Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 0.774090556 0.390319556 1.983222579 0.053752401 -0.013063847 1.561244959 -0.013063847 1.561244959 5.8 0.440116855 0.033569302 13.11069412 1.27553E-16 0.37

The data you provided includes statistical results for a regression analysis, specifically for an intercept and a coefficient. To interpret these values: ### Intercept - **Coefficient**: 0.7741 - This represents the expected value of the dependent variable when all independent variables are zero. In practical terms, if the independent variable(s) were held at zero, the model predicts a value of approximately 0.7741 for the dependent variable. - **Standard Error**: 0.3903 - This value measures the average distance that the observed values fall from the regression line. A smaller standard error indicates a more precise estimate of the coefficient. - **t Stat**: 1.9832 - The t-statistic is used to determine whether the coefficient is significantly different from zero. It is calculated as the coefficient divided by its standard error. - **P-value**: 0.0538 - The P-value indicates the probability of observing the data, or something more extreme, if the null hypothesis (which typically states that the coefficient is equal to zero) is true. A P-value just below 0.05 suggests a marginal significance; it indicates that there may be evidence to reject the null hypothesis at the 5% significance level, but it is very close to the threshold. - **Confidence Intervals**: - Lower 95%: -0.0131 - Upper 95%: 1.5612 - With a 95% confidence level, we can say that we are 95% confident that the true value of the intercept lies between -0.0131 and 1.5612. ### Coefficient (for the variable with coefficient 5.8) - **Coefficient**: 5.8 - This coefficient represents the expected change in the dependent variable for a one-unit increase in the independent variable associated with this coefficient, assuming all other variables in the model are held constant. - **Standard Error**: 0.4401 - Again, this measures the level of uncertainty associated with the coefficient estimate. - **t Stat**: 13.1107 - This indicates that the coefficient is very statistically significant since a t-stat greater than 2 (or -2) typically indicates a significant relationship. - **P-value**: 1.27553E-16 - This extremely low P-value suggests that we can reject the null hypothesis with a very high level of confidence, meaning there is a very significant effect of this independent variable on the dependent variable. - **Confidence Intervals**: - Lower 95%: 0.3724 - Upper 95%: 0.5078 - We can say with 95% confidence that the true effect of this coefficient lies within this interval. ### Summary In summary, the intercept and the significant coefficient indicate that the modeled dependent variable has a baseline value of approximately 0.7741 when the independent variables are zero, and the specific independent variable associated with a coefficient of 5.8 substantially increases the dependent variable's value when increased by one unit. The low P-values and tight confidence intervals support the reliability of these estimates. Update (2024-08-20): The "Age" coefficient row shows a coefficient and standard error of 0, which is unusual and implies that the variable "Age" may not have been included in the model properly, or that it had no variance in the dataset used for the regression analysis. Here are a few possible explanations for the all-zeros output: 1. **Multicollinearity**: If "Age" is perfectly multicollinear with another variable in the model (e.g., another variable that captures the same information), the regression algorithm may drop it from the model or assign it a coefficient of zero. 2. **No Variability**: If all observations in the dataset for "Age" are the same (e.g., all participants are the same age or missing values), then the model cannot estimate the effect of "Age" on the dependent variable, resulting in a coefficient of zero. 3. **Data Entry Issue**: There might be a data entry error where "Age" data were not entered or were entered incorrectly, leading to an absence of meaningful information to base a coefficient on. 4. **Dummy Variable**: If "Age" was intended to be used as a categorical variable represented by dummy variables, perhaps the original continuous variable was erroneously replaced or omitted. 5. **Model Specification**: It's possible that "Age" was not significant in preliminary analysis or model iterations, leading to it being excluded or assigned a coefficient of zero in the final model. In any case, it would be important to investigate the "Age" variable in the context of your dataset to understand why it is being reported as having no effect on the outcome.