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 |
| 2.443 | 0.134 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.693 |
| Model: | OLS | Adj. R-squared: | 0.644 |
| Method: | Least Squares | F-statistic: | 14.29 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 4.13e-05 |
| Time: | 22:55:12 | Log-Likelihood: | -99.528 |
| No. Observations: | 23 | AIC: | 207.1 |
| Df Residuals: | 19 | BIC: | 211.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 166.4039 | 70.080 | 2.374 | 0.028 | 19.725 313.083 |
| C(dose)[T.1] | -20.8781 | 126.371 | -0.165 | 0.871 | -285.376 243.619 |
| expression | -17.4456 | 10.859 | -1.607 | 0.125 | -40.174 5.283 |
| expression:C(dose)[T.1] | 11.5563 | 19.569 | 0.591 | 0.562 | -29.401 52.514 |
| Omnibus: | 0.775 | Durbin-Watson: | 1.397 |
| Prob(Omnibus): | 0.679 | Jarque-Bera (JB): | 0.694 |
| Skew: | -0.012 | Prob(JB): | 0.707 |
| Kurtosis: | 2.149 | Cond. No. | 238. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.687 |
| Model: | OLS | Adj. R-squared: | 0.656 |
| Method: | Least Squares | F-statistic: | 21.97 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 8.95e-06 |
| Time: | 22:55:12 | Log-Likelihood: | -99.738 |
| No. Observations: | 23 | AIC: | 205.5 |
| Df Residuals: | 20 | BIC: | 208.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 143.5165 | 57.430 | 2.499 | 0.021 | 23.720 263.313 |
| C(dose)[T.1] | 53.5853 | 8.280 | 6.471 | 0.000 | 36.313 70.858 |
| expression | -13.8868 | 8.885 | -1.563 | 0.134 | -32.422 4.648 |
| Omnibus: | 1.878 | Durbin-Watson: | 1.495 |
| Prob(Omnibus): | 0.391 | Jarque-Bera (JB): | 1.059 |
| Skew: | 0.104 | Prob(JB): | 0.589 |
| Kurtosis: | 1.969 | Cond. No. | 92.0 |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:55:12 | 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: | 0.032 |
| Model: | OLS | Adj. R-squared: | -0.014 |
| Method: | Least Squares | F-statistic: | 0.7028 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.411 |
| Time: | 22:55:13 | Log-Likelihood: | -112.73 |
| No. Observations: | 23 | AIC: | 229.5 |
| Df Residuals: | 21 | BIC: | 231.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 162.0434 | 98.460 | 1.646 | 0.115 | -42.715 366.801 |
| expression | -12.7841 | 15.250 | -0.838 | 0.411 | -44.498 18.929 |
| Omnibus: | 2.828 | Durbin-Watson: | 2.419 |
| Prob(Omnibus): | 0.243 | Jarque-Bera (JB): | 1.684 |
| Skew: | 0.408 | Prob(JB): | 0.431 |
| Kurtosis: | 1.955 | Cond. No. | 91.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.936 | 0.352 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.491 |
| Model: | OLS | Adj. R-squared: | 0.352 |
| Method: | Least Squares | F-statistic: | 3.531 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0521 |
| Time: | 22:55:13 | Log-Likelihood: | -70.242 |
| No. Observations: | 15 | AIC: | 148.5 |
| Df Residuals: | 11 | BIC: | 151.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -25.3222 | 103.011 | -0.246 | 0.810 | -252.047 201.403 |
| C(dose)[T.1] | 88.3017 | 215.214 | 0.410 | 0.689 | -385.380 561.983 |
| expression | 14.9693 | 16.521 | 0.906 | 0.384 | -21.392 51.331 |
| expression:C(dose)[T.1] | -6.7357 | 33.336 | -0.202 | 0.844 | -80.108 66.636 |
| Omnibus: | 3.655 | Durbin-Watson: | 1.074 |
| Prob(Omnibus): | 0.161 | Jarque-Bera (JB): | 2.225 |
| Skew: | -0.943 | Prob(JB): | 0.329 |
| Kurtosis: | 2.942 | Cond. No. | 216. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.489 |
| Model: | OLS | Adj. R-squared: | 0.403 |
| Method: | Least Squares | F-statistic: | 5.734 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0179 |
| Time: | 22:55:13 | Log-Likelihood: | -70.270 |
| No. Observations: | 15 | AIC: | 146.5 |
| Df Residuals: | 12 | BIC: | 148.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -15.0723 | 85.996 | -0.175 | 0.864 | -202.442 172.298 |
| C(dose)[T.1] | 44.9438 | 15.784 | 2.847 | 0.015 | 10.553 79.334 |
| expression | 13.3151 | 13.764 | 0.967 | 0.352 | -16.674 43.304 |
| Omnibus: | 4.132 | Durbin-Watson: | 1.033 |
| Prob(Omnibus): | 0.127 | Jarque-Bera (JB): | 2.424 |
| Skew: | -0.984 | Prob(JB): | 0.298 |
| Kurtosis: | 3.082 | Cond. No. | 74.8 |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:55:13 | 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: | 0.143 |
| Model: | OLS | Adj. R-squared: | 0.077 |
| Method: | Least Squares | F-statistic: | 2.172 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.164 |
| Time: | 22:55:13 | Log-Likelihood: | -74.141 |
| No. Observations: | 15 | AIC: | 152.3 |
| Df Residuals: | 13 | BIC: | 153.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -60.5897 | 105.088 | -0.577 | 0.574 | -287.619 166.440 |
| expression | 24.2298 | 16.440 | 1.474 | 0.164 | -11.288 59.747 |
| Omnibus: | 1.052 | Durbin-Watson: | 1.831 |
| Prob(Omnibus): | 0.591 | Jarque-Bera (JB): | 0.723 |
| Skew: | 0.034 | Prob(JB): | 0.697 |
| Kurtosis: | 1.926 | Cond. No. | 73.1 |