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 |
0.475 | 0.499 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.659 |
Model: | OLS | Adj. R-squared: | 0.605 |
Method: | Least Squares | F-statistic: | 12.22 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000110 |
Time: | 03:34:36 | Log-Likelihood: | -100.74 |
No. Observations: | 23 | AIC: | 209.5 |
Df Residuals: | 19 | BIC: | 214.0 |
Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 63.9715 | 65.246 | 0.980 | 0.339 | -72.590 200.533 |
C(dose)[T.1] | 74.2259 | 78.267 | 0.948 | 0.355 | -89.589 238.041 |
expression | -1.8826 | 12.526 | -0.150 | 0.882 | -28.099 24.334 |
expression:C(dose)[T.1] | -4.4132 | 15.297 | -0.289 | 0.776 | -36.430 27.604 |
Omnibus: | 2.020 | Durbin-Watson: | 1.903 |
Prob(Omnibus): | 0.364 | Jarque-Bera (JB): | 1.170 |
Skew: | 0.203 | Prob(JB): | 0.557 |
Kurtosis: | 1.973 | Cond. No. | 130. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.657 |
Model: | OLS | Adj. R-squared: | 0.623 |
Method: | Least Squares | F-statistic: | 19.17 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.24e-05 |
Time: | 03:34:36 | Log-Likelihood: | -100.79 |
No. Observations: | 23 | AIC: | 207.6 |
Df Residuals: | 20 | BIC: | 211.0 |
Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 79.3165 | 36.912 | 2.149 | 0.044 | 2.319 156.314 |
C(dose)[T.1] | 51.8010 | 8.949 | 5.788 | 0.000 | 33.133 70.469 |
expression | -4.8417 | 7.023 | -0.689 | 0.499 | -19.492 9.809 |
Omnibus: | 1.574 | Durbin-Watson: | 1.907 |
Prob(Omnibus): | 0.455 | Jarque-Bera (JB): | 1.044 |
Skew: | 0.202 | Prob(JB): | 0.593 |
Kurtosis: | 2.037 | Cond. No. | 45.2 |
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: | 03:34:37 | 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: | 0.083 |
Model: | OLS | Adj. R-squared: | 0.039 |
Method: | Least Squares | F-statistic: | 1.899 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.183 |
Time: | 03:34:37 | Log-Likelihood: | -112.11 |
No. Observations: | 23 | AIC: | 228.2 |
Df Residuals: | 21 | BIC: | 230.5 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 155.0483 | 55.094 | 2.814 | 0.010 | 40.475 269.622 |
expression | -14.9641 | 10.858 | -1.378 | 0.183 | -37.544 7.616 |
Omnibus: | 2.586 | Durbin-Watson: | 2.430 |
Prob(Omnibus): | 0.274 | Jarque-Bera (JB): | 1.412 |
Skew: | 0.288 | Prob(JB): | 0.494 |
Kurtosis: | 1.931 | Cond. No. | 42.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
F-statistic | p-value | df difference |
0.060 | 0.811 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.463 |
Model: | OLS | Adj. R-squared: | 0.317 |
Method: | Least Squares | F-statistic: | 3.166 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0678 |
Time: | 03:34:37 | Log-Likelihood: | -70.632 |
No. Observations: | 15 | AIC: | 149.3 |
Df Residuals: | 11 | BIC: | 152.1 |
Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 165.0551 | 182.792 | 0.903 | 0.386 | -237.268 567.378 |
C(dose)[T.1] | -65.6852 | 239.176 | -0.275 | 0.789 | -592.108 460.738 |
expression | -17.6019 | 32.888 | -0.535 | 0.603 | -89.988 54.784 |
expression:C(dose)[T.1] | 20.4243 | 41.411 | 0.493 | 0.632 | -70.721 111.570 |
Omnibus: | 2.205 | Durbin-Watson: | 0.639 |
Prob(Omnibus): | 0.332 | Jarque-Bera (JB): | 1.577 |
Skew: | -0.759 | Prob(JB): | 0.455 |
Kurtosis: | 2.532 | Cond. No. | 252. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.451 |
Model: | OLS | Adj. R-squared: | 0.360 |
Method: | Least Squares | F-statistic: | 4.939 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0272 |
Time: | 03:34:37 | Log-Likelihood: | -70.796 |
No. Observations: | 15 | AIC: | 147.6 |
Df Residuals: | 12 | BIC: | 149.7 |
Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 93.6070 | 107.906 | 0.867 | 0.403 | -141.499 328.713 |
C(dose)[T.1] | 51.8737 | 19.155 | 2.708 | 0.019 | 10.138 93.609 |
expression | -4.7199 | 19.345 | -0.244 | 0.811 | -46.869 37.429 |
Omnibus: | 2.406 | Durbin-Watson: | 0.787 |
Prob(Omnibus): | 0.300 | Jarque-Bera (JB): | 1.704 |
Skew: | -0.795 | Prob(JB): | 0.427 |
Kurtosis: | 2.553 | Cond. No. | 84.0 |
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: | 03:34:37 | 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: | 0.116 |
Model: | OLS | Adj. R-squared: | 0.048 |
Method: | Least Squares | F-statistic: | 1.711 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.214 |
Time: | 03:34:37 | Log-Likelihood: | -74.373 |
No. Observations: | 15 | AIC: | 152.7 |
Df Residuals: | 13 | BIC: | 154.2 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -54.2537 | 113.503 | -0.478 | 0.641 | -299.462 190.955 |
expression | 25.2904 | 19.337 | 1.308 | 0.214 | -16.485 67.066 |
Omnibus: | 0.309 | Durbin-Watson: | 1.368 |
Prob(Omnibus): | 0.857 | Jarque-Bera (JB): | 0.119 |
Skew: | -0.184 | Prob(JB): | 0.942 |
Kurtosis: | 2.766 | Cond. No. | 71.8 |