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 |
| 0.518 | 0.480 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.732 |
| Model: | OLS | Adj. R-squared: | 0.689 |
| Method: | Least Squares | F-statistic: | 17.26 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.18e-05 |
| Time: | 04:34:18 | Log-Likelihood: | -97.980 |
| No. Observations: | 23 | AIC: | 204.0 |
| Df Residuals: | 19 | BIC: | 208.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -53.3117 | 184.165 | -0.289 | 0.775 | -438.773 332.150 |
| C(dose)[T.1] | 830.4554 | 339.785 | 2.444 | 0.024 | 119.277 1541.634 |
| expression | 13.4285 | 22.991 | 0.584 | 0.566 | -34.692 61.549 |
| expression:C(dose)[T.1] | -96.1341 | 42.095 | -2.284 | 0.034 | -184.241 -8.027 |
| Omnibus: | 0.948 | Durbin-Watson: | 1.969 |
| Prob(Omnibus): | 0.623 | Jarque-Bera (JB): | 0.262 |
| Skew: | 0.246 | Prob(JB): | 0.877 |
| Kurtosis: | 3.178 | Cond. No. | 844. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 19.23 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.19e-05 |
| Time: | 04:34:18 | Log-Likelihood: | -100.77 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 176.2931 | 169.784 | 1.038 | 0.312 | -177.870 530.456 |
| C(dose)[T.1] | 54.6990 | 8.863 | 6.172 | 0.000 | 36.211 73.187 |
| expression | -15.2475 | 21.192 | -0.720 | 0.480 | -59.453 28.957 |
| Omnibus: | 1.101 | Durbin-Watson: | 2.000 |
| Prob(Omnibus): | 0.577 | Jarque-Bera (JB): | 0.811 |
| Skew: | 0.022 | Prob(JB): | 0.667 |
| Kurtosis: | 2.081 | Cond. No. | 322. |
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: | 04:34:18 | 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: | 0.006 |
| Model: | OLS | Adj. R-squared: | -0.041 |
| Method: | Least Squares | F-statistic: | 0.1357 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.716 |
| Time: | 04:34:18 | Log-Likelihood: | -113.03 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -22.3757 | 277.258 | -0.081 | 0.936 | -598.965 554.214 |
| expression | 12.6831 | 34.432 | 0.368 | 0.716 | -58.923 84.289 |
| Omnibus: | 2.952 | Durbin-Watson: | 2.426 |
| Prob(Omnibus): | 0.229 | Jarque-Bera (JB): | 1.645 |
| Skew: | 0.374 | Prob(JB): | 0.439 |
| Kurtosis: | 1.924 | Cond. No. | 315. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.069 | 0.322 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.530 |
| Model: | OLS | Adj. R-squared: | 0.402 |
| Method: | Least Squares | F-statistic: | 4.134 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0344 |
| Time: | 04:34:18 | Log-Likelihood: | -69.638 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 11 | BIC: | 150.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 62.3803 | 194.120 | 0.321 | 0.754 | -364.875 489.635 |
| C(dose)[T.1] | 277.5278 | 253.060 | 1.097 | 0.296 | -279.454 834.510 |
| expression | 0.7078 | 27.171 | 0.026 | 0.980 | -59.096 60.512 |
| expression:C(dose)[T.1] | -32.9528 | 35.857 | -0.919 | 0.378 | -111.874 45.968 |
| Omnibus: | 2.090 | Durbin-Watson: | 0.962 |
| Prob(Omnibus): | 0.352 | Jarque-Bera (JB): | 1.327 |
| Skew: | -0.715 | Prob(JB): | 0.515 |
| Kurtosis: | 2.718 | Cond. No. | 329. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.494 |
| Model: | OLS | Adj. R-squared: | 0.410 |
| Method: | Least Squares | F-statistic: | 5.855 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0168 |
| Time: | 04:34:18 | Log-Likelihood: | -70.193 |
| 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 | 197.3442 | 126.120 | 1.565 | 0.144 | -77.448 472.137 |
| C(dose)[T.1] | 45.4077 | 15.521 | 2.926 | 0.013 | 11.591 79.224 |
| expression | -18.2144 | 17.615 | -1.034 | 0.322 | -56.593 20.165 |
| Omnibus: | 2.366 | Durbin-Watson: | 1.047 |
| Prob(Omnibus): | 0.306 | Jarque-Bera (JB): | 1.664 |
| Skew: | -0.786 | Prob(JB): | 0.435 |
| Kurtosis: | 2.565 | Cond. No. | 121. |
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: | 04:34:18 | 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: | 0.133 |
| Model: | OLS | Adj. R-squared: | 0.066 |
| Method: | Least Squares | F-statistic: | 1.992 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.182 |
| Time: | 04:34:19 | Log-Likelihood: | -74.231 |
| No. Observations: | 15 | AIC: | 152.5 |
| Df Residuals: | 13 | BIC: | 153.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 306.9850 | 151.441 | 2.027 | 0.064 | -20.183 634.153 |
| expression | -30.3801 | 21.526 | -1.411 | 0.182 | -76.883 16.123 |
| Omnibus: | 0.217 | Durbin-Watson: | 1.754 |
| Prob(Omnibus): | 0.897 | Jarque-Bera (JB): | 0.403 |
| Skew: | -0.149 | Prob(JB): | 0.818 |
| Kurtosis: | 2.255 | Cond. No. | 115. |