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 |
1.583 | 0.223 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.675 |
Model: | OLS | Adj. R-squared: | 0.624 |
Method: | Least Squares | F-statistic: | 13.15 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.01e-05 |
Time: | 04:27:11 | Log-Likelihood: | -100.18 |
No. Observations: | 23 | AIC: | 208.4 |
Df Residuals: | 19 | BIC: | 212.9 |
Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -426.5286 | 507.540 | -0.840 | 0.411 | -1488.822 635.765 |
C(dose)[T.1] | -30.5006 | 880.557 | -0.035 | 0.973 | -1873.527 1812.526 |
expression | 40.5342 | 42.791 | 0.947 | 0.355 | -49.029 130.097 |
expression:C(dose)[T.1] | 6.6016 | 73.756 | 0.090 | 0.930 | -147.772 160.975 |
Omnibus: | 1.371 | Durbin-Watson: | 1.887 |
Prob(Omnibus): | 0.504 | Jarque-Bera (JB): | 0.925 |
Skew: | 0.118 | Prob(JB): | 0.630 |
Kurtosis: | 2.046 | Cond. No. | 2.96e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.675 |
Model: | OLS | Adj. R-squared: | 0.642 |
Method: | Least Squares | F-statistic: | 20.75 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.32e-05 |
Time: | 04:27:11 | Log-Likelihood: | -100.19 |
No. Observations: | 23 | AIC: | 206.4 |
Df Residuals: | 20 | BIC: | 209.8 |
Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -452.8826 | 403.021 | -1.124 | 0.274 | -1293.569 387.804 |
C(dose)[T.1] | 48.3098 | 9.340 | 5.173 | 0.000 | 28.828 67.792 |
expression | 42.7563 | 33.978 | 1.258 | 0.223 | -28.120 113.633 |
Omnibus: | 1.371 | Durbin-Watson: | 1.870 |
Prob(Omnibus): | 0.504 | Jarque-Bera (JB): | 0.920 |
Skew: | 0.108 | Prob(JB): | 0.631 |
Kurtosis: | 2.044 | Cond. No. | 1.15e+03 |
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: | 04:27:11 | 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: | 0.240 |
Model: | OLS | Adj. R-squared: | 0.204 |
Method: | Least Squares | F-statistic: | 6.623 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0177 |
Time: | 04:27:11 | Log-Likelihood: | -109.95 |
No. Observations: | 23 | AIC: | 223.9 |
Df Residuals: | 21 | BIC: | 226.2 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -1325.6498 | 546.117 | -2.427 | 0.024 | -2461.363 -189.937 |
expression | 117.9370 | 45.827 | 2.574 | 0.018 | 22.635 213.239 |
Omnibus: | 1.972 | Durbin-Watson: | 2.338 |
Prob(Omnibus): | 0.373 | Jarque-Bera (JB): | 1.275 |
Skew: | 0.303 | Prob(JB): | 0.529 |
Kurtosis: | 2.018 | Cond. No. | 1.04e+03 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
F-statistic | p-value | df difference |
0.003 | 0.956 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.482 |
Model: | OLS | Adj. R-squared: | 0.340 |
Method: | Least Squares | F-statistic: | 3.406 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0569 |
Time: | 04:27:11 | Log-Likelihood: | -70.373 |
No. Observations: | 15 | AIC: | 148.7 |
Df Residuals: | 11 | BIC: | 151.6 |
Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -979.5827 | 1535.402 | -0.638 | 0.537 | -4358.979 2399.814 |
C(dose)[T.1] | 1706.8321 | 1992.352 | 0.857 | 0.410 | -2678.306 6091.970 |
expression | 89.7705 | 131.641 | 0.682 | 0.509 | -199.970 379.511 |
expression:C(dose)[T.1] | -141.0504 | 169.403 | -0.833 | 0.423 | -513.904 231.803 |
Omnibus: | 3.502 | Durbin-Watson: | 0.993 |
Prob(Omnibus): | 0.174 | Jarque-Bera (JB): | 2.072 |
Skew: | -0.910 | Prob(JB): | 0.355 |
Kurtosis: | 2.974 | Cond. No. | 4.14e+03 |
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: | 4.888 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0280 |
Time: | 04:27:11 | Log-Likelihood: | -70.831 |
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 | 13.8399 | 953.978 | 0.015 | 0.989 | -2064.701 2092.380 |
C(dose)[T.1] | 48.0731 | 25.446 | 1.889 | 0.083 | -7.368 103.514 |
expression | 4.5947 | 81.788 | 0.056 | 0.956 | -173.606 182.795 |
Omnibus: | 2.668 | Durbin-Watson: | 0.824 |
Prob(Omnibus): | 0.263 | Jarque-Bera (JB): | 1.861 |
Skew: | -0.838 | Prob(JB): | 0.394 |
Kurtosis: | 2.590 | Cond. No. | 1.45e+03 |
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: | 04:27:11 | 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: | 0.285 |
Model: | OLS | Adj. R-squared: | 0.230 |
Method: | Least Squares | F-statistic: | 5.182 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0404 |
Time: | 04:27:11 | Log-Likelihood: | -72.784 |
No. Observations: | 15 | AIC: | 149.6 |
Df Residuals: | 13 | BIC: | 151.0 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -1392.5024 | 652.919 | -2.133 | 0.053 | -2803.048 18.043 |
expression | 126.0150 | 55.357 | 2.276 | 0.040 | 6.423 245.607 |
Omnibus: | 1.329 | Durbin-Watson: | 1.475 |
Prob(Omnibus): | 0.515 | Jarque-Bera (JB): | 0.796 |
Skew: | 0.032 | Prob(JB): | 0.672 |
Kurtosis: | 1.873 | Cond. No. | 903. |