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 |
| 0.164 | 0.689 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.718 |
| Model: | OLS | Adj. R-squared: | 0.673 |
| Method: | Least Squares | F-statistic: | 16.10 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.89e-05 |
| Time: | 04:32:55 | Log-Likelihood: | -98.561 |
| No. Observations: | 23 | AIC: | 205.1 |
| Df Residuals: | 19 | BIC: | 209.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 189.6077 | 80.163 | 2.365 | 0.029 | 21.825 357.390 |
| C(dose)[T.1] | -136.4071 | 90.260 | -1.511 | 0.147 | -325.323 52.508 |
| expression | -35.5228 | 20.980 | -1.693 | 0.107 | -79.435 8.389 |
| expression:C(dose)[T.1] | 49.1970 | 23.387 | 2.104 | 0.049 | 0.247 98.147 |
| Omnibus: | 0.396 | Durbin-Watson: | 1.774 |
| Prob(Omnibus): | 0.820 | Jarque-Bera (JB): | 0.360 |
| Skew: | 0.262 | Prob(JB): | 0.835 |
| Kurtosis: | 2.682 | 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: | 18.73 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.61e-05 |
| Time: | 04:32:55 | Log-Likelihood: | -100.97 |
| 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 | 38.7005 | 38.716 | 1.000 | 0.329 | -42.059 119.460 |
| C(dose)[T.1] | 52.6755 | 8.885 | 5.929 | 0.000 | 34.141 71.209 |
| expression | 4.0686 | 10.033 | 0.406 | 0.689 | -16.860 24.997 |
| Omnibus: | 0.347 | Durbin-Watson: | 1.957 |
| Prob(Omnibus): | 0.841 | Jarque-Bera (JB): | 0.499 |
| Skew: | 0.052 | Prob(JB): | 0.779 |
| Kurtosis: | 2.286 | Cond. No. | 37.3 |
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: | 04:32:55 | 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: | 0.040 |
| Model: | OLS | Adj. R-squared: | -0.005 |
| Method: | Least Squares | F-statistic: | 0.8799 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.359 |
| Time: | 04:32:55 | Log-Likelihood: | -112.63 |
| No. Observations: | 23 | AIC: | 229.3 |
| Df Residuals: | 21 | BIC: | 231.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 21.4099 | 62.561 | 0.342 | 0.736 | -108.693 151.513 |
| expression | 14.9914 | 15.982 | 0.938 | 0.359 | -18.245 48.228 |
| Omnibus: | 1.531 | Durbin-Watson: | 2.584 |
| Prob(Omnibus): | 0.465 | Jarque-Bera (JB): | 0.976 |
| Skew: | 0.124 | Prob(JB): | 0.614 |
| Kurtosis: | 2.022 | Cond. No. | 36.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.000 | 0.985 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.299 |
| Method: | Least Squares | F-statistic: | 2.994 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0772 |
| Time: | 04:32:55 | Log-Likelihood: | -70.823 |
| No. Observations: | 15 | AIC: | 149.6 |
| Df Residuals: | 11 | BIC: | 152.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 48.6812 | 182.288 | 0.267 | 0.794 | -352.533 449.895 |
| C(dose)[T.1] | 76.7312 | 232.681 | 0.330 | 0.748 | -435.397 588.859 |
| expression | 5.3179 | 51.596 | 0.103 | 0.920 | -108.244 118.880 |
| expression:C(dose)[T.1] | -7.9264 | 67.035 | -0.118 | 0.908 | -155.470 139.617 |
| Omnibus: | 2.778 | Durbin-Watson: | 0.826 |
| Prob(Omnibus): | 0.249 | Jarque-Bera (JB): | 1.914 |
| Skew: | -0.854 | Prob(JB): | 0.384 |
| Kurtosis: | 2.621 | Cond. No. | 147. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.885 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0280 |
| Time: | 04:32:55 | Log-Likelihood: | -70.833 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 65.2352 | 111.844 | 0.583 | 0.571 | -178.452 308.923 |
| C(dose)[T.1] | 49.2939 | 16.497 | 2.988 | 0.011 | 13.350 85.238 |
| expression | 0.6222 | 31.558 | 0.020 | 0.985 | -68.136 69.381 |
| Omnibus: | 2.755 | Durbin-Watson: | 0.814 |
| Prob(Omnibus): | 0.252 | Jarque-Bera (JB): | 1.888 |
| Skew: | -0.849 | Prob(JB): | 0.389 |
| Kurtosis: | 2.631 | Cond. No. | 53.8 |
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, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 04:32:55 | 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: | 0.039 |
| Model: | OLS | Adj. R-squared: | -0.035 |
| Method: | Least Squares | F-statistic: | 0.5229 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.482 |
| Time: | 04:32:55 | Log-Likelihood: | -75.004 |
| No. Observations: | 15 | AIC: | 154.0 |
| Df Residuals: | 13 | BIC: | 155.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 188.7443 | 131.863 | 1.431 | 0.176 | -96.128 473.616 |
| expression | -27.6242 | 38.202 | -0.723 | 0.482 | -110.155 54.907 |
| Omnibus: | 1.175 | Durbin-Watson: | 1.342 |
| Prob(Omnibus): | 0.556 | Jarque-Bera (JB): | 0.768 |
| Skew: | 0.097 | Prob(JB): | 0.681 |
| Kurtosis: | 1.908 | Cond. No. | 49.5 |