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 |
| 0.175 | 0.680 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.666 |
| Model: | OLS | Adj. R-squared: | 0.613 |
| Method: | Least Squares | F-statistic: | 12.63 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.01e-05 |
| Time: | 04:14:03 | Log-Likelihood: | -100.49 |
| No. Observations: | 23 | AIC: | 209.0 |
| Df Residuals: | 19 | BIC: | 213.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 11.2691 | 50.227 | 0.224 | 0.825 | -93.857 116.395 |
| C(dose)[T.1] | 128.3557 | 84.458 | 1.520 | 0.145 | -48.417 305.129 |
| expression | 7.4666 | 8.670 | 0.861 | 0.400 | -10.680 25.613 |
| expression:C(dose)[T.1] | -13.1116 | 14.720 | -0.891 | 0.384 | -43.921 17.698 |
| Omnibus: | 0.103 | Durbin-Watson: | 1.785 |
| Prob(Omnibus): | 0.950 | Jarque-Bera (JB): | 0.321 |
| Skew: | -0.066 | Prob(JB): | 0.852 |
| Kurtosis: | 2.437 | Cond. No. | 139. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.617 |
| Method: | Least Squares | F-statistic: | 18.74 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.60e-05 |
| Time: | 04:14:03 | Log-Likelihood: | -100.96 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 37.4256 | 40.537 | 0.923 | 0.367 | -47.133 121.984 |
| C(dose)[T.1] | 53.5357 | 8.745 | 6.122 | 0.000 | 35.295 71.776 |
| expression | 2.9183 | 6.970 | 0.419 | 0.680 | -11.621 17.458 |
| Omnibus: | 0.703 | Durbin-Watson: | 1.829 |
| Prob(Omnibus): | 0.704 | Jarque-Bera (JB): | 0.665 |
| Skew: | -0.013 | Prob(JB): | 0.717 |
| Kurtosis: | 2.167 | Cond. No. | 55.2 |
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: | 04:14:03 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.002754 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.959 |
| Time: | 04:14:03 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 76.2619 | 66.240 | 1.151 | 0.263 | -61.492 214.016 |
| expression | 0.6043 | 11.515 | 0.052 | 0.959 | -23.342 24.551 |
| Omnibus: | 3.320 | Durbin-Watson: | 2.480 |
| Prob(Omnibus): | 0.190 | Jarque-Bera (JB): | 1.579 |
| Skew: | 0.293 | Prob(JB): | 0.454 |
| Kurtosis: | 1.858 | Cond. No. | 54.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.111 | 0.744 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.471 |
| Model: | OLS | Adj. R-squared: | 0.327 |
| Method: | Least Squares | F-statistic: | 3.263 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0632 |
| Time: | 04:14:03 | Log-Likelihood: | -70.527 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 151.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -0.5857 | 204.584 | -0.003 | 0.998 | -450.872 449.701 |
| C(dose)[T.1] | 194.6575 | 243.351 | 0.800 | 0.441 | -340.955 730.270 |
| expression | 13.0996 | 39.338 | 0.333 | 0.745 | -73.482 99.682 |
| expression:C(dose)[T.1] | -27.5481 | 46.343 | -0.594 | 0.564 | -129.548 74.451 |
| Omnibus: | 2.095 | Durbin-Watson: | 0.705 |
| Prob(Omnibus): | 0.351 | Jarque-Bera (JB): | 1.626 |
| Skew: | -0.718 | Prob(JB): | 0.443 |
| Kurtosis: | 2.267 | Cond. No. | 244. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.454 |
| Model: | OLS | Adj. R-squared: | 0.363 |
| Method: | Least Squares | F-statistic: | 4.986 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0265 |
| Time: | 04:14:03 | Log-Likelihood: | -70.764 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 102.4751 | 105.644 | 0.970 | 0.351 | -127.704 332.654 |
| C(dose)[T.1] | 50.3311 | 16.032 | 3.139 | 0.009 | 15.401 85.261 |
| expression | -6.7500 | 20.227 | -0.334 | 0.744 | -50.822 37.322 |
| Omnibus: | 2.580 | Durbin-Watson: | 0.844 |
| Prob(Omnibus): | 0.275 | Jarque-Bera (JB): | 1.883 |
| Skew: | -0.828 | Prob(JB): | 0.390 |
| Kurtosis: | 2.481 | Cond. No. | 74.5 |
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: | 04:14:03 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.071 |
| Method: | Least Squares | F-statistic: | 0.06870 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.797 |
| Time: | 04:14:03 | Log-Likelihood: | -75.261 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 58.1838 | 135.754 | 0.429 | 0.675 | -235.095 351.463 |
| expression | 6.7180 | 25.631 | 0.262 | 0.797 | -48.654 62.090 |
| Omnibus: | 0.699 | Durbin-Watson: | 1.607 |
| Prob(Omnibus): | 0.705 | Jarque-Bera (JB): | 0.618 |
| Skew: | 0.068 | Prob(JB): | 0.734 |
| Kurtosis: | 2.015 | Cond. No. | 73.5 |