Statistics Paper - Condosales

Efficient use of the statistical tool-regression will helps in deriving crucial relationship between variables that could offer significant pointers towards successful business decisions. Number crunching has emerged as the single most effective solution to pull up a declining businesses while on the other hand, the author advices customers to be vigilant in their business transactions that offer additional features at the same price. He also explains, how customer feedback extracted from market data and random surveys drives towards profitable decisions in the future for different sections of the society.

Due to financial hardship, the Nyke shoe company feels they only need to make one size of shoes, regardless of gender or height. They have collected data on gender, shoe size, and height and have asked you to tell them if they can change their business model to include only one size of shoes – regardless of height or gender of the wearer. In no more 5-10 pages (including figures), explain your recommendations, using statistical evidence to support your findings. The data found are below:

(e) Is there a significant relationship between the selling price and the assessed value of the house? Use 5 % level of significance.

Because of the method of monthly data collection, absolute randomness could not be obtained; however, it was decided that 5 iterations was sufficient because the sixth iteration showed a decrease in the quality of the residual plots. The first test performed was the p-value test of the individual variables. A p-value is the probability, ranging from 0 to 1, of obtaining a test statistic similar to the one that was actually observed. The only input that did not have a p-value less than 0.05, which was the chosen significance level, was the “Number of Walmarts” variable; the number of Walmarts has no specific effect on the output, property crime rate. The R2 of the analysis, or the coefficient of determination, provides a measure of how well future outcomes are likely to be predicted by the model. R2 values range from 0 to 100% (or 0 and 1) and the

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An agent for a residential real estate company in a large city would like to be able to predict the monthly rental cost of apartments

Goodlife Management experienced an increase in the demand curve of rental apartments due to the decrease in the rental rate. This shift in the demand curve would cause the equilibrium price to slightly increase because the demand curve would shift to the right and the supply curve would stay the same causing the price to fall higher upon that demand curve. The quantity of the apartments available would stay the same and ultimately would encourage the property manager to follow through with the decision to decrease the rental price. A great example of a shift in the supply curve occurred when the property manager was asked to rent all of the 2500 apartments available in order to obtain zero percent occupancy. With the increase of the monthly rental price, Goodlife Management shall have more incentive to lease more apartments to tenants. This shift in the supply curve would drive the equilibrium price in a more positive direction to further encourage the rental of more apartments. The quantity of apartments would obviously increase caused by the increase in the supply available for rent. Such a decision to rent additional apartments at a higher price would more than likely be a definite alternative as revenue shall increase as the vacancy rate gets closer to zero percent. Ebara Technologies, Inc. (ETI) is a nationwide corporation who manufactures vacuum pumps in which one of the corporate offices resides

The sales data were obtained for a new oceanside condominium complex consisting of two adjacent and connected eight-floor buildings. The complex contains over 200 units of equal size (approximately 500 square feet each). The

Sales data were drawn from a new Oceanside condominium complex consisting of two adjacent and connected eight floor buildings. The 200 units in the complex are approximately 500 square feet each.

To calculate the mean I added up the sum of days to sell and divided by 18. The mean is 203,188.89, which mean the average of the listed price. The median was calculated by listing the numbers in numerical order from lowest to highest and located the number in the middle of 203,500. The median represents the middle number of the days that it took to sell the condominiums. After calculating the median I located the minimum and maximum based the lowest and highest data, which are 135,500 and 292,500. These represent the range of the sale price. Lastly, I used the formula to get the standard deviation of 43,891.72, which measures the variability.

Major League Baseball is known as America’s favorite pastime, and MLB teams spend an extensive amount of money in the excess of a billion dollars with the ultimate goal to win the World Series. This learning team’s focus throughout this descriptive statistics paper is the MLB players’ performances, salaries, salary caps, and winning percentages. Though salaries will by no means be a trade for wins, the goal is to use the less experienced players and pay them a lower salary. Research has been done on whether or not player’s salaries and wins are connected.

The data for the second test to be conducted by our group consists of lot sizes of the residential properties that are up for sale in Toronto and Vancouver. The samples are represented in m2 (metres squared; area of the land in which the residential properties are built on). The data taken are based on the properties that are up for

A difficult characteristic to understand about the housing market is how a price is given for a particular house. That price will be designated to that particular house alone. All houses have various pricing, so I can’t always assume that one will cost more or less than any other. The pricing for houses vary based on their characteristics. Each characteristic must be analyzed to determine its contribution or detraction toward the price. I have taken some of these characteristics and modeled the relationship between them and the price of real estate for a specific area.

Run the regression Report your answer in the format of equation 5.8 (Chapter 5, p. 152) in the textbook including and the standard error of the regression (SER). Interpret the estimated slope parameter for LOT. In the interpretation, please note that PRICE is measured in thousands of dollars and LOT is measured in acres.

• Error values (ε) are statistically independent • Error values are normally distributed for any given value of x