To begin, you will need to obtain the data on gasoline demand and price from St. Louis Fed for the period 1991-2021. Once you have the data, you can perform a regression analysis using a statistical software package such as R or Python. The regression model you will need to estimate is as follows: log(gasoline demand) = b0 + b1log(income) + b2log(price) + b3*time + e where b0 is the intercept or constant, b1 is the coefficient for log(income), b2 is the coefficient for log(price), b3 is the coefficient for time, and e is the error term. You can then estimate the coefficients using the ordinary least squares (OLS) method. The estimated coefficients will give you information about the relationships between the variables in the model. To create the table you requested, you can use the summary output from the regression analysis. The table should include the estimated coefficients for log(income), log(price), constant, time, adjusted R-squared, standard error of the regression, and F-statistic. The adjusted R-squared is a measure of the goodness of fit of the regression model, while the standard error of the regression is a measure of the variability of the error term. Keep in mind that the results of the regression analysis will depend on the quality and reliability of the data, as well as the assumptions and limitations of the regression model. Therefore, it is important to carefully evaluate the results and interpret them in the context of the research question and the available evidence.