Statistical Analysis of Average Vehicle Prices: Hypothesis Testing and Results

September 28, 2023
Ryan Nelson
Ryan Nelson
🇺🇸 United States
Statistics
Ryan Nelson, Ph.D., is a seasoned statistics expert with 7+ years of experience and a doctorate from Northwest University. Specializing in assisting students with assignments, Ryan brings a wealth of knowledge and expertise to statistical challenges.
Key Topics
  • Problem Description
  • Data Summary:
  • Hypothesis:

In our comprehensive statistical analysis, we delve into the fascinating world of average vehicle prices in your town area. Through hypothesis testing, we determine if these prices significantly differ from the 80th percentile of the dataset, employing a t-test for small sample sizes and an unknown population standard deviation. Our data-driven approach reveals the crucial insights you need, including the calculated t-test statistic and the p-value. The results lead us to a compelling conclusion: there is no substantial statistical basis to assert a meaningful difference between the average vehicle prices and the 80th percentile value in your local area. Explore this analysis for a deeper understanding of statistical methods and their real-world applications.

Problem Description

In this Statistical Analysis assignment, we aim to determine whether the average vehicle price in a town area deviates significantly from the 80th percentile of the dataset. To do this, we need to conduct a hypothesis test. The choice between a z-test and a t-test hinges on two essential factors: the sample size and our knowledge of the population standard deviation.

Given a relatively small sample size (n = 10) and an unknown population standard deviation, it is appropriate to utilize a t-test. The t-test is designed to accommodate smaller sample sizes and employs the sample standard deviation to estimate the population standard deviation, making it a more suitable choice for cases where the population standard deviation is unknown. Therefore, for this scenario, a t-test is the appropriate statistical method for conducting the hypothesis test.

Data Summary:

  • Sample Mean (x̅): $85,623
  • Sample Standard Deviation (s):$59,931.02
  • Population Mean (μ – 80th percentile):$114,592
  • Sample Size (n):10

Hypothesis:

  • Null Hypothesis (Ho):The average vehicle price in the town area is equal to the 80th percentile ($114,592). μ = $114,592
  • Alternative Hypothesis (Ha):The average vehicle price in the town area is not equal to the 80th percentile ($114,592). μ ≠ $114,592

T-Test Statistic: To analyze our hypothesis, we'll perform a t-test, considering our small sample size (n = 10) and an unknown population standard deviation. The t-test accommodates smaller sample sizes and uses the sample standard deviation to estimate the population standard deviation. This robust approach is suitable for small samples where the population standard deviation is not known.

T-Test Statistic (TS) = (Sample Mean - Population Mean) / (Sample Standard Deviation / √Sample Size) TS = ($85,623 - $114,592) / ($59,931.02 / √10) = -1.5286

P-Value:The p-value, which measures the probability of obtaining results as extreme as the ones observed in our sample, is a crucial part of the hypothesis test.

P-value ≈0.1607

Conclusion: After a thorough analysis of the hypothesis test results, it becomes clear that the data does not provide substantial support for the claim that the average vehicle price in the town area significantly differs from the 80th percentile of the dataset.

The calculated p-value is approximately 0.1607, which exceeds our chosen significance level (α = 0.05). Therefore, we are unable to reject the null hypothesis (Ho). This implies that, within the scope of this analysis, there is no strong statistical evidence to suggest a meaningful difference between the average vehicle price and the 80th percentile value.

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