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 |
| 0.008 | 0.928 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.714 |
| Model: | OLS | Adj. R-squared: | 0.669 |
| Method: | Least Squares | F-statistic: | 15.84 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.10e-05 |
| Time: | 04:55:27 | Log-Likelihood: | -98.694 |
| No. Observations: | 23 | AIC: | 205.4 |
| Df Residuals: | 19 | BIC: | 209.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 155.2524 | 82.693 | 1.877 | 0.076 | -17.827 328.331 |
| C(dose)[T.1] | -215.1123 | 129.294 | -1.664 | 0.113 | -485.729 55.504 |
| expression | -15.2067 | 12.416 | -1.225 | 0.236 | -41.194 10.781 |
| expression:C(dose)[T.1] | 41.3536 | 19.858 | 2.083 | 0.051 | -0.209 82.916 |
| Omnibus: | 0.060 | Durbin-Watson: | 1.956 |
| Prob(Omnibus): | 0.971 | Jarque-Bera (JB): | 0.079 |
| Skew: | -0.053 | Prob(JB): | 0.961 |
| Kurtosis: | 2.733 | Cond. No. | 264. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.51 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.82e-05 |
| Time: | 04:55:27 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 47.8237 | 69.814 | 0.685 | 0.501 | -97.805 193.453 |
| C(dose)[T.1] | 53.5699 | 9.127 | 5.869 | 0.000 | 34.531 72.609 |
| expression | 0.9609 | 10.467 | 0.092 | 0.928 | -20.873 22.795 |
| Omnibus: | 0.320 | Durbin-Watson: | 1.905 |
| Prob(Omnibus): | 0.852 | Jarque-Bera (JB): | 0.483 |
| Skew: | 0.054 | Prob(JB): | 0.785 |
| Kurtosis: | 2.298 | Cond. No. | 107. |
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: | 04:55:27 | 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: | 0.045 |
| Model: | OLS | Adj. R-squared: | -0.000 |
| Method: | Least Squares | F-statistic: | 0.9896 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.331 |
| Time: | 04:55:27 | Log-Likelihood: | -112.58 |
| No. Observations: | 23 | AIC: | 229.2 |
| Df Residuals: | 21 | BIC: | 231.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 184.8690 | 105.940 | 1.745 | 0.096 | -35.446 405.184 |
| expression | -16.1056 | 16.190 | -0.995 | 0.331 | -49.776 17.564 |
| Omnibus: | 2.434 | Durbin-Watson: | 2.290 |
| Prob(Omnibus): | 0.296 | Jarque-Bera (JB): | 1.814 |
| Skew: | 0.520 | Prob(JB): | 0.404 |
| Kurtosis: | 2.099 | Cond. No. | 101. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 5.515 | 0.037 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.625 |
| Model: | OLS | Adj. R-squared: | 0.523 |
| Method: | Least Squares | F-statistic: | 6.123 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0105 |
| Time: | 04:55:27 | Log-Likelihood: | -67.935 |
| No. Observations: | 15 | AIC: | 143.9 |
| Df Residuals: | 11 | BIC: | 146.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 351.0356 | 187.033 | 1.877 | 0.087 | -60.622 762.693 |
| C(dose)[T.1] | -21.7296 | 225.289 | -0.096 | 0.925 | -517.588 474.128 |
| expression | -38.6687 | 25.466 | -1.518 | 0.157 | -94.718 17.381 |
| expression:C(dose)[T.1] | 9.3246 | 30.776 | 0.303 | 0.768 | -58.412 77.061 |
| Omnibus: | 0.303 | Durbin-Watson: | 1.728 |
| Prob(Omnibus): | 0.859 | Jarque-Bera (JB): | 0.458 |
| Skew: | 0.175 | Prob(JB): | 0.795 |
| Kurtosis: | 2.219 | Cond. No. | 358. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.622 |
| Model: | OLS | Adj. R-squared: | 0.559 |
| Method: | Least Squares | F-statistic: | 9.887 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00290 |
| Time: | 04:55:27 | Log-Likelihood: | -67.997 |
| No. Observations: | 15 | AIC: | 142.0 |
| Df Residuals: | 12 | BIC: | 144.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 304.2105 | 101.278 | 3.004 | 0.011 | 83.544 524.877 |
| C(dose)[T.1] | 46.4051 | 13.082 | 3.547 | 0.004 | 17.901 74.909 |
| expression | -32.2843 | 13.748 | -2.348 | 0.037 | -62.238 -2.330 |
| Omnibus: | 0.333 | Durbin-Watson: | 1.691 |
| Prob(Omnibus): | 0.847 | Jarque-Bera (JB): | 0.474 |
| Skew: | 0.222 | Prob(JB): | 0.789 |
| Kurtosis: | 2.251 | Cond. No. | 116. |
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: | 04:55:27 | 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: | 0.226 |
| Model: | OLS | Adj. R-squared: | 0.167 |
| Method: | Least Squares | F-statistic: | 3.803 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0731 |
| Time: | 04:55:27 | Log-Likelihood: | -73.375 |
| No. Observations: | 15 | AIC: | 150.8 |
| Df Residuals: | 13 | BIC: | 152.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 361.2519 | 137.503 | 2.627 | 0.021 | 64.195 658.308 |
| expression | -36.7150 | 18.827 | -1.950 | 0.073 | -77.388 3.958 |
| Omnibus: | 1.638 | Durbin-Watson: | 2.244 |
| Prob(Omnibus): | 0.441 | Jarque-Bera (JB): | 1.179 |
| Skew: | 0.647 | Prob(JB): | 0.555 |
| Kurtosis: | 2.538 | Cond. No. | 114. |