Create An Inferential Statistics Hypothesis Test Usin 407851

Createan Inferential Statistics Hypothesis Test Usingthe Research Qu

Createan inferential statistics (hypothesis) test using the research question and two variables your learning team developed for the Week 2 Business Research Project Part 1 assignment. Include: · The research question · Mock data for the independent and dependent variables Determine the appropriate statistical tool to test the hypothesis based on the research question. Conduct a hypothesis test with a 95% confidence level, using the statistical tool. Write an interpretation of no more than 350-words of the results and provide your findings. Format your paper consistent with APA guidelines.

Paper For Above instruction

Introduction

In contemporary business research, hypothesis testing serves as a pivotal method for confirming or refuting assumptions about relationships between variables. This study focuses on investigating whether a decline in product quality impacts sales in HD, Inc., a leading motorcycle manufacturer. Specifically, the research explores if a reduction in manufacturing quality correlates with a decrease in quarterly sales, a question crucial for strategic quality management and sales forecasting.

Research Question

Does a decrease in product quality have a statistically significant effect on HD, Inc.'s quarterly sales?

Operational Variables and Mock Data

For the purpose of this hypothesis test, the dependent variable is the quarterly sales figures, measured in units sold, which are numeric. The independent variable is the level of manufacturing quality, operationalized as a composite score derived from the four inspection types (pre-production, during production, final inspection, and container loading), scored on a scale from 1 to 10. Higher scores reflect better quality.

Sample mock data represent five quarters with varied quality scores and corresponding sales:

  • Quarter 1: Quality Score = 8.5, Sales = 50,000 units
  • Quarter 2: Quality Score = 7.8, Sales = 45,000 units
  • Quarter 3: Quality Score = 6.2, Sales = 40,000 units
  • Quarter 4: Quality Score = 5.5, Sales = 35,000 units
  • Quarter 5: Quality Score = 4.8, Sales = 30,000 units

Selection of Statistical Tool

Given the relationship between a continuous independent variable (quality score) and a continuous dependent variable (sales), the appropriate statistical analysis is the Pearson correlation coefficient, supplemented by simple linear regression to understand the predictive impact of quality on sales.

Hypothesis Testing

Null Hypothesis (H0): There is no relationship between quality score and sales. (Correlation coefficient ρ = 0)

Alternative Hypothesis (H1): There is a significant relationship between quality score and sales. (Correlation coefficient ρ ≠ 0)

Using the mock data, a Pearson correlation coefficient (r) is calculated, and significance is tested at a 95% confidence level (α = 0.05). The computed r value is approximately 0.99, indicating a very strong positive correlation.

With degrees of freedom df = n - 2 = 3, the critical value for r at the 0.05 significance level is approximately 0.878. Since the calculated r exceeds this critical value, we reject the null hypothesis, concluding that there is a statistically significant positive relationship between manufacturing quality and sales.

Results and Interpretation

The analysis demonstrates a strong and statistically significant positive correlation (r ≈ 0.99, p

References

  • Deming, W. E. (1986). Out of the crisis. MIT Center for Advanced Educational Services.
  • Juran, J. M., & De Feo, J. A. (2011). Juran's Quality Control Handbook (6th ed.). McGraw-Hill Education.
  • Field, A. (2013). Discovering statistics using IBM SPSS Statistics (4th ed.). SAGE Publications.
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
  • Sheskin, D. J. (2011). Handbook of parametric and nonparametric statistical procedures (5th ed.). Chapman and Hall/CRC.
  • Glen, S. (2012). Pearson Correlation Coefficient (r). Statistics How To. https://www.statisticshowto.com/pearson-correlation-coefficient/
  • Roberts, M. (2015). Applied statistics in business and economics. Pearson Education.
  • Heinrich, J. G. (2017). Business statistics: Practical decision-making. Routledge.
  • Montgomery, D. C. (2012). Introduction to statistical quality control. Wiley.
  • Walpole, R. E., Myers, R. H., Myers, S. L., & Ye, K. (2012). Probability and statistics for engineering and the sciences. Pearson.

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