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 |
| 0.806 | 0.380 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.667 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 12.69 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.77e-05 |
| Time: | 03:59:01 | Log-Likelihood: | -100.46 |
| No. Observations: | 23 | AIC: | 208.9 |
| Df Residuals: | 19 | BIC: | 213.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 117.7620 | 68.579 | 1.717 | 0.102 | -25.775 261.299 |
| C(dose)[T.1] | 11.8742 | 88.283 | 0.135 | 0.894 | -172.905 196.653 |
| expression | -10.7677 | 11.574 | -0.930 | 0.364 | -34.992 13.456 |
| expression:C(dose)[T.1] | 7.2591 | 14.523 | 0.500 | 0.623 | -23.137 37.655 |
| Omnibus: | 0.135 | Durbin-Watson: | 2.274 |
| Prob(Omnibus): | 0.935 | Jarque-Bera (JB): | 0.356 |
| Skew: | 0.034 | Prob(JB): | 0.837 |
| Kurtosis: | 2.395 | Cond. No. | 175. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.663 |
| Model: | OLS | Adj. R-squared: | 0.629 |
| Method: | Least Squares | F-statistic: | 19.64 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.91e-05 |
| Time: | 03:59:01 | Log-Likelihood: | -100.61 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 90.5506 | 40.917 | 2.213 | 0.039 | 5.200 175.902 |
| C(dose)[T.1] | 55.7631 | 9.013 | 6.187 | 0.000 | 36.962 74.564 |
| expression | -6.1573 | 6.859 | -0.898 | 0.380 | -20.465 8.150 |
| Omnibus: | 0.015 | Durbin-Watson: | 2.068 |
| Prob(Omnibus): | 0.993 | Jarque-Bera (JB): | 0.122 |
| Skew: | 0.037 | Prob(JB): | 0.941 |
| Kurtosis: | 2.650 | Cond. No. | 60.2 |
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: | 03:59:01 | 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: | 0.017 |
| Model: | OLS | Adj. R-squared: | -0.030 |
| Method: | Least Squares | F-statistic: | 0.3628 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.553 |
| Time: | 03:59:01 | Log-Likelihood: | -112.91 |
| No. Observations: | 23 | AIC: | 229.8 |
| Df Residuals: | 21 | BIC: | 232.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 39.7269 | 66.775 | 0.595 | 0.558 | -99.139 178.593 |
| expression | 6.5658 | 10.900 | 0.602 | 0.553 | -16.103 29.234 |
| Omnibus: | 2.773 | Durbin-Watson: | 2.385 |
| Prob(Omnibus): | 0.250 | Jarque-Bera (JB): | 1.434 |
| Skew: | 0.274 | Prob(JB): | 0.488 |
| Kurtosis: | 1.906 | Cond. No. | 58.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 7.322 | 0.019 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.673 |
| Model: | OLS | Adj. R-squared: | 0.583 |
| Method: | Least Squares | F-statistic: | 7.532 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00516 |
| Time: | 03:59:01 | Log-Likelihood: | -66.926 |
| No. Observations: | 15 | AIC: | 141.9 |
| Df Residuals: | 11 | BIC: | 144.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -75.8740 | 101.792 | -0.745 | 0.472 | -299.916 148.168 |
| C(dose)[T.1] | -57.5818 | 147.530 | -0.390 | 0.704 | -382.293 267.129 |
| expression | 24.1721 | 17.099 | 1.414 | 0.185 | -13.463 61.807 |
| expression:C(dose)[T.1] | 17.4317 | 24.615 | 0.708 | 0.494 | -36.746 71.609 |
| Omnibus: | 0.402 | Durbin-Watson: | 1.367 |
| Prob(Omnibus): | 0.818 | Jarque-Bera (JB): | 0.449 |
| Skew: | 0.312 | Prob(JB): | 0.799 |
| Kurtosis: | 2.428 | Cond. No. | 190. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.601 |
| Method: | Least Squares | F-statistic: | 11.53 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00161 |
| Time: | 03:59:01 | Log-Likelihood: | -67.261 |
| No. Observations: | 15 | AIC: | 140.5 |
| Df Residuals: | 12 | BIC: | 142.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -125.7410 | 71.962 | -1.747 | 0.106 | -282.533 31.051 |
| C(dose)[T.1] | 46.5054 | 12.444 | 3.737 | 0.003 | 19.393 73.618 |
| expression | 32.5836 | 12.042 | 2.706 | 0.019 | 6.347 58.821 |
| Omnibus: | 0.613 | Durbin-Watson: | 1.557 |
| Prob(Omnibus): | 0.736 | Jarque-Bera (JB): | 0.614 |
| Skew: | 0.185 | Prob(JB): | 0.736 |
| Kurtosis: | 2.081 | Cond. No. | 71.9 |
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: | 03:59:01 | 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: | 0.259 |
| Model: | OLS | Adj. R-squared: | 0.202 |
| Method: | Least Squares | F-statistic: | 4.548 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0526 |
| Time: | 03:59:01 | Log-Likelihood: | -73.050 |
| No. Observations: | 15 | AIC: | 150.1 |
| Df Residuals: | 13 | BIC: | 151.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -122.4194 | 101.697 | -1.204 | 0.250 | -342.122 97.283 |
| expression | 36.1804 | 16.965 | 2.133 | 0.053 | -0.469 72.830 |
| Omnibus: | 1.439 | Durbin-Watson: | 2.026 |
| Prob(Omnibus): | 0.487 | Jarque-Bera (JB): | 1.006 |
| Skew: | 0.361 | Prob(JB): | 0.605 |
| Kurtosis: | 1.956 | Cond. No. | 71.6 |