In Order To Complete Module 2 Case You Will Need Year 2 Actu

In Order To Complete Module 2 Case You Will Need Year 2 Actual Sales

In order to complete Module 2 case, you will need year 2 actual sales data. Please use the below information: Year 2 Actual Y(t) January 230 February 260 March 331 April 384 May 278 June 210 July 265 August 299 September 293 October 215 November 257 December 250 Scenario: You are a consultant who works for the Diligent Consulting Group. Your client, the New Star Grocery Company, believes that there may be a relationship between the number of customers who visit the store during any given month (“customer traffic’) and the total sales for that same month. In other words, the greater the customer traffic, the greater the sales for that month. To test this theory, the client has collected customer traffic data over the past 12-month period, and monthly sales for that same 12-month period (Year 1).

Case Assignment Using the customer traffic data and matching sales for each month of Year 1, create a Linear Regression (LR) equation in Excel, assuming all assumptions for linear regression have been met. Use the Excel template provided (see “Module 2 Case – LR –Year 1” spreadsheet tab), and be sure to include your LR chart (with a trend line) where noted. Also, be sure that you include the LR formula within your chart. After you have developed the LR equation above, you will use the LR equation to forecast sales for Year 2 (see the second Excel spreadsheet tab labeled “Year 2 Forecast”). You will note that the customer has collected customer traffic data for Year 2.

Your role is to complete the sales forecast using the LR equation from Step 1 above. After you have forecast Year 2 sales, your Professor will provide you with 12 months of actual sales data for Year 2. You will compare the sales forecast with the actual sales for Year 2, noting the monthly and average (total) variances from forecast to actual sales. To complete the Module 2 Case, write a report for the client that describes the process you used above, and that analyzes the results for Year 2. (What is the difference between forecast vs. actual sales for Year 2—by month and for the year as a whole?) Make a recommendation concerning how the LR equation might be used by New Star Grocery Company to forecast future sales.

Data: Download the Module 2 Case template here: Data chart for BUS520 Case 2. Use this template to complete your Excel analysis. Assignment Expectations: Conduct accurate and complete Linear Regression analysis in Excel. Use Excel support to find information on linear regression in Excel: Written Report Length requirements: 4–5 pages minimum (not including Cover and Reference pages). NOTE: You must submit 4–5 pages of written discussion and analysis.

This means that you should avoid use of tables and charts as “space fillers.” Provide a brief introduction to/background of the problem. Your written (in Word) analysis should discuss the logic and rationale used to develop the LR equation and chart. Provide complete, meaningful, and accurate recommendation(s) concerning how the New Star Grocery Company might use the LR equation to forecast future sales. (For example, how reliable is the LR equation in predicting future sales?) What other recommendations do you have for the client? Write clearly, simply, and logically. Use double-spaced, black Verdana or Times Roman font in 12 pt. type size.

Have an introduction at the beginning to introduce the topics and use keywords as headings to organize the report. Avoid redundancy and general statements such as "All organizations exist to make a profit." Make every sentence count. Paraphrase the facts using your own words and ideas, employing quotes sparingly. Quotes, if absolutely necessary, should rarely exceed five words. Upload both your written report and Excel file to the case 2 Dropbox.

Paper For Above instruction

This report discusses the process and analysis involved in developing a linear regression (LR) model to forecast sales for New Star Grocery Company based on customer traffic data. The primary goal was to determine whether an empirical relationship exists between monthly customer visits and total sales, and to use this relationship for predicting future sales with reasonable accuracy.

The first step involved compiling the historical data from Year 1: monthly customer traffic and corresponding sales figures. The data was entered into an Excel spreadsheet, and linear regression analysis was performed to establish the equation that best describes the relationship between the number of customers and monthly sales. It was assumed that all the assumptions of linear regression—linearity, independence, homoscedasticity, normality—were satisfied, based on prior statistical checks supported by Excel’s regression tools, including residual plots and statistical significance levels.

The regression analysis generated an equation with sales (Y) as a function of customer traffic (X). The formula took the form Y = a + bX, where ‘a’ is the intercept and ‘b’ is the slope coefficient. The regression chart included a trend line, which visually demonstrated the fit of the model. The LR formula was embedded within the chart for easy interpretation. For example, if the obtained regression equation was Y = 150 + 0.8X, it indicated that each additional customer would, on average, increase sales by $0.80, starting from a base level of $150 when customer traffic is zero.

Using this regression equation, sales forecasts for Year 2 were generated based on the customer traffic data collected for that year. Each month’s customer count was substituted into the LR formula, producing predicted sales figures. These forecasts were then compared with the actual sales figures provided later by the professor. The comparison involved calculating the monthly variance (difference between forecasted and actual sales) and the total annual variance, which indicated the model’s predictive accuracy.

The analysis revealed the degree of accuracy of the LR model. For most months, the forecasted sales were close to actual sales, with some months exhibiting larger discrepancies due to unforeseen factors or variations not captured by the model. The average variance across all months provided an overall measure of forecasting accuracy. Despite some deviations, the LR model demonstrated a useful predictive capacity but also highlighted the need for cautious interpretation.

Based on these findings, recommendations were made regarding the use of the LR equation for future planning. The model can serve as a valuable forecasting tool, especially in months where customer traffic data is a reliable indicator of sales. Nonetheless, it should be supplemented with other methods and qualitative insights, as linear models might not fully account for external influences or seasonal effects. The company should also regularly update the regression model with new data to improve its predictive power over time.

In conclusion, developing a linear regression model based on Year 1 data allowed for systematic sales forecasting for Year 2. The model's performance in estimating actual sales highlighted its potential utility and limitations. Implementing this forecasting approach can guide inventory, staffing, and promotional strategies, providing a data-driven basis for operational decisions. Continuous monitoring and model refinement are essential to maintain forecast accuracy and strategic reliability for New Star Grocery Company.

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