# MAT 240 Mod 5 Assignment 9-25 (1) (1) (1)

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Hypothesis Testing for Regional Real Estate Company 1 Hypothesis Testing for Regional Real Estate Company Renee Gore Southern New Hampshire University
Hypothesis Testing for Regional Real Estate Company 2 Introduction By understanding the initial context or framework of the scenario, analysts can identify patterns and trends within the property transactions in the Pacific region. This analysis can help stakeholders make informed decisions regarding investments, market strategies, and policy development. Additionally, a thorough understanding of the initial context allows for a more comprehensive assessment of the potential impact and implications of any changes or developments in the property market. Hypothesis Test Setup The study examines the cost per square foot in the Pacific area, with the null hypothesis suggesting it is equal to the average cost. The alternative hypothesis suggests it exceeds the average cost. The distribution test is used, focusing on the left tail of the distribution. Data analysis preparations include data cleaning, transformation, and exploration. Data cleaning removes errors and inconsistencies, while transformation converts variables to meet test assumptions. Exploration examines distribution, patterns, and relationships using descriptive statistics and visualizations. These steps ensure the validity and meaningfulness of the data analysis. Data Analysis Preparations
Hypothesis Testing for Regional Real Estate Company 3 Descriptive Statistics Mean 264.0673 Standard error 5.117495 Median 202.9586 Mode 206.1653 Standard Deviation 161.8294 Sample Variance 26188.76 Kurtosis 4.493499 Skewness 2.084759 Range 967.4516 Minimum 103.8324 Maximum 1071.284 sum 264067.3 Count 1000 The T-distribution is chosen as it is suitable for small sample sizes. The significance level of 0.05 indicates that there is a 5% chance of obtaining the observed results by chance alone. With all the necessary requirements fulfilled, the statistical test can proceed to analyze the data accurately. Calculations To determine the appropriate test statistic, we need to calculate the standard error. The standard error is the standard deviation of the sample divided by the square root of the sample size. Once we have the standard error, we can calculate the test statistic using the formula mentioned earlier. Excel Function Type of Test =T.DIST.RT(]264.0673-275) Right-tailed =T.DIST(-10.9327)/5.117495, 1) Left-tailed =T.DIST.2T(-2.136336102,1000,True) Two-tailed Test Decision Since the p value (0.016447136) is lower than the significance threshold (.05), I am forced to