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 |
| 0.528 | 0.476 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.604 |
| Method: | Least Squares | F-statistic: | 12.19 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000112 |
| Time: | 05:13:40 | Log-Likelihood: | -100.76 |
| No. Observations: | 23 | AIC: | 209.5 |
| Df Residuals: | 19 | BIC: | 214.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -137.5035 | 362.774 | -0.379 | 0.709 | -896.798 621.791 |
| C(dose)[T.1] | 47.5384 | 554.320 | 0.086 | 0.933 | -1112.667 1207.743 |
| expression | 21.7352 | 41.123 | 0.529 | 0.603 | -64.337 107.807 |
| expression:C(dose)[T.1] | -0.3723 | 61.202 | -0.006 | 0.995 | -128.470 127.726 |
| Omnibus: | 0.619 | Durbin-Watson: | 2.080 |
| Prob(Omnibus): | 0.734 | Jarque-Bera (JB): | 0.631 |
| Skew: | -0.033 | Prob(JB): | 0.729 |
| Kurtosis: | 2.191 | Cond. No. | 1.45e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 19.25 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.18e-05 |
| Time: | 05:13:40 | Log-Likelihood: | -100.76 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -136.0209 | 261.905 | -0.519 | 0.609 | -682.346 410.304 |
| C(dose)[T.1] | 44.1678 | 15.304 | 2.886 | 0.009 | 12.244 76.092 |
| expression | 21.5671 | 29.686 | 0.727 | 0.476 | -40.356 83.490 |
| Omnibus: | 0.613 | Durbin-Watson: | 2.079 |
| Prob(Omnibus): | 0.736 | Jarque-Bera (JB): | 0.629 |
| Skew: | -0.033 | Prob(JB): | 0.730 |
| Kurtosis: | 2.193 | Cond. No. | 555. |
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: | 05:13:40 | 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: | 0.516 |
| Model: | OLS | Adj. R-squared: | 0.493 |
| Method: | Least Squares | F-statistic: | 22.36 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000114 |
| Time: | 05:13:40 | Log-Likelihood: | -104.77 |
| No. Observations: | 23 | AIC: | 213.5 |
| Df Residuals: | 21 | BIC: | 215.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -752.4268 | 176.049 | -4.274 | 0.000 | -1118.540 -386.314 |
| expression | 92.2180 | 19.502 | 4.729 | 0.000 | 51.662 132.774 |
| Omnibus: | 2.003 | Durbin-Watson: | 2.494 |
| Prob(Omnibus): | 0.367 | Jarque-Bera (JB): | 1.195 |
| Skew: | -0.232 | Prob(JB): | 0.550 |
| Kurtosis: | 1.984 | Cond. No. | 320. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.211 | 0.163 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.535 |
| Model: | OLS | Adj. R-squared: | 0.408 |
| Method: | Least Squares | F-statistic: | 4.214 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0327 |
| Time: | 05:13:40 | Log-Likelihood: | -69.562 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 11 | BIC: | 150.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -326.4553 | 368.853 | -0.885 | 0.395 | -1138.295 485.384 |
| C(dose)[T.1] | 91.7176 | 524.256 | 0.175 | 0.864 | -1062.163 1245.598 |
| expression | 45.6991 | 42.776 | 1.068 | 0.308 | -48.450 139.848 |
| expression:C(dose)[T.1] | -3.8412 | 61.628 | -0.062 | 0.951 | -139.483 131.801 |
| Omnibus: | 1.192 | Durbin-Watson: | 1.000 |
| Prob(Omnibus): | 0.551 | Jarque-Bera (JB): | 0.986 |
| Skew: | -0.438 | Prob(JB): | 0.611 |
| Kurtosis: | 2.099 | Cond. No. | 790. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.535 |
| Model: | OLS | Adj. R-squared: | 0.457 |
| Method: | Least Squares | F-statistic: | 6.891 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0102 |
| Time: | 05:13:40 | Log-Likelihood: | -69.565 |
| No. Observations: | 15 | AIC: | 145.1 |
| Df Residuals: | 12 | BIC: | 147.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -310.5049 | 254.376 | -1.221 | 0.246 | -864.743 243.733 |
| C(dose)[T.1] | 59.0574 | 15.911 | 3.712 | 0.003 | 24.390 93.725 |
| expression | 43.8485 | 29.488 | 1.487 | 0.163 | -20.400 108.097 |
| Omnibus: | 1.106 | Durbin-Watson: | 0.970 |
| Prob(Omnibus): | 0.575 | Jarque-Bera (JB): | 0.931 |
| Skew: | -0.410 | Prob(JB): | 0.628 |
| Kurtosis: | 2.097 | Cond. No. | 305. |
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: | 05:13:40 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.077 |
| Method: | Least Squares | F-statistic: | 0.002193 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.963 |
| Time: | 05:13:40 | Log-Likelihood: | -75.299 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 108.6883 | 320.954 | 0.339 | 0.740 | -584.691 802.068 |
| expression | -1.7674 | 37.744 | -0.047 | 0.963 | -83.309 79.774 |
| Omnibus: | 0.510 | Durbin-Watson: | 1.610 |
| Prob(Omnibus): | 0.775 | Jarque-Bera (JB): | 0.544 |
| Skew: | 0.032 | Prob(JB): | 0.762 |
| Kurtosis: | 2.069 | Cond. No. | 272. |