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 |
| 0.370 | 0.550 | 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: | 0.607 |
| Method: | Least Squares | F-statistic: | 12.35 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000104 |
| Time: | 04:46:05 | Log-Likelihood: | -100.67 |
| 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 | 97.9899 | 54.006 | 1.814 | 0.085 | -15.047 211.026 |
| C(dose)[T.1] | 10.7420 | 73.450 | 0.146 | 0.885 | -142.990 164.474 |
| expression | -7.2363 | 8.869 | -0.816 | 0.425 | -25.799 11.326 |
| expression:C(dose)[T.1] | 7.0204 | 12.622 | 0.556 | 0.585 | -19.398 33.439 |
| Omnibus: | 0.226 | Durbin-Watson: | 2.271 |
| Prob(Omnibus): | 0.893 | Jarque-Bera (JB): | 0.424 |
| Skew: | -0.010 | Prob(JB): | 0.809 |
| Kurtosis: | 2.335 | Cond. No. | 129. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.655 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 19.02 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.36e-05 |
| Time: | 04:46:05 | Log-Likelihood: | -100.85 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 77.0204 | 37.995 | 2.027 | 0.056 | -2.235 156.276 |
| C(dose)[T.1] | 51.2503 | 9.343 | 5.485 | 0.000 | 31.761 70.740 |
| expression | -3.7704 | 6.201 | -0.608 | 0.550 | -16.705 9.164 |
| Omnibus: | 0.161 | Durbin-Watson: | 2.123 |
| Prob(Omnibus): | 0.923 | Jarque-Bera (JB): | 0.376 |
| Skew: | 0.043 | Prob(JB): | 0.828 |
| Kurtosis: | 2.379 | Cond. No. | 53.0 |
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, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 04:46:05 | 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: | 0.137 |
| Model: | OLS | Adj. R-squared: | 0.096 |
| Method: | Least Squares | F-statistic: | 3.334 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0821 |
| Time: | 04:46:05 | Log-Likelihood: | -111.41 |
| No. Observations: | 23 | AIC: | 226.8 |
| Df Residuals: | 21 | BIC: | 229.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 173.8180 | 51.967 | 3.345 | 0.003 | 65.747 281.889 |
| expression | -16.2647 | 8.907 | -1.826 | 0.082 | -34.788 2.259 |
| Omnibus: | 1.358 | Durbin-Watson: | 2.851 |
| Prob(Omnibus): | 0.507 | Jarque-Bera (JB): | 1.168 |
| Skew: | 0.384 | Prob(JB): | 0.558 |
| Kurtosis: | 2.207 | Cond. No. | 46.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.434 | 0.523 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.537 |
| Model: | OLS | Adj. R-squared: | 0.410 |
| Method: | Least Squares | F-statistic: | 4.248 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0319 |
| Time: | 04:46:05 | Log-Likelihood: | -69.529 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 11 | BIC: | 149.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -14.3029 | 73.398 | -0.195 | 0.849 | -175.850 147.244 |
| C(dose)[T.1] | 279.0239 | 179.514 | 1.554 | 0.148 | -116.085 674.132 |
| expression | 12.7835 | 11.350 | 1.126 | 0.284 | -12.198 37.765 |
| expression:C(dose)[T.1] | -36.6252 | 28.664 | -1.278 | 0.228 | -99.715 26.465 |
| Omnibus: | 0.299 | Durbin-Watson: | 1.369 |
| Prob(Omnibus): | 0.861 | Jarque-Bera (JB): | 0.455 |
| Skew: | -0.188 | Prob(JB): | 0.797 |
| Kurtosis: | 2.235 | Cond. No. | 181. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.468 |
| Model: | OLS | Adj. R-squared: | 0.379 |
| Method: | Least Squares | F-statistic: | 5.278 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0227 |
| Time: | 04:46:05 | Log-Likelihood: | -70.567 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 22.4119 | 69.296 | 0.323 | 0.752 | -128.572 173.396 |
| C(dose)[T.1] | 50.4770 | 15.585 | 3.239 | 0.007 | 16.521 84.433 |
| expression | 7.0410 | 10.694 | 0.658 | 0.523 | -16.259 30.341 |
| Omnibus: | 3.773 | Durbin-Watson: | 0.887 |
| Prob(Omnibus): | 0.152 | Jarque-Bera (JB): | 2.255 |
| Skew: | -0.950 | Prob(JB): | 0.324 |
| Kurtosis: | 2.992 | Cond. No. | 58.6 |
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: | 04:46:05 | 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: | 0.003 |
| Model: | OLS | Adj. R-squared: | -0.074 |
| Method: | Least Squares | F-statistic: | 0.03795 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.849 |
| Time: | 04:46:05 | Log-Likelihood: | -75.278 |
| 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 | 76.5483 | 88.455 | 0.865 | 0.403 | -114.547 267.644 |
| expression | 2.7187 | 13.956 | 0.195 | 0.849 | -27.430 32.868 |
| Omnibus: | 1.089 | Durbin-Watson: | 1.645 |
| Prob(Omnibus): | 0.580 | Jarque-Bera (JB): | 0.746 |
| Skew: | 0.102 | Prob(JB): | 0.689 |
| Kurtosis: | 1.927 | Cond. No. | 56.6 |