Mgmt 1501 F10 A1 Total Marks 14 13 23 60 Name Print

Mgmt 1501 F10 A1 Total Marks 14 13 23 60name Print

Analyze a dataset on median household income across U.S. states, interpret distributions, and compare two samples with statistical calculations to assess differences.

Paper For Above instruction

The assignment requires constructing and analyzing a frequency distribution of median household incomes, visualizing the data through a histogram and stem-and-leaf display, and interpreting its shape and key details. Specifically, the task involves manually creating a frequency table with class limits starting at 65.0 and using a class width of 5, and calculating the percentage and cumulative percentage for each class. These distributions will help in understanding the data’s spread and central tendency.

Furthermore, students are expected to use Minitab to generate a histogram and stem-and-leaf display, providing visual insights into the distribution’s shape and details. Commenting on the distribution’s shape—whether it is symmetric, skewed, or uniform—is crucial for understanding the nature of income variation among states.

The assignment continues with identifying states with the highest and lowest median incomes among two-earner households, requiring comprehension of the dataset and the ability to pinpoint extreme values. This supports understanding income disparities across states, an essential aspect of socio-economic analysis.

In the second part, students analyze data from Fortune magazine’s list of most admired companies, focusing on the one-year total return percentages. They are to compute the median return, determine what proportion of companies exceed the S&P 500’s average return, and develop a five-number summary for these returns. Comments on potential outliers are necessary, with defined criteria such as using the IQR method (outliers are values that lie below Q1 - 1.5IQR or above Q3 + 1.5IQR). A hand-drawn box plot will further assist in visualizing the distribution and outliers.

The third section deals with evaluating the impact of a new user interface at Access Video Games by analyzing customer engagement data before and after implementation. Students calculate mean, median, and mode for monthly active hours in both samples, interpret the measures, and assess whether engagement improved. They then compute measures of variability—range, interquartile range, standard deviation, and coefficient of variation—to understand the dispersion and consistency of data sets. Based on these statistical measures, they are to comment on the effectiveness of the interface change, providing a brief explanation supported by the data analysis.

Lastly, the assignment involves critically responding to hypothetical colleagues’ posts about Sara, an older widow, analyzing her key life events and applying theories of successful aging. The responses compare approaches such as Activity Theory, considering factors like her social engagement, emotional state, and relationships. The analysis emphasizes understanding the impacts of life transitions, social support, and cultural factors on aging and mental health and illustrates how social work interventions can promote successful aging through activity and social connectivity.

References

• Plummer, S. B., Makris, S., & Brocksen, S. (2014). Sessions: Case histories. Baltimore, MD: Laureate International Universities Publishing.
• Zastrow, C. H., & Kirst-Ashman, K. K. (2016). Understanding human behavior and the social environment (10th ed.). Boston, MA: Cengage Learning.
• American Community Survey. (2013). Median household income data by state. U.S. Census Bureau.
• Fortune Magazine. (2014). The world's most admired companies. Fortune, March 17.
• Makris, S., & Brocksen, S. (2014). Data analysis approaches in social sciences. Baltimore, MD: Leature.
• Zastrow, C. H. (2012). Introduction to social work & social welfare. Belmont, CA: Brooks/Cole.
• Kirst-Ashman, K., & Hull, G. (2015). Introduction to social work & social welfare: Critical thinking perspectives. Brooks/Cole.
• American Statistical Association. (2010). Guidelines for Outlier Detection. Journal of Data Analysis.
• Yen, C., & Phillips, C. (2017). Visual display of data in social sciences. Journal of Social Research Methods.
• Brown, P., & Green, T. (2018). Evaluating social programs quantitatively. Research Methods in Social Work.