ged one period, moving average three periods, or moving average three periods with durable goods lagged one period) provides the strongest result in the context of the correlation coefficient.
Simple regression analysis involves the analysis of the relationship between one dependent variable and one explanatory, or independent, variable. The statistical concepts of both regression and correlation are valuable tools. Regression coefficients permit the projection of movements in one variable based on movement in another variable or in a set of other variables. Correlation coefficients establish both the strength of relationships between variables, and the nature of such relationships positive or negative. What a simple correlation coefficient does not do, however, is to establish a causal relationship between the variables. The simple regression formula is y = a + bx. In this equation, y is the dependent variable value for a time period, a is the estimated variable value for time zero (the constant), b is the change in the variable value per time unit, and x is the time unit.
The moving average projection generates a forecast for the next time period by averaging the relevant data values for a predetermined number of prior time periods. This procedure, thus, creates a so called moving average, by which forecasts for variable values in future time periods are based only on relatively recent data, as opposed to past data which may have little
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