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 |
| 0.796 | 0.383 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.663 |
| Model: | OLS | Adj. R-squared: | 0.609 |
| Method: | Least Squares | F-statistic: | 12.43 |
| Date: | Tue, 21 May 2024 | Prob (F-statistic): | 9.94e-05 |
| Time: | 00:05:07 | Log-Likelihood: | -100.61 |
| No. Observations: | 23 | AIC: | 209.2 |
| Df Residuals: | 19 | BIC: | 213.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 85.4670 | 48.590 | 1.759 | 0.095 | -16.234 187.168 |
| C(dose)[T.1] | 51.4908 | 70.517 | 0.730 | 0.474 | -96.102 199.084 |
| expression | -5.1784 | 7.986 | -0.648 | 0.524 | -21.893 11.536 |
| expression:C(dose)[T.1] | -0.3250 | 12.401 | -0.026 | 0.979 | -26.280 25.630 |
| Omnibus: | 0.347 | Durbin-Watson: | 1.880 |
| Prob(Omnibus): | 0.841 | Jarque-Bera (JB): | 0.507 |
| Skew: | 0.141 | Prob(JB): | 0.776 |
| Kurtosis: | 2.330 | Cond. No. | 119. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.629 |
| Method: | Least Squares | F-statistic: | 19.63 |
| Date: | Tue, 21 May 2024 | Prob (F-statistic): | 1.92e-05 |
| Time: | 00:05:08 | Log-Likelihood: | -100.61 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 86.2805 | 36.435 | 2.368 | 0.028 | 10.278 162.283 |
| C(dose)[T.1] | 49.6609 | 9.536 | 5.207 | 0.000 | 29.768 69.554 |
| expression | -5.3131 | 5.955 | -0.892 | 0.383 | -17.735 7.109 |
| Omnibus: | 0.337 | Durbin-Watson: | 1.878 |
| Prob(Omnibus): | 0.845 | Jarque-Bera (JB): | 0.500 |
| Skew: | 0.139 | Prob(JB): | 0.779 |
| Kurtosis: | 2.333 | Cond. No. | 50.9 |
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: | Tue, 21 May 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 00:05:08 | 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: | 0.205 |
| Model: | OLS | Adj. R-squared: | 0.167 |
| Method: | Least Squares | F-statistic: | 5.411 |
| Date: | Tue, 21 May 2024 | Prob (F-statistic): | 0.0301 |
| Time: | 00:05:08 | Log-Likelihood: | -110.47 |
| No. Observations: | 23 | AIC: | 224.9 |
| Df Residuals: | 21 | BIC: | 227.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 186.4765 | 46.346 | 4.024 | 0.001 | 90.095 282.858 |
| expression | -18.7117 | 8.044 | -2.326 | 0.030 | -35.441 -1.982 |
| Omnibus: | 1.175 | Durbin-Watson: | 2.048 |
| Prob(Omnibus): | 0.556 | Jarque-Bera (JB): | 1.087 |
| Skew: | 0.396 | Prob(JB): | 0.581 |
| Kurtosis: | 2.288 | Cond. No. | 42.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 10.705 | 0.007 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.722 |
| Model: | OLS | Adj. R-squared: | 0.646 |
| Method: | Least Squares | F-statistic: | 9.504 |
| Date: | Tue, 21 May 2024 | Prob (F-statistic): | 0.00218 |
| Time: | 00:05:08 | Log-Likelihood: | -65.710 |
| No. Observations: | 15 | AIC: | 139.4 |
| Df Residuals: | 11 | BIC: | 142.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -87.2529 | 128.470 | -0.679 | 0.511 | -370.013 195.507 |
| C(dose)[T.1] | -100.3324 | 162.767 | -0.616 | 0.550 | -458.581 257.916 |
| expression | 21.1257 | 17.507 | 1.207 | 0.253 | -17.407 59.658 |
| expression:C(dose)[T.1] | 15.1257 | 21.153 | 0.715 | 0.489 | -31.431 61.682 |
| Omnibus: | 0.474 | Durbin-Watson: | 1.549 |
| Prob(Omnibus): | 0.789 | Jarque-Bera (JB): | 0.547 |
| Skew: | 0.317 | Prob(JB): | 0.761 |
| Kurtosis: | 2.312 | Cond. No. | 326. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.709 |
| Model: | OLS | Adj. R-squared: | 0.660 |
| Method: | Least Squares | F-statistic: | 14.60 |
| Date: | Tue, 21 May 2024 | Prob (F-statistic): | 0.000611 |
| Time: | 00:05:08 | Log-Likelihood: | -66.050 |
| No. Observations: | 15 | AIC: | 138.1 |
| Df Residuals: | 12 | BIC: | 140.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -163.1175 | 70.957 | -2.299 | 0.040 | -317.720 -8.515 |
| C(dose)[T.1] | 15.5140 | 15.392 | 1.008 | 0.333 | -18.022 49.050 |
| expression | 31.4869 | 9.624 | 3.272 | 0.007 | 10.519 52.455 |
| Omnibus: | 0.310 | Durbin-Watson: | 1.327 |
| Prob(Omnibus): | 0.857 | Jarque-Bera (JB): | 0.461 |
| Skew: | 0.198 | Prob(JB): | 0.794 |
| Kurtosis: | 2.238 | Cond. No. | 101. |
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: | Tue, 21 May 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 00:05:08 | 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: | 0.684 |
| Model: | OLS | Adj. R-squared: | 0.660 |
| Method: | Least Squares | F-statistic: | 28.14 |
| Date: | Tue, 21 May 2024 | Prob (F-statistic): | 0.000143 |
| Time: | 00:05:08 | Log-Likelihood: | -66.660 |
| No. Observations: | 15 | AIC: | 137.3 |
| Df Residuals: | 13 | BIC: | 138.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -206.0465 | 56.788 | -3.628 | 0.003 | -328.729 -83.364 |
| expression | 37.9745 | 7.159 | 5.305 | 0.000 | 22.509 53.440 |
| Omnibus: | 0.933 | Durbin-Watson: | 1.651 |
| Prob(Omnibus): | 0.627 | Jarque-Bera (JB): | 0.098 |
| Skew: | 0.173 | Prob(JB): | 0.952 |
| Kurtosis: | 3.194 | Cond. No. | 80.1 |