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 |
| 0.077 | 0.785 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.673 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 13.03 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.43e-05 |
| Time: | 04:19:36 | Log-Likelihood: | -100.25 |
| No. Observations: | 23 | AIC: | 208.5 |
| Df Residuals: | 19 | BIC: | 213.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 41.0081 | 69.948 | 0.586 | 0.565 | -105.395 187.411 |
| C(dose)[T.1] | 245.9105 | 169.645 | 1.450 | 0.163 | -109.161 600.982 |
| expression | 2.1462 | 11.331 | 0.189 | 0.852 | -21.569 25.862 |
| expression:C(dose)[T.1] | -33.1812 | 29.022 | -1.143 | 0.267 | -93.925 27.562 |
| Omnibus: | 0.167 | Durbin-Watson: | 1.965 |
| Prob(Omnibus): | 0.920 | Jarque-Bera (JB): | 0.071 |
| Skew: | 0.101 | Prob(JB): | 0.965 |
| Kurtosis: | 2.818 | Cond. No. | 274. |
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: | 18.60 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.73e-05 |
| Time: | 04:19:36 | Log-Likelihood: | -101.02 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 72.1156 | 64.933 | 1.111 | 0.280 | -63.331 207.562 |
| C(dose)[T.1] | 52.2575 | 9.582 | 5.454 | 0.000 | 32.270 72.245 |
| expression | -2.9115 | 10.511 | -0.277 | 0.785 | -24.838 19.015 |
| Omnibus: | 0.141 | Durbin-Watson: | 1.810 |
| Prob(Omnibus): | 0.932 | Jarque-Bera (JB): | 0.361 |
| Skew: | 0.036 | Prob(JB): | 0.835 |
| Kurtosis: | 2.391 | Cond. No. | 91.8 |
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: | 04:19:36 | 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: | 0.130 |
| Model: | OLS | Adj. R-squared: | 0.089 |
| Method: | Least Squares | F-statistic: | 3.151 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0904 |
| Time: | 04:19:36 | Log-Likelihood: | -111.50 |
| No. Observations: | 23 | AIC: | 227.0 |
| Df Residuals: | 21 | BIC: | 229.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 236.4056 | 88.532 | 2.670 | 0.014 | 52.293 420.518 |
| expression | -26.2319 | 14.779 | -1.775 | 0.090 | -56.966 4.502 |
| Omnibus: | 0.187 | Durbin-Watson: | 2.132 |
| Prob(Omnibus): | 0.911 | Jarque-Bera (JB): | 0.383 |
| Skew: | -0.132 | Prob(JB): | 0.826 |
| Kurtosis: | 2.425 | Cond. No. | 81.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 6.336 | 0.027 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.664 |
| Model: | OLS | Adj. R-squared: | 0.573 |
| Method: | Least Squares | F-statistic: | 7.250 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00591 |
| Time: | 04:19:36 | Log-Likelihood: | -67.118 |
| No. Observations: | 15 | AIC: | 142.2 |
| Df Residuals: | 11 | BIC: | 145.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -9.8684 | 122.461 | -0.081 | 0.937 | -279.402 259.666 |
| C(dose)[T.1] | -75.2051 | 145.568 | -0.517 | 0.616 | -395.598 245.187 |
| expression | 12.5015 | 19.748 | 0.633 | 0.540 | -30.963 55.966 |
| expression:C(dose)[T.1] | 21.4033 | 23.724 | 0.902 | 0.386 | -30.812 73.619 |
| Omnibus: | 7.781 | Durbin-Watson: | 1.547 |
| Prob(Omnibus): | 0.020 | Jarque-Bera (JB): | 4.766 |
| Skew: | -1.337 | Prob(JB): | 0.0923 |
| Kurtosis: | 3.690 | Cond. No. | 206. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.639 |
| Model: | OLS | Adj. R-squared: | 0.579 |
| Method: | Least Squares | F-statistic: | 10.63 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00220 |
| Time: | 04:19:36 | Log-Likelihood: | -67.653 |
| No. Observations: | 15 | AIC: | 141.3 |
| Df Residuals: | 12 | BIC: | 143.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -101.5666 | 67.777 | -1.499 | 0.160 | -249.241 46.107 |
| C(dose)[T.1] | 55.5936 | 12.984 | 4.282 | 0.001 | 27.304 83.884 |
| expression | 27.3321 | 10.858 | 2.517 | 0.027 | 3.674 50.990 |
| Omnibus: | 6.696 | Durbin-Watson: | 1.611 |
| Prob(Omnibus): | 0.035 | Jarque-Bera (JB): | 4.063 |
| Skew: | -1.253 | Prob(JB): | 0.131 |
| Kurtosis: | 3.467 | Cond. No. | 67.0 |
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: | 04:19:36 | 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: | 0.088 |
| Model: | OLS | Adj. R-squared: | 0.018 |
| Method: | Least Squares | F-statistic: | 1.257 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.283 |
| Time: | 04:19:36 | Log-Likelihood: | -74.608 |
| 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 | -16.7890 | 99.014 | -0.170 | 0.868 | -230.696 197.118 |
| expression | 18.2324 | 16.265 | 1.121 | 0.283 | -16.906 53.371 |
| Omnibus: | 0.561 | Durbin-Watson: | 2.264 |
| Prob(Omnibus): | 0.756 | Jarque-Bera (JB): | 0.600 |
| Skew: | -0.216 | Prob(JB): | 0.741 |
| Kurtosis: | 2.120 | Cond. No. | 63.8 |