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 |
| 0.018 | 0.895 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.706 |
| Model: | OLS | Adj. R-squared: | 0.659 |
| Method: | Least Squares | F-statistic: | 15.17 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.80e-05 |
| Time: | 22:53:02 | Log-Likelihood: | -99.046 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 19 | BIC: | 210.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 126.3906 | 58.915 | 2.145 | 0.045 | 3.079 249.702 |
| C(dose)[T.1] | -101.5033 | 81.971 | -1.238 | 0.231 | -273.072 70.065 |
| expression | -11.9253 | 9.688 | -1.231 | 0.233 | -32.202 8.352 |
| expression:C(dose)[T.1] | 26.3216 | 13.832 | 1.903 | 0.072 | -2.628 55.271 |
| Omnibus: | 3.070 | Durbin-Watson: | 2.149 |
| Prob(Omnibus): | 0.215 | Jarque-Bera (JB): | 1.555 |
| Skew: | -0.145 | Prob(JB): | 0.460 |
| Kurtosis: | 4.240 | Cond. No. | 157. |
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: | 18.52 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.81e-05 |
| Time: | 22:53:03 | Log-Likelihood: | -101.05 |
| 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 | 48.2312 | 44.922 | 1.074 | 0.296 | -45.475 141.938 |
| C(dose)[T.1] | 53.6445 | 9.060 | 5.921 | 0.000 | 34.746 72.543 |
| expression | 0.9875 | 7.354 | 0.134 | 0.895 | -14.352 16.327 |
| Omnibus: | 0.237 | Durbin-Watson: | 1.856 |
| Prob(Omnibus): | 0.888 | Jarque-Bera (JB): | 0.431 |
| Skew: | 0.031 | Prob(JB): | 0.806 |
| Kurtosis: | 2.332 | Cond. No. | 62.9 |
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: | Thu, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:53:03 | 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: | 0.035 |
| Model: | OLS | Adj. R-squared: | -0.011 |
| Method: | Least Squares | F-statistic: | 0.7555 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.395 |
| Time: | 22:53:03 | Log-Likelihood: | -112.70 |
| No. Observations: | 23 | AIC: | 229.4 |
| Df Residuals: | 21 | BIC: | 231.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 138.8417 | 68.389 | 2.030 | 0.055 | -3.381 281.064 |
| expression | -10.0143 | 11.521 | -0.869 | 0.395 | -33.974 13.945 |
| Omnibus: | 3.294 | Durbin-Watson: | 2.452 |
| Prob(Omnibus): | 0.193 | Jarque-Bera (JB): | 2.080 |
| Skew: | 0.525 | Prob(JB): | 0.354 |
| Kurtosis: | 1.967 | Cond. No. | 58.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.592 | 0.456 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.493 |
| Model: | OLS | Adj. R-squared: | 0.354 |
| Method: | Least Squares | F-statistic: | 3.560 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0510 |
| Time: | 22:53:03 | Log-Likelihood: | -70.211 |
| No. Observations: | 15 | AIC: | 148.4 |
| Df Residuals: | 11 | BIC: | 151.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 64.7802 | 172.456 | 0.376 | 0.714 | -314.792 444.352 |
| C(dose)[T.1] | 190.7843 | 223.951 | 0.852 | 0.412 | -302.128 683.697 |
| expression | 0.5172 | 33.607 | 0.015 | 0.988 | -73.451 74.485 |
| expression:C(dose)[T.1] | -26.9292 | 43.161 | -0.624 | 0.545 | -121.926 68.068 |
| Omnibus: | 1.171 | Durbin-Watson: | 0.750 |
| Prob(Omnibus): | 0.557 | Jarque-Bera (JB): | 0.973 |
| Skew: | -0.537 | Prob(JB): | 0.615 |
| Kurtosis: | 2.364 | Cond. No. | 214. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.475 |
| Model: | OLS | Adj. R-squared: | 0.387 |
| Method: | Least Squares | F-statistic: | 5.422 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0210 |
| Time: | 22:53:03 | Log-Likelihood: | -70.472 |
| No. Observations: | 15 | AIC: | 146.9 |
| Df Residuals: | 12 | BIC: | 149.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 148.3736 | 105.784 | 1.403 | 0.186 | -82.110 378.857 |
| C(dose)[T.1] | 51.4153 | 15.633 | 3.289 | 0.006 | 17.353 85.477 |
| expression | -15.8092 | 20.544 | -0.770 | 0.456 | -60.570 28.952 |
| Omnibus: | 1.930 | Durbin-Watson: | 0.913 |
| Prob(Omnibus): | 0.381 | Jarque-Bera (JB): | 1.466 |
| Skew: | -0.706 | Prob(JB): | 0.480 |
| Kurtosis: | 2.408 | Cond. No. | 74.9 |
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: | 22:53:04 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.076 |
| Method: | Least Squares | F-statistic: | 0.01566 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.902 |
| Time: | 22:53:04 | Log-Likelihood: | -75.291 |
| 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 | 111.0581 | 139.335 | 0.797 | 0.440 | -189.956 412.072 |
| expression | -3.3477 | 26.750 | -0.125 | 0.902 | -61.137 54.441 |
| Omnibus: | 0.523 | Durbin-Watson: | 1.635 |
| Prob(Omnibus): | 0.770 | Jarque-Bera (JB): | 0.550 |
| Skew: | 0.041 | Prob(JB): | 0.760 |
| Kurtosis: | 2.065 | Cond. No. | 74.1 |