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 |
| 12.391 | 0.002 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.783 |
| Model: | OLS | Adj. R-squared: | 0.749 |
| Method: | Least Squares | F-statistic: | 22.91 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.58e-06 |
| Time: | 04:47:32 | Log-Likelihood: | -95.513 |
| No. Observations: | 23 | AIC: | 199.0 |
| Df Residuals: | 19 | BIC: | 203.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -67.1874 | 57.027 | -1.178 | 0.253 | -186.546 52.171 |
| C(dose)[T.1] | 65.2560 | 70.273 | 0.929 | 0.365 | -81.827 212.339 |
| expression | 21.2253 | 9.934 | 2.137 | 0.046 | 0.433 42.018 |
| expression:C(dose)[T.1] | -1.1235 | 12.436 | -0.090 | 0.929 | -27.153 24.906 |
| Omnibus: | 1.711 | Durbin-Watson: | 1.990 |
| Prob(Omnibus): | 0.425 | Jarque-Bera (JB): | 1.140 |
| Skew: | 0.256 | Prob(JB): | 0.566 |
| Kurtosis: | 2.037 | Cond. No. | 159. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.783 |
| Model: | OLS | Adj. R-squared: | 0.762 |
| Method: | Least Squares | F-statistic: | 36.15 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.28e-07 |
| Time: | 04:47:32 | Log-Likelihood: | -95.518 |
| No. Observations: | 23 | AIC: | 197.0 |
| Df Residuals: | 20 | BIC: | 200.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -63.0872 | 33.661 | -1.874 | 0.076 | -133.304 7.129 |
| C(dose)[T.1] | 58.9411 | 7.073 | 8.334 | 0.000 | 44.188 73.695 |
| expression | 20.5084 | 5.826 | 3.520 | 0.002 | 8.355 32.662 |
| Omnibus: | 1.554 | Durbin-Watson: | 1.978 |
| Prob(Omnibus): | 0.460 | Jarque-Bera (JB): | 1.092 |
| Skew: | 0.256 | Prob(JB): | 0.579 |
| Kurtosis: | 2.064 | Cond. No. | 57.0 |
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: | 04:47:32 | 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: | 0.031 |
| Model: | OLS | Adj. R-squared: | -0.015 |
| Method: | Least Squares | F-statistic: | 0.6685 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.423 |
| Time: | 04:47:32 | Log-Likelihood: | -112.74 |
| No. Observations: | 23 | AIC: | 229.5 |
| Df Residuals: | 21 | BIC: | 231.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 26.1811 | 65.860 | 0.398 | 0.695 | -110.783 163.145 |
| expression | 9.5794 | 11.716 | 0.818 | 0.423 | -14.785 33.944 |
| Omnibus: | 3.700 | Durbin-Watson: | 2.595 |
| Prob(Omnibus): | 0.157 | Jarque-Bera (JB): | 1.623 |
| Skew: | 0.275 | Prob(JB): | 0.444 |
| Kurtosis: | 1.821 | Cond. No. | 53.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.061 | 0.323 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.694 |
| Model: | OLS | Adj. R-squared: | 0.610 |
| Method: | Least Squares | F-statistic: | 8.305 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00362 |
| Time: | 04:47:32 | Log-Likelihood: | -66.426 |
| No. Observations: | 15 | AIC: | 140.9 |
| Df Residuals: | 11 | BIC: | 143.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 198.2105 | 136.719 | 1.450 | 0.175 | -102.706 499.127 |
| C(dose)[T.1] | -471.4390 | 195.079 | -2.417 | 0.034 | -900.805 -42.073 |
| expression | -21.1883 | 22.103 | -0.959 | 0.358 | -69.836 27.459 |
| expression:C(dose)[T.1] | 85.0579 | 31.723 | 2.681 | 0.021 | 15.235 154.880 |
| Omnibus: | 4.243 | Durbin-Watson: | 0.988 |
| Prob(Omnibus): | 0.120 | Jarque-Bera (JB): | 1.815 |
| Skew: | 0.747 | Prob(JB): | 0.404 |
| Kurtosis: | 3.819 | Cond. No. | 266. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.494 |
| Model: | OLS | Adj. R-squared: | 0.409 |
| Method: | Least Squares | F-statistic: | 5.847 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0169 |
| Time: | 04:47:32 | Log-Likelihood: | -70.198 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 12 | BIC: | 148.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -56.6474 | 120.987 | -0.468 | 0.648 | -320.256 206.961 |
| C(dose)[T.1] | 50.5728 | 15.146 | 3.339 | 0.006 | 17.572 83.573 |
| expression | 20.1018 | 19.520 | 1.030 | 0.323 | -22.429 62.632 |
| Omnibus: | 2.612 | Durbin-Watson: | 0.739 |
| Prob(Omnibus): | 0.271 | Jarque-Bera (JB): | 1.836 |
| Skew: | -0.830 | Prob(JB): | 0.399 |
| Kurtosis: | 2.573 | Cond. No. | 102. |
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: | 04:47:32 | 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: | 0.023 |
| Model: | OLS | Adj. R-squared: | -0.052 |
| Method: | Least Squares | F-statistic: | 0.3059 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.590 |
| Time: | 04:47:32 | Log-Likelihood: | -75.126 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 5.6130 | 159.519 | 0.035 | 0.972 | -339.006 350.232 |
| expression | 14.3507 | 25.946 | 0.553 | 0.590 | -41.703 70.404 |
| Omnibus: | 0.159 | Durbin-Watson: | 1.768 |
| Prob(Omnibus): | 0.924 | Jarque-Bera (JB): | 0.302 |
| Skew: | -0.193 | Prob(JB): | 0.860 |
| Kurtosis: | 2.422 | Cond. No. | 100. |