Posted: June 26th, 2022

STT100 Statistics For Business

Question:

Give a general understanding of the case study.

Students are asked to add to the case study information and make it unique for their group.

The location, services, and size of the company can all be specified.

It is crucial to include the information you will use in your research project.

You can’t change the spreadsheet, but you can add to it.

Include all research questions, and one paragraph per question.

Avoid bullet points and listing.

I recommend at most three research questions.

Explain the definitions, formulas, and how you will use each statistical method. Give a brief explanation of variables, events, and probabilities.

Give a brief analysis of each result using statistical methods.

Answer:

Profit-oriented businesses are all about making money.

Profits can only be achieved by setting strategic goals and allocating resources to activities that will help achieve the goals and objectives (Ulrich 2003).

Hikins & Main is a global real estate service company.

Multinational organizations that operate in different markets will need to have succinct strategies that yield sufficient profits. (Heizer 2016).

Hikins & Main has to develop plans for the success of its services and products in various markets.

The business currently operates in at most five locations, including Belton and Domaine.

The Mount and Terratae are also available.

Understanding the business operating model is one way Hikins & Main can calculate their profitability (Higgins 2012).

It may be necessary to look back at the financial year in question in order to determine the operation’s level, number of houses sold, and revenue generated.

An analysis of the financial results from the previous year is also helpful in determining what needs to be done to increase the company’s profitability.

This historical analysis can help to identify the risks and challenges that the business faces in order to determine risk mitigation strategies (Kleindorfer, Saad 2005).

This paper focuses on the past financial year of the business with a focus on sales, operations and rooms.

This analysis focuses on specific locations where the houses are located, and is categorized into cities.

Hikins & Main’s historical data analysis can help us understand the future.

It is important to understand the relationships between various factors.

The business might be interested in knowing how the number of bedrooms affects the marketing budget.

A set of questions can then be asked to the company data, which will be analysed (Veal 2005).

These questions will give insight into the market and potential benefits of leveraging competitive business advantage.

How is the distribution of rental properties per city?

A business may be interested in categorizing the property that is available for sale.

This categorization could take many forms. However, since the properties are in different places, it would be best to base your analysis on the city.

It can be used to indicate the types, distribution and number of different property types in the market.

Cano et. al. argued that knowing the distribution of property is helpful in determining what kind of marketing can be done in any given area or city (Cano & Jaramillo 2004).

How does the listed price compare to the final sales price of the house in each city?

The sales projections are represented by the listed price.

The final sales, on the other hand are the actual market sales.

The two variables can be compared to determine if the company is meeting the benchmarks.

The deviations between actual and listed sales volumes can be used to determine the corrective actions that the company should take in order to improve profitability (Averill 2011, Averill).

How much is advertising spend and how many rooms are available in each house?

The company spends money on advertising to increase the sales index of the property rooms.

One property may have only one bedroom while others have seven.

Similar to the above, one property may have only one bathroom while others might have four bathrooms.

Because the property features are different, so are the markets.

Therefore, the business must ensure that they are correctly advertising any property unit.

De (2013) noted that advertisements should be specific to the market and appropriately focused.

How are the bedrooms, bathrooms, and other rooms distributed in each city?

The economic capacity of the residents of any particular location should dictate the type of rental property that is developed.

This is why it is important for companies to assess the number of bathrooms and bedrooms in each location.

If there are more bathrooms and bedrooms in a property, the owners should have the financial ability to purchase it.

You can increase your marketing budget and rent the property at a premium rate (Aaker Kumar & Day 2008).

Does the house number correlate with the advertising budget?

House numbers can indicate when the house was acquired by the company.

This information can be used to distinguish between the new and the old houses.

If the house numbering is progressive, the first numbers such as number one could be considered an old house.

The company might have the most recent numbers for the houses that are currently in its custody.

This information could be used to determine how profitable an old house is compared to new houses.

Is there a relationship between the number of advertising expenses and all rooms in the property?

The business can be assured that their investment funds are being used optimally if there is a relationship between the number of rooms in each property and the amount of advertising expenditure.

If the business experiences a higher level of investment in house facilities but not higher returns, it will be believed that the company is investing in areas that haven’t been properly researched and developed (Sreenivasan 2007).

Select Statistical Methods

This research study will use both the descriptive as well as the inferential analyses.

The descriptive analysis will help to understand the core and superficial values of the analysis (Stone. Sidel & Bloomquist. 2008).

The MS Excel software will allow you to use pivot tables and pivot chart in order to provide descriptive data analytical results.

Inferential data analysis can be used to reveal hidden trends and the lateral analysis hidden variables through regression and correlation analysis (Ramsay 2006).

A simple linear correlation analysis and a simple regression analysis can be used for a basic research.

These are useful for displaying the relationships between variables and the nature and strength of those relationships.

To provide insight into sales data for future model analysis and strategic decision making, the research will look at regression and correlation analysis.

Technical Analysis

The City Distribution of the Rental Property

Hikins & Main has five locations.

The amount of business activity concentrated in each city is determined by the number of properties.

For example, marketing activities can be more concentrated in a city that has fewer rental houses (Keegan & Schlegelmilch 2001).

To determine the location of a branch office, the business can use information about the number of houses in each city.

The business may explore ways to increase the property’s value in cities with fewer houses.

Below is a table that summarizes the business data showing how many rental houses are concentrated in each city.

Domaine was the most concentrated rental property area, with a total sale of 196 units in the preceding year.

This was a far cry from Terratae, which sold 70 units.

Belton sold 56 units, four more than Mount who sold 52 units.

Hikins & Main managed only 43 houses in Hills, which is where they sold the most units.

The analysis shows that the business is concentrating its activities in Domaine. This city could qualify for a regional or headquarter.

It should also have a larger marketing budget for next year, in order to invest in new growth strategies.

The business should also consider strategies to increase Hills’ number.

Below is a summary of the distribution of houses by city.

The sum of advertising expenditure and all rooms available in every house property

The sum of all advertising costs in a city is closely related to the number of rooms.

Domaine has the largest number of rooms at 196 and the highest budget at 854000 dollars.

Hills with the smallest number of rooms had a marketing budget of 98000 dollars.

This indicates that the company was able to distribute marketing resources equally across all cities.

It is very helpful for businesses to see the distribution of rooms in each city. This includes information such as how many bedrooms, bathrooms, and total rooms.

These analyses can be used to determine the amount of units that need to be added for the facilities.

There were 180 bedrooms and 128 bathrooms in each city.

There were a total 308 bedrooms and bathroom.

The total number of rooms was 418. This means that the living rooms and other rooms of the residential homes accounted for 109.

Therefore, the business has the ability to decide whether or not to add more rooms to its property in future constructions and sales.

The table shows a positive correlation.

The correlation between actualized and listed sales is strong.

The correlation coefficient is 0.980465121, which indicates a strong positive correlation (Cohen West & Aiken 2013, 2013).

This means that a business’s sales projection increases will result in an increase in actualized sales volume.

Correlation between the House Number and Advertising Expenditure

Management at Hikins & Main may be interested in if there is a correlation of the house number with the advertising expenditure.

Below is the correlation analysis of the variables.

The table shows that there is a very weak correlation between advertising spending and house number.

The correlation coefficient is 0.06824 which is close to zero (Cohen West & Aiken 2013, 2013).

Therefore, the business management might not bother to figure out how to correlate the variables.

Comparison of All Rooms and Advertising Levels

Management at Hikins & Main may be interested to see the regression relationship between advertising expenditure and total rooms in houses in its past financial year.

Below is a table that summarizes the regression.

The table below shows a significance interval (0.00005) which is lower than the 0.05 significance range that gives 95% confidence level (Montgomery, Peck & Vining, 2012.

The linear regression relationship between advertising expenditure and room count in the previous financial year is 3.618443854. The coefficient of the variable factor, 0.134502319, can be found here.

The regression equation (Menard 2002) can be used to calculate the total value of the equation Y = C + a1X1+ e0.

The equation shows that Y is the linear regression value and C is the constant value in the analysis. A1X1 + e0 would be positive.

Results and Discussions

In the last financial year, Hikins & Main did extremely well.

The business was able create performance benchmarks based on the presented analytical data. These benchmarks were generally achieved for all costs.

The business was also able to ensure profitability across all businesses by allocating sufficient resources to cities with high housing stock and a smaller amount to those with fewer.

Analyzing the relationships between different concepts that could help in creating a strategic plan (Kaynak 2003) revealed that the budget and the number of rooms in each house had a direct relationship.

This was confirmed by linear regression analysis which created a positive relationship between advertisement costs and the number rooms in the city.

The business was unable to find any relationship between advertisement costs and house numbers, as the house number is an arbitrarily calculated value that may not be indicative of any performance index.

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