Regression Analysis for Demand Estimation

For d3, t-statistic= 8.8773, t-statistic > t-critical. Thus we reject Ho and d3 is significant.

* Customer demand: Rather than basing on history and forecast sale of its products, the company should pay more attention in analyzing some uncontrollable factors such as inflation, recession, and currency exchange rate which may affect customers’ buying behavior.

Demand can either decrease or increase based on price of a product or service. Consumers have a tendency to buy products when there is a decrease in price. Companies have to kick off discounts to the consumers to increase demand. Pricing strategies for consumers are to buy when prices are low, although companies have to change prices to increase and decrease demand when needed. The simulation showed the same effect from the property management company. When supply was low of apartments the company had to increase price to decrease demand. When supply was too high the company had to decrease price to increase demand. The price elasticity of demand is flexible in which it can be changed and in return have an immediate effect. However, this can be harmful for

We first predict the annual demand for the year 1972 based on trend for 4 months of 1972 based on corresponding months of 1971.

* The F-value suggests that there is a significant difference between the results of the control and treatment groups. The P-value of 0.005 is < the alpha of 0.05. This suggest that the groups are significantly different and the null hypothesis should be rejected.

Demand refers to the quantity of products people are willing and able to purchase during some specific time period, all other relevant factors being held constant. Price and quantity demanded stand in a negative (inverse) relationship: as price rises, consumers buy fewer units; and as price falls, consumers buy more units (Stone 75).

To summarize the concept, when the price of a product falls, the quantity demanded of the product will increase, and conversely, when the price of a product increases, the quantity demanded of the product will decrease, where all other relevant factors are constant. (Glen, 2012).

The purpose of the assignment is to review basic hypothesis testing and regression techniques. There is an appendix in your textbook, Appendix C: Using Excel to Conduct Analysis, which may help you with running regressions in Microsoft Excel. You may also wish to use a basic statistics text for guidance if needed. I have also provided you with a table with the t distribution.

However, it is also essential for the economists to understand how demand and price relationship, in terms of quantity, varies from product to product. When we systematically scrutinize different products and services, it becomes apparent to us that the quantity demanded,

We first predict the annual demand for the year 1972 based on trend for 4 months of 1972 based on corresponding months of 1971.

1.Detail and discuss alt the challenges you faced in projecting demand: meeting customer needs and wants, pricing, competitive actions and competitive response. How did your decisions impact your end performance (market share, income statement)?

Before, the concept of demand forecast was to serve the key functional groups in achieving their own interest. Facing the new challenges, forecast needed to be more accurate. And therefore it needed a new concept that is to have a consensus forecasting that would accurately reveal market demand and align the needs of key actors in the forecasting process. Leitax implemented two specific changes in forecasting process. The first one is to switch the focus from sell-in to sell-through and second one is to ignore capacity constraints.

A set of graphs shows the relationship between demand and total revenue (TR) for a linear demand curve. As price decreases in the elastic range, TR increases, but in the inelastic range, TR decreases. TR is maximized at the quantity where PED = 1.

Demand is the relationship between price and quantity demanded for a particular good and service in particular circumstances. For each price the demand relationship tells the quantity the buyers want to buy at that corresponding price. The quantity the buyers want to buy at a particular price is called the Quantity Demanded.

Forecasting demand is the art and science of predicting future demand. There are several different techniques that can be employed alone or in combination with each other, depending upon the firm’s particular situation and the point in the product’s life cycle, and they are further classified as to the time horizon they represent. Forecasts are generally quantitative (relying on historical data) or qualitative (such as variable personal experiences).