.730 5.414 |
| Omnibus: | 1.971 | Durbin-Watson: | 1.406 |
| Prob(Omnibus): | 0.373 | Jarque-Bera (JB): | 1.210 |
| Skew: | 0.413 | Prob(JB): | 0.546 |
| Kurtosis: | 1.880 | Cond. No. | 99.6 |