We can work on Regional real estate company to determine if your regions housing prices

Scenario
You have been hired by your regional real estate company to determine if your regions housing prices and housing square footage are significantly different from those of the national market. The regional sales director has three questions that they want to see addressed in the report:

Are housing prices in your regional market lower than the national market average?
Is the square footage for homes in your region different than the average square footage for homes in the national market?
For your region, what is the range of values for the 95% confidence interval of square footage for homes in your market?
You are given a real estate data set that has houses listed for every county in the United States. In addition, you have been given national statistics and graphs that show the national averages for housing prices and square footage. Your job is to analyze the data, complete the statistical analyses, and provide a report to the regional sales director. You will do so by completing the Project Two Template located in the What to Submit area below.

Directions
Introduction

Region: Start by picking one region from the following list of regions:
West South Central, West North Central, East South Central, East North Central, Mid Atlantic
Purpose: What is the purpose of your analysis?
Sample: Define your sample. Take a random sample of 500 house sales for your region.
Describe what is included in your sample (i.e., states, region, years or months).
Questions and type of test: For your selected sample, define two hypothesis questions (see the Scenario above) and the appropriate type of test for each. Address the following for each hypothesis:
Describe the population parameter for the variable you are analyzing.
Describe your hypothesis in your own words.
Identify the hypothesis test you will use (1-Tail or 2-Tail).
Level of confidence: Discuss how you will use estimation and confidence intervals to help you solve the problem.
1-Tail Test

Hypothesis: Define your hypothesis.
Define the population parameter.
Write null (Ho) and alternative (Ha) hypotheses. Note: For means, define a hypothesis that is less than the population parameter.
Specify your significance level.
Data analysis: Summarize your sample data using appropriate graphical displays and summary statistics and confirm assumptions have not been violated to complete this hypothesis test.
Provide at least one histogram of your sample data.
In a table, provide summary statistics including sample size, mean, median, and standard deviation. Note: For quartiles 1 and 3, use the quartile function in Excel:
=QUARTILE([data range], [quartile number])
Summarize your sample data, describing the center, spread, and shape in comparison to the national information (under Supporting Materials, see the National Summary Statistics and Graphs House Listing Price by Region PDF). Note: For shape, think about the distribution: skewed or symmetric.
Check the conditions.
Determine if the normal condition has been met.
Determine if there are any other conditions that you should check and whether they have been met. Note: Think about the central limit theorem and sampling methods.
Hypothesis test calculations: Complete hypothesis test calculations.
Calculate the hypothesis statistics.
Determine the appropriate test statistic (t). Note: This calculation is (mean target)/standard error. In this case, the mean is your regional mean, and the target is the national mean.
Calculate the probability (p value). Note: This calculation is done with the T.DIST function in Excel:
=T.DIST([test statistic], [degree of freedom], True) The degree of freedom is calculated by subtracting 1 from your sample size.
Interpretation: Interpret your hypothesis test results using the p value method to reject or not reject the null hypothesis.
Relate the p value and significance level.
Make the correct decision (reject or fail to reject).
Provide a conclusion in the context of your hypothesis.
2-Tail Test

Hypotheses: Define your hypothesis.
Define the population parameter.
Write null and alternative hypotheses. Note: For means, define a hypothesis that is not equal to the population parameter.
State your significance level.

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Sample Answer

 

 

 

 

Analyzing Regional Housing Market Data

Introduction

Purpose: The primary objective of this analysis is to assess whether our region’s housing market differs significantly from the national average in terms of pricing and square footage. By comparing regional data to national benchmarks, we aim to identify potential opportunities and challenges for our sales team.

Sample: We will analyze a random sample of 500 house sales from the West South Central region. This region includes the states of Texas, Oklahoma, Arkansas, and Louisiana. The data will cover sales from the past year.

 

Full Answer Section

 

 

 

 

Hypothesis Testing:

Hypothesis 1: Are housing prices in the West South Central region lower than the national average?

  • Population Parameter: The population mean housing price for the West South Central region.
  • Hypothesis: The average housing price in the West South Central region is less than the national average.
  • Hypothesis Test: One-tailed t-test.

Hypothesis 2: Is the average square footage of homes in the West South Central region different from the national average?

  • Population Parameter: The population mean square footage of homes in the West South Central region.
  • Hypothesis: The average square footage of homes in the West South Central region is different from the national average.
  • Hypothesis Test: Two-tailed t-test.

Level of Confidence:

We will use a 95% confidence level for our analysis. This means that we are 95% confident that the true population parameter lies within our calculated confidence interval.

Data Analysis and Hypothesis Testing

Data Summary:

[Insert a histogram of housing prices and square footage]

Statistic Value
Sample Size (n) 500
Mean Price $X
Median Price $X
Standard Deviation (Price) $X
Mean Square Footage X sqft
Median Square Footage X sqft
Standard Deviation (Square Footage) X sqft

Checking Assumptions:

  • Normality: We will assess normality using a histogram and a normal probability plot. If the sample size is large enough (n > 30), the Central Limit Theorem can be applied.
  • Independence: We assume that the house sales are independent of each other.

Hypothesis Testing for Housing Prices:

  • Null Hypothesis (H₀): μ ≥ μ₀ (where μ₀ is the national average price)

  • Alternative Hypothesis (H₁): μ < μ₀

  • Test Statistic: t = (x̄ – μ₀) / (s/√n)

  • P-value: Calculate the p-value using the t-distribution and the calculated test statistic.

  • Decision Rule: If the p-value is less than the significance level (α = 0.05), reject the null hypothesis.

Hypothesis Testing for Square Footage:

  • Null Hypothesis (H₀): μ = μ₀ (where μ₀ is the national average square footage)

  • Alternative Hypothesis (H₁): μ ≠ μ₀

  • Test Statistic: t = (x̄ – μ₀) / (s/√n)

  • P-value: Calculate the p-value using the t-distribution and the calculated test statistic.

  • Decision Rule: If the p-value is less than the significance level (α = 0.05), reject the null hypothesis.

Confidence Intervals:

Calculate the 95% confidence interval for the mean square footage using the formula:

CI = x̄ ± tα/2 * (s/√n)

Interpretation of Results:

Based on the p-values and confidence intervals, we can draw conclusions about the differences between our region’s housing market and the national market.

Note: The specific calculations and interpretations will depend on the actual data collected. It is essential to use statistical software like Excel, SPSS, or R to perform the calculations accurately.

 

 

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