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 |
| 0.189 | 0.668 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.598 |
| Method: | Least Squares | F-statistic: | 11.91 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000129 |
| Time: | 05:13:45 | Log-Likelihood: | -100.94 |
| No. Observations: | 23 | AIC: | 209.9 |
| Df Residuals: | 19 | BIC: | 214.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 35.8857 | 184.649 | 0.194 | 0.848 | -350.590 422.361 |
| C(dose)[T.1] | 19.7449 | 219.115 | 0.090 | 0.929 | -438.868 478.357 |
| expression | 2.4366 | 24.542 | 0.099 | 0.922 | -48.930 53.803 |
| expression:C(dose)[T.1] | 4.5149 | 29.173 | 0.155 | 0.879 | -56.545 65.575 |
| Omnibus: | 0.367 | Durbin-Watson: | 1.873 |
| Prob(Omnibus): | 0.832 | Jarque-Bera (JB): | 0.509 |
| Skew: | 0.041 | Prob(JB): | 0.775 |
| Kurtosis: | 2.276 | Cond. No. | 532. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 18.76 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.58e-05 |
| Time: | 05:13:45 | Log-Likelihood: | -100.95 |
| 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 | 11.8588 | 97.496 | 0.122 | 0.904 | -191.514 215.231 |
| C(dose)[T.1] | 53.6275 | 8.754 | 6.126 | 0.000 | 35.367 71.888 |
| expression | 5.6318 | 12.941 | 0.435 | 0.668 | -21.362 32.625 |
| Omnibus: | 0.464 | Durbin-Watson: | 1.875 |
| Prob(Omnibus): | 0.793 | Jarque-Bera (JB): | 0.560 |
| Skew: | 0.041 | Prob(JB): | 0.756 |
| Kurtosis: | 2.240 | Cond. No. | 171. |
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: | 05:13:45 | 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: | -0.048 |
| Method: | Least Squares | F-statistic: | 0.0003696 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.985 |
| Time: | 05:13:45 | 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 | 82.7946 | 160.225 | 0.517 | 0.611 | -250.412 416.001 |
| expression | -0.4106 | 21.356 | -0.019 | 0.985 | -44.823 44.001 |
| Omnibus: | 3.289 | Durbin-Watson: | 2.487 |
| Prob(Omnibus): | 0.193 | Jarque-Bera (JB): | 1.568 |
| Skew: | 0.290 | Prob(JB): | 0.456 |
| Kurtosis: | 1.860 | Cond. No. | 170. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.461 | 0.250 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.511 |
| Model: | OLS | Adj. R-squared: | 0.378 |
| Method: | Least Squares | F-statistic: | 3.832 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0422 |
| Time: | 05:13:45 | Log-Likelihood: | -69.934 |
| No. Observations: | 15 | AIC: | 147.9 |
| Df Residuals: | 11 | BIC: | 150.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 274.0770 | 284.043 | 0.965 | 0.355 | -351.097 899.251 |
| C(dose)[T.1] | 148.8064 | 434.134 | 0.343 | 0.738 | -806.717 1104.330 |
| expression | -24.9961 | 34.330 | -0.728 | 0.482 | -100.557 50.565 |
| expression:C(dose)[T.1] | -12.4167 | 52.778 | -0.235 | 0.818 | -128.581 103.747 |
| Omnibus: | 3.182 | Durbin-Watson: | 0.764 |
| Prob(Omnibus): | 0.204 | Jarque-Bera (JB): | 2.007 |
| Skew: | -0.891 | Prob(JB): | 0.367 |
| Kurtosis: | 2.821 | Cond. No. | 603. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.509 |
| Model: | OLS | Adj. R-squared: | 0.427 |
| Method: | Least Squares | F-statistic: | 6.210 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0141 |
| Time: | 05:13:45 | Log-Likelihood: | -69.971 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 12 | BIC: | 148.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 317.5098 | 207.195 | 1.532 | 0.151 | -133.929 768.948 |
| C(dose)[T.1] | 46.7368 | 15.000 | 3.116 | 0.009 | 14.055 79.418 |
| expression | -30.2497 | 25.028 | -1.209 | 0.250 | -84.781 24.281 |
| Omnibus: | 3.299 | Durbin-Watson: | 0.755 |
| Prob(Omnibus): | 0.192 | Jarque-Bera (JB): | 2.120 |
| Skew: | -0.915 | Prob(JB): | 0.347 |
| Kurtosis: | 2.798 | Cond. No. | 234. |
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: | 05:13:45 | 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: | 0.111 |
| Model: | OLS | Adj. R-squared: | 0.043 |
| Method: | Least Squares | F-statistic: | 1.624 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.225 |
| Time: | 05:13:45 | Log-Likelihood: | -74.417 |
| No. Observations: | 15 | AIC: | 152.8 |
| Df Residuals: | 13 | BIC: | 154.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 429.4449 | 263.690 | 1.629 | 0.127 | -140.222 999.112 |
| expression | -40.8297 | 32.043 | -1.274 | 0.225 | -110.054 28.395 |
| Omnibus: | 0.414 | Durbin-Watson: | 1.875 |
| Prob(Omnibus): | 0.813 | Jarque-Bera (JB): | 0.506 |
| Skew: | 0.061 | Prob(JB): | 0.776 |
| Kurtosis: | 2.108 | Cond. No. | 230. |