Sears Business Model

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Based on pattern recognition (see Figure 1), it seems that every fourth quarter in each year has the highest sales revenue, and there appears to have a decreasing trend. To confirm this, we will create different statistical models, first using the three dummy variables and then incorporating external conditions such as CPI and disposal income. Lastly, we will use the best fit model to develop our expectation for 2015’s revenue.

From the above regression statistics (see Figure 2), since adjusted R^2 = 0.05, significant low, there is no association between the years and sales according to the simple regression model because only 5% of the variation in revenue can be explained by the quarters. Therefore, it’s unlikely that trend is an accurate …show more content…

The first one that we choose is S&P 500 because we want to see if there is a direct relationship between the stock market and consumers’ purchasing behaviors. Since S&P 500 is a American stock market index based on the market capitalizations of 500 large companies, we choose it as an economic variable to see if it will influence buyer behaviors in Sears’ revenue.We use Fred Economic Data as our primary resources to find the S&P 500 index , and we choose the median among the data to represent our quarterly data. Based on the regression output above ( See Figure 4), although the overall model is useful with F=0, the p value for S&P 500 is 35% greater than α=0.05(assumed). Thus, adding the S&P 500 economic factor does not provide a better regression model in comparison to the dummy variable regression model we present …show more content…

Since they only provide monthly index, we take the median among the monthly data to represent our quarterly data. We are expecting a positive correlated relationship between disposable income and the sales revenue because when people have more disposable income, they are more likely to increase spending or vice versa. There is a economic theory called “ marginal propensity to consume”, which means that the proportion of an aggregate raise in pay that a consumer spends on the consumption of goods and services, as opposed to saving it. Therefore, from an economist's perspective, there is an association among the consumption and income. Refer back to the regression analysis (See Figure 6), the new model’s adjusted R square is 99% which means 99% of the variation in revenue can be explained by variables including trend, spring quarter, fall quarter, winter quarter, and disposable income. By looking at the coefficient of disposable income, and it indicates that for every additional in disposable income; one can expect the sales revenue to decrease an average of 1.37 dollars with holding other predictors constant. Therefore, according to the new model, there is actually a negative relationship between the disposable income and sales’ revenue. The overall model is significant with F=0. Since the p values are all less than α=0.05(assumed), and the adjusted R^2

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