Researchers frequently work in teams to help minimize the ri

Researchers frequently work in teams to help minimize the risk of bias. This econometric model will test the realtor’s theory that location is the most important factor in determining the value of a home. Simply stated this theory is “location, location, location.” To test this hypothesis, include variables that estimate the value of the home by the home’s characteristics (e.g., number of rooms or bathrooms) and the neighborhood’s characteristics (e.g., noise, traffic, crime). There is no one correctly specified model that you will be evaluated on, but you should use this discussion to justify which variables are include in your model and evaluate the overall fit of the econometric model to the simple theory put forth by the real estate industry.

H0: The type of home (e.g., single family, multi-family, condo) is an important factor when it comes to purchasing a home

H1: They type of home (e.g., single family, multi-family, condo) is not an important factor when it comes to purchasing a home

What would be some examples of regression analysis results from the above mentioned hypotheses?

Solution

Some Examples

Lets test the parameter p of a Binomial distribution at the 10% level.

Suppose a coin is tossed 10 times and we get 7 heads. We want to test whether or not the coin is fair. If the coin is fair, p = 0.5 . Put this as the null hypothesis:

H₀: p = 0.5
H₁: p =(doesn\' equal) 0.5

Similarly one more example

Suppose the mean weight of King Penguins found in an Antarctic colony last year was 15.4 kg. In a sample of 35 penguins same time this year in the same colony, the mean penguin weight is 14.6 kg. Assume the sample standard deviation is 2.5 kg. At .05 significance level, can we reject the null hypothesis that the mean penguin weight does not differ from last year?