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 |
| 0.065 | 0.801 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.608 |
| Method: | Least Squares | F-statistic: | 12.36 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000103 |
| Time: | 03:47:15 | Log-Likelihood: | -100.66 |
| No. Observations: | 23 | AIC: | 209.3 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 72.4176 | 45.985 | 1.575 | 0.132 | -23.831 168.666 |
| C(dose)[T.1] | 2.1964 | 64.950 | 0.034 | 0.973 | -133.746 138.139 |
| expression | -3.0827 | 7.716 | -0.400 | 0.694 | -19.232 13.067 |
| expression:C(dose)[T.1] | 8.3618 | 10.609 | 0.788 | 0.440 | -13.843 30.567 |
| Omnibus: | 0.438 | Durbin-Watson: | 1.974 |
| Prob(Omnibus): | 0.803 | Jarque-Bera (JB): | 0.567 |
| Skew: | 0.165 | Prob(JB): | 0.753 |
| Kurtosis: | 2.306 | Cond. No. | 121. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.59 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.74e-05 |
| Time: | 03:47:15 | Log-Likelihood: | -101.03 |
| 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 | 46.2916 | 31.570 | 1.466 | 0.158 | -19.562 112.145 |
| C(dose)[T.1] | 52.8934 | 8.926 | 5.926 | 0.000 | 34.274 71.513 |
| expression | 1.3402 | 5.245 | 0.256 | 0.801 | -9.601 12.282 |
| Omnibus: | 0.216 | Durbin-Watson: | 1.945 |
| Prob(Omnibus): | 0.898 | Jarque-Bera (JB): | 0.416 |
| Skew: | 0.071 | Prob(JB): | 0.812 |
| Kurtosis: | 2.356 | Cond. No. | 45.5 |
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: | 03:47:15 | 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: | 0.036 |
| Model: | OLS | Adj. R-squared: | -0.010 |
| Method: | Least Squares | F-statistic: | 0.7854 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.386 |
| Time: | 03:47:15 | Log-Likelihood: | -112.68 |
| No. Observations: | 23 | AIC: | 229.4 |
| Df Residuals: | 21 | BIC: | 231.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 34.9147 | 51.049 | 0.684 | 0.501 | -71.248 141.077 |
| expression | 7.3867 | 8.335 | 0.886 | 0.386 | -9.947 24.720 |
| Omnibus: | 1.493 | Durbin-Watson: | 2.506 |
| Prob(Omnibus): | 0.474 | Jarque-Bera (JB): | 1.037 |
| Skew: | 0.221 | Prob(JB): | 0.595 |
| Kurtosis: | 2.059 | Cond. No. | 45.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.074 | 0.790 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.453 |
| Model: | OLS | Adj. R-squared: | 0.304 |
| Method: | Least Squares | F-statistic: | 3.040 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0746 |
| Time: | 03:47:15 | Log-Likelihood: | -70.772 |
| No. Observations: | 15 | AIC: | 149.5 |
| Df Residuals: | 11 | BIC: | 152.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 72.2122 | 55.812 | 1.294 | 0.222 | -50.630 195.054 |
| C(dose)[T.1] | 65.1290 | 91.898 | 0.709 | 0.493 | -137.136 267.394 |
| expression | -1.2430 | 14.166 | -0.088 | 0.932 | -32.421 29.935 |
| expression:C(dose)[T.1] | -3.0531 | 20.604 | -0.148 | 0.885 | -48.402 42.295 |
| Omnibus: | 1.875 | Durbin-Watson: | 0.920 |
| Prob(Omnibus): | 0.392 | Jarque-Bera (JB): | 1.366 |
| Skew: | -0.697 | Prob(JB): | 0.505 |
| Kurtosis: | 2.505 | Cond. No. | 69.3 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.361 |
| Method: | Least Squares | F-statistic: | 4.952 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0270 |
| Time: | 03:47:15 | Log-Likelihood: | -70.787 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 77.7664 | 39.633 | 1.962 | 0.073 | -8.586 164.119 |
| C(dose)[T.1] | 51.8116 | 18.394 | 2.817 | 0.016 | 11.735 91.888 |
| expression | -2.6862 | 9.858 | -0.272 | 0.790 | -24.166 18.793 |
| Omnibus: | 2.203 | Durbin-Watson: | 0.917 |
| Prob(Omnibus): | 0.332 | Jarque-Bera (JB): | 1.566 |
| Skew: | -0.758 | Prob(JB): | 0.457 |
| Kurtosis: | 2.544 | Cond. No. | 24.3 |
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: | 03:47:15 | 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: | 0.090 |
| Model: | OLS | Adj. R-squared: | 0.020 |
| Method: | Least Squares | F-statistic: | 1.285 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.278 |
| Time: | 03:47:15 | Log-Likelihood: | -74.593 |
| No. Observations: | 15 | AIC: | 153.2 |
| Df Residuals: | 13 | BIC: | 154.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 42.1119 | 46.508 | 0.905 | 0.382 | -58.362 142.586 |
| expression | 11.8035 | 10.414 | 1.133 | 0.278 | -10.695 34.302 |
| Omnibus: | 1.003 | Durbin-Watson: | 1.170 |
| Prob(Omnibus): | 0.606 | Jarque-Bera (JB): | 0.741 |
| Skew: | -0.162 | Prob(JB): | 0.690 |
| Kurtosis: | 1.960 | Cond. No. | 22.5 |