Linear Regression ( LR ) Equation In Excel

a. This particular industry has a constantly increasing cost. There will be an increase in the demand for input factors for one key reason. Every day, new companies will be introduced into this market of remodeling, economic profits being the encouraging factor. Because of this, there will be a bid up on input prices for the companies in the industry of remodeling. “When a market is characterized by a large number of small producers, the demand curve facing the manager of each individual firm is horizontal at the price determined by the

The case involves the decision to locate a new store at one of two candidate sites. The decision will be based on estimates of sales potential, and for this purpose, you will need to develop a multiple regression model to predict sales. Specific case questions are given in the textbook, and the necessary data is in the file named pamsue.xls.

Seven models were created in order to obtain the model in which all-remaining variables are statistically significant and the final equation to predict sales is as follow:

Thank you for the opportunity to assess your sales data in order to provide recommendations for increasing your sales. The analysis and recommendations below are based on the data you provided, which covers a period from May 2004 through June 2006. The analysis below is based on this data alone. Therefore, our recommendations should be tempered by your knowledge of business realities and your market. Please let us know if we can answer any questions concerning the analysis or the recommendations provided.

4) Use exponential regression (or find a constant ratio) to determine an equation for the data.

* Our company’s sales forecast has been based on performance from previous years along with market circumstances. We are looking at the future of the business objectively which we then can evaluate past to

Greaves provided five years and two months of annual sales data. Using Stat Tools, the following analysis were run: Moving Average, Exponential Smoothing Simple, Exponential Smoothing Holt’s, and Exponential Smoothing Winter’s. Following a comparison on the average on all models, the Exponential Smoothing Winter’s was found to be the most suitable model for the case. A graph analysis is captured below.

In an attempt to improve this model, we attempt to do a multiple regression model predicting SALES based on CALLS, TIME, and YEARS.

In this particular case, Randy will need to assign the correct numbers to the correct category. For the purposes of this case study, assume T will equal 1 to make the equation represent one year of employment in one of the ice cream shops. For following variables, Nn will equal 50 as there will be 50 applicants total selected to be hired, rxy will represent .30 in one equation representing the interview and job performance and in the other equation, it will represent .50 which will represent the work sample predictor and job performance, SDy will be chosen to represent .20, Ẑs will be .80 because it will be the predictor score of the selected applicants, Na will represent 100, as that is the total number of applicants that submitted applications, and Cy will represent the cost per applicant in the interview and job performance in one equation as 100 and it will represent 150 in the other equation for work sample and job

In this specific Case, that has asked the Sale growth for the four-year period, can be calculated ‎as bellow;‎

3. Refer to the monthly sales forecasts given in the first Table. Assume that these amounts are realized and that the firm’s customers pay exactly as predicted.

As previously mentioned, no analysis is given for the regression model, so we shall at least attempt to ask the appropriate questions needed to make an intelligent decision. We begin by looking at the structure of the regression model prior to performing the diagnostic procedures. First, we must understand why the consultants chose to model the revenue in the markdown period as a product of the observed performance in the regular period, the PL effect, Department effect, the promotions effect, and a constant. If there are no PL effects, department effects, or promotions effects, then the revenue is simply a function of the overserved performance in the regular price period. This is logical, as adding in an “effect” simply scales the revenue.

The sales forecast is based on projected levels of demand. All the numbers are expressed

During performing the sales forecast for Victoria's Secret, I learned that for most part that Victoria's Secret has an incline in their profits. They have however hit a few bumps here and there. The causes of this could be more cost for Victoria's Secret purchasing materials and production of their products. Another reason for this could also be a slower rate in sales than usual. Like I said, for the most Victoria Secret has seen an incline in their profits and sales throughout the years. Performing the percentage of sales forecast for Victoria's Secret, I established a forecasted sales of 5 percent which means that they would have to have a sales of $2,808 compared to their last years $2,675. This is a very feasible number for Victoria Secret to achieve, considering that majority of their money in assets outweighs their liabilities.

As a result, this paper makes an assumption that only data from wholesalers 1 and 2 will fit the model best, in terms of finding the correct diamond for the professor.