Case 1: Forecasting
In this section, the paper focuses on analyzing historical data with a view of forecasting expected monthly sales. The case requires monthly sales to be projected, given the assumption that the actual monthly sales are correlated with the number of hits on the company’s website in the previous month. Consequently, the historical data on actual sales and number of hits are both used to forecast the expected sales over a period of three months. Notably, forecasting entails application of simple linear regression in order to understand the underlying relationship between sales volumes and number of hits on the company’s website. In this case, an Excel worksheet is used to analyze the data, and to forecast the sales based on the data collected on the number of hits.
Chase (2013) explains that forecasting in business scenarios entails …show more content…
In this case, linear regression traces the correlation between two sets of data, where one is assigned as the dependent variable, and the other is the independent variable. In most cases, linear regression analysis is used to assess the causal relationship between the two data sets. To this end, it is assumed that the underlying relationship causes the dependent variable to change in case the independent variable changes (Felli & Hazen (2015). Consequently, the underlying relationship can be used to forecast the extent to which the dependent variable changes on varying the independent variable. Additionally, the two data sets can be plotted on a scatter graph using corresponding data for each set. In this case, the independent variable is plotted on the x-axis, while the dependent variable is plotted on the y-axis. A trend line can then be used to forecast corresponding values for the dependent variable, given that the corresponding data for independent variables can be
In this section, the paper focuses on analyzing historical data with a view of forecasting expected monthly sales. The case requires monthly sales to be projected, given the assumption that the actual monthly sales are correlated with the number of hits on the company’s website in the previous month. Consequently, the historical data on actual sales and number of hits are both used to forecast the expected sales over a period of three months. Notably, forecasting entails application of simple linear regression in order to understand the underlying relationship between sales volumes and number of hits on the company’s website. In this case, an Excel worksheet is used to analyze the data, and to forecast the sales based on the data collected on the number of hits.
Chase (2013) explains that forecasting in business scenarios entails …show more content…
In this case, linear regression traces the correlation between two sets of data, where one is assigned as the dependent variable, and the other is the independent variable. In most cases, linear regression analysis is used to assess the causal relationship between the two data sets. To this end, it is assumed that the underlying relationship causes the dependent variable to change in case the independent variable changes (Felli & Hazen (2015). Consequently, the underlying relationship can be used to forecast the extent to which the dependent variable changes on varying the independent variable. Additionally, the two data sets can be plotted on a scatter graph using corresponding data for each set. In this case, the independent variable is plotted on the x-axis, while the dependent variable is plotted on the y-axis. A trend line can then be used to forecast corresponding values for the dependent variable, given that the corresponding data for independent variables can be