Regression Analysis for Demand Estimation

We conduct an independent sample t-test using Excel, and obtain the following output (see t-test-height)

15 In testing the hypotheses: H0 β1 ’ 0: vs. H1: β 1 ≠ 0 , the following statistics are available: n = 10, b0 = 1.8, b1 = 2.45, and Sb1= 1.20. The value of the test statistic is:

* Statistical significance of the coefficient – This is a statistical test that confirms if the coefficient regardless of its value is robust and different from zero. Also referred to as the P-value.

H_a: μ 50 requires that the z-test for mean be used to test the given hypothesis.

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

Fry Brothers heating and Air Conditioning, Inc. employs Larry Clark and George Murnen to make service calls to repair furnaces and air conditioning units in homes. Tom Fry, the owner, would like to know whether there is a difference in the mean number of service calls they make per day. Assume the population standard deviation for Larry Clark is 1.05 calls per day and 1.23 calls per day for George Murnen. A random sample of 40 days last year showed that Larry Clark made an average of

We conduct an independent sample t-test using Excel, and obtain the following output (see sheet T-TEST)

Economists have created a theory of demand which states the following. Demand curve has a downward slopping which shows the relation between price and quantity while all other factors are equal. At higher prices the demand will decrease, while at lower prices demand will increase.

A field researcher is gathering data on the trunk diameters of mature pine and spruce trees in a certain area. The following are the results of his random sampling. Can he conclude, at the .10 level of significance, that

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

Normal products are categorized as products whose value is lower than prestigious products, they both assume a role in the market as they are targeted to different market segments. Demand curves differ as both products may be perceived differently, typically normal products are perceived as lower quality contrary to prestigious products whose customers perceive them as higher quality products. Ultimately, they differ in the perception of the customer as each product is categorized according to their price, and higher priced products may be perceived as better. An example could be represented by two different vehicle manufacturers; Honda, and BMW, Honda is a low priced vehicle known for reliability fuel efficiency, etc.… BMW are known for their luxury, and price. Here their demand may differ as they both have different target markets. Perhaps if Honda decided to raise their price, they would more than likely lose profit, but if BMW was to lower their price, their demand would potentially increase. These changes are known as demand shifts here a firm identifies the traits of each shift, and can identify the relationship in demand. Moreover, marketers can estimate their product demand. This is done by one;

In this case, level of significance, α was not provided. Therefore, the analysis will be evaluated based on two α values which are:

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.

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).