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 |
| 0.262 | 0.614 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.599 |
| Method: | Least Squares | F-statistic: | 11.95 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000127 |
| Time: | 03:32:17 | Log-Likelihood: | -100.91 |
| No. Observations: | 23 | AIC: | 209.8 |
| Df Residuals: | 19 | BIC: | 214.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -18.9037 | 218.086 | -0.087 | 0.932 | -475.363 437.556 |
| C(dose)[T.1] | 54.9312 | 291.732 | 0.188 | 0.853 | -555.672 665.534 |
| expression | 9.1994 | 27.430 | 0.335 | 0.741 | -48.212 66.611 |
| expression:C(dose)[T.1] | -0.2743 | 36.558 | -0.008 | 0.994 | -76.791 76.243 |
| Omnibus: | 0.973 | Durbin-Watson: | 1.767 |
| Prob(Omnibus): | 0.615 | Jarque-Bera (JB): | 0.806 |
| Skew: | 0.147 | Prob(JB): | 0.668 |
| Kurtosis: | 2.131 | Cond. No. | 706. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 18.87 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.49e-05 |
| Time: | 03:32:17 | Log-Likelihood: | -100.91 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -17.6765 | 140.596 | -0.126 | 0.901 | -310.954 275.601 |
| C(dose)[T.1] | 52.7433 | 8.790 | 6.000 | 0.000 | 34.408 71.079 |
| expression | 9.0450 | 17.674 | 0.512 | 0.614 | -27.823 45.913 |
| Omnibus: | 0.979 | Durbin-Watson: | 1.769 |
| Prob(Omnibus): | 0.613 | Jarque-Bera (JB): | 0.809 |
| Skew: | 0.149 | Prob(JB): | 0.667 |
| Kurtosis: | 2.131 | Cond. No. | 262. |
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: | 03:32:17 | 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: | 0.030 |
| Model: | OLS | Adj. R-squared: | -0.016 |
| Method: | Least Squares | F-statistic: | 0.6488 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.430 |
| Time: | 03:32:17 | Log-Likelihood: | -112.75 |
| 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 | -104.1531 | 228.393 | -0.456 | 0.653 | -579.123 370.817 |
| expression | 23.0448 | 28.611 | 0.805 | 0.430 | -36.455 82.545 |
| Omnibus: | 1.983 | Durbin-Watson: | 2.447 |
| Prob(Omnibus): | 0.371 | Jarque-Bera (JB): | 1.345 |
| Skew: | 0.348 | Prob(JB): | 0.510 |
| Kurtosis: | 2.042 | Cond. No. | 261. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.016 | 0.903 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.450 |
| Model: | OLS | Adj. R-squared: | 0.299 |
| Method: | Least Squares | F-statistic: | 2.994 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0772 |
| Time: | 03:32:17 | Log-Likelihood: | -70.822 |
| No. Observations: | 15 | AIC: | 149.6 |
| Df Residuals: | 11 | BIC: | 152.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 87.0318 | 350.199 | 0.249 | 0.808 | -683.750 857.814 |
| C(dose)[T.1] | 66.6399 | 484.060 | 0.138 | 0.893 | -998.768 1132.048 |
| expression | -2.3644 | 42.213 | -0.056 | 0.956 | -95.275 90.547 |
| expression:C(dose)[T.1] | -2.0143 | 57.795 | -0.035 | 0.973 | -129.220 125.191 |
| Omnibus: | 3.159 | Durbin-Watson: | 0.798 |
| Prob(Omnibus): | 0.206 | Jarque-Bera (JB): | 2.061 |
| Skew: | -0.900 | Prob(JB): | 0.357 |
| Kurtosis: | 2.756 | Cond. No. | 678. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.899 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0278 |
| Time: | 03:32:17 | Log-Likelihood: | -70.823 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 95.9411 | 229.176 | 0.419 | 0.683 | -403.390 595.272 |
| C(dose)[T.1] | 49.7801 | 16.413 | 3.033 | 0.010 | 14.020 85.540 |
| expression | -3.4390 | 27.607 | -0.125 | 0.903 | -63.589 56.711 |
| Omnibus: | 3.019 | Durbin-Watson: | 0.796 |
| Prob(Omnibus): | 0.221 | Jarque-Bera (JB): | 1.992 |
| Skew: | -0.882 | Prob(JB): | 0.369 |
| Kurtosis: | 2.722 | Cond. No. | 249. |
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: | 03:32:17 | 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: | 0.027 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.3669 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.555 |
| Time: | 03:32:17 | Log-Likelihood: | -75.091 |
| No. Observations: | 15 | AIC: | 154.2 |
| Df Residuals: | 13 | BIC: | 155.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -77.8652 | 283.361 | -0.275 | 0.788 | -690.029 534.298 |
| expression | 20.4654 | 33.786 | 0.606 | 0.555 | -52.526 93.457 |
| Omnibus: | 0.180 | Durbin-Watson: | 1.477 |
| Prob(Omnibus): | 0.914 | Jarque-Bera (JB): | 0.379 |
| Skew: | 0.120 | Prob(JB): | 0.827 |
| Kurtosis: | 2.259 | Cond. No. | 241. |