Holmes Institute Faculty Of Higher Education Assessme 978416

Holmes Institutefaculty Ofhigher Educationassessment Details And Submi

Holmes Institute Faculty of Higher Education assessment details and submission guidelines for Trimester T2 2020 include the following key information: assessment type, assessment title, purpose with ULO mapping, weight, total marks, due date, and submission instructions. All work must be submitted via Blackboard in MS Word format, with appropriate formatting including 12-point Arial font, 2 cm margins, section headings, and page numbers. References must be cited using Harvard style. The purpose of the assessment is to evaluate students' understanding of business research principles, statistical analysis, data summarization, and interpretation. The assignment comprises four questions covering data types, graphical representation, descriptive statistics, sampling methods, data collection, and analysis of relationships via correlation and regression. Students must show all working steps in prescribed formats, include Excel outputs as supplementary in Appendix, and ensure original submissions are free from plagiarism. Feedback criteria focus on correct application of statistical concepts, appropriate graphical and analytical techniques, and clarity of interpretation.

Paper For Above instruction

The assessment aims to demonstrate a comprehensive understanding of business research methodologies, statistical analysis techniques, and data interpretation relevant to business decisions. The purpose is to assess students’ ability to apply theoretical knowledge critically to practical scenarios, utilizing appropriate statistical tools and graphical methods while maintaining academic integrity.

Question 1 explores the analysis of categorical survey data related to public opinion on a proposed increase in development tax for a new railroad project. The responses are divided into three categories: vote for, vote against, and no opinion. The student should identify the data as categorical, with measurement at the nominal scale, and then present a suitable graph, such as a pie chart or bar chart, to visualize the distribution of responses. The analysis should interpret whether the results suggest overall support or opposition concerning the proposed tax increase based on the graphical representation and the proportions involved.

Question 2 involves the analysis of a dataset capturing the weekly hours spent by 25 CEOs in meetings. The student is expected to compute summary statistics—including mean, median, range, variance, standard deviation, coefficient of variation, and quartiles—to describe the data set comprehensively. Following this, a frequency distribution with class width of two hours should be created along with percentage frequencies. The task also involves plotting a histogram to analyze the shape of the distribution, making comments on whether it is symmetric, skewed, or bimodal, based on the visual shape and numerical measures.

Question 3 requires proposing appropriate data collection methods and sampling techniques for distinct business research scenarios. Justifications should be based on the purpose of each study, population characteristics, and the need for representativeness. For example, conducting an opinion poll among Australian voters may warrant stratified random sampling with face-to-face or online surveys, whereas understanding demographic profiles might use cluster sampling within specific communities. The analysis should reflect an understanding of qualitative and quantitative data collection methods, as well as suitable sampling frames and techniques tailored to each scenario.

Question 4 involves analyzing a dataset obtained from 15 children to explore whether watching television correlates with weight gain. The student should select television hours as the independent variable (X) and overweight in kilograms as the dependent variable (Y). An appropriate scatterplot should be generated, explaining the rationale for variable selection. Subsequently, a correlation coefficient should be calculated and interpreted to assess linear relationship strength. A simple linear regression model should be estimated, and its coefficients interpreted in context. The coefficient of determination (R-squared) should be computed to quantify explained variance, along with significance testing of the overall model at a 5% significance level. The standard error of the estimate must also be calculated, and conclusions made regarding the model’s fitness and predictive accuracy.

References

• Cleary, P., & Smith, J. (2019). Business research methods. Journal of Business Studies, 34(2), 112-130.
• Devore, J. L. (2015). Probability and Statistics for Engineering and the Sciences. Cengage Learning.
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018). Multivariate Data Analysis. Pearson.
• Keller, G., & Warrack, B. (2020). Statistics for Management and Economics. Cengage Learning.
• Lavrakas, P. (2008). Encyclopedia of survey research methods. Sage Publications.
• Montgomery, D. C., & Runger, G. C. (2014). Applied Statistics and Probability for Engineers. Wiley.
• Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics. Pearson.
• Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2013). Business Research Methods. Cengage Learning.
• INQAAHE. (2020). Standards and guidelines for quality assurance of joint programmes. International Network for Quality Assurance Agencies in Higher Education.
• Black, K. (2019). Business Statistics: For Contemporary Decision Making. John Wiley & Sons.