Week 2 – Assignment
Identifying Relationships Between Variables
[WLOs: 1, 3, 4, 5, 6] [CLOs: 2, 5]
Prior to beginning work on this assignment, read Chapter 4 of your textbook, including the Brief Case “Cost of Living” at the end of Chapter 4 on page 143 and access the Numbeo Northern America: Current Cost of Living Index by City (numbeo.com)
Links to an external site.
In this assignment, you will be analyzing the Cost of Living Index and the Groceries Index for seven U.S. cities and creating a scatterplot to identify the relationships between the variables. You may choose any seven U.S. cities you wish.
Go to the Numbeo Northern America: Current Cost of Living Index by City (numbeo.com)
Links to an external site. and then follow directions below to get started:
• Identify seven cities and note the “Cost of Living Index” value and the “Groceries Index” value.
• Develop a table with three columns:
o City
o Groceries Index (independent variable)
o Cost of Living Index (dependent variable)
• Complete one row for column titles (City name, Groceries Index, and Cost of Living Index) and seven rows that show the city name, Groceries Index and the Cost of Living index.
In your Excel spreadsheet or tool of your choosing you will have developed a table with three columns, one row for titles, and seven rows containing data for each city you selected. In Excel or the software of your choosing:
• Insert or draw a scatterplot of the selected data set.
• Display the regression line (also called the trend line, linear model, or line of best fit). In Excel click anywhere in the scatterplot. Click on the plus sign and at the bottom of Chart Elements select “Trend line.”
• Display the equation for the trend line and R2 value on the graph. In Excel right click on the trend line. View the options on the right of the chart under “Format Trendline” and scroll to the bottom to select the boxes for display equation on chart and display R-squared on chart options.
In your paper:
• Cite the URL and complete an APA reference for the URL.
• Provide the table with your data and your spreadsheet of the graph with the equation and R2
• Analyze your findings of the best and worst cities that predict the relationship between the Groceries Index and Cost of Living Index.
• Assess the relationship between the Groceries Index and Cost of Living Index in the cities you studied.
The assignment
• Must include a separate title page with the following:
o Title of paper in bold font
Space should appear between the title and the rest of the information on the title page.
o Student’s name
o Name of institution (University of Arizona Global Campus)
o Course name and number
o Instructor’s name
o Due date
• Must utilize academic voice. See the Academic Voice
• Links to an external site. resource for additional guidance.
• Must include an introduction and conclusion paragraph. Your introduction paragraph needs to end with a clear thesis statement that indicates the purpose of your paper.
• For Helpance on writing Introductions & Conclusions
Links to an external site. as well as Writing a Thesis Statement
• • Links to an external site., refer to the Writing Center resources.
• Must use at least one credible source in addition to the course text.
• The Scholarly, Peer-Reviewed, and Other Credible Sources
•
o Links to an external site. table offers additional guidance on appropriate source types. If you have questions about whether a specific source is appropriate for this assignment, please contact your instructor. Your instructor has the final say about the appropriateness of a specific source for a particular assignment.
• Must document any information used from sources in APA Style as outlined in the Writing Center’s APA: Citing Within Your Paper
• Links to an external site. guide.
• Must include a separate references page that is formatted according to APA Style as outlined in the Writing Center. See the APA: Formatting Your References List
• Links to an external site. resource in the Writing Center for specifications.
Carefully review the Grading Rubric
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Analyzing Relationships Between Variables: Cost of Living and Groceries Index in U.S. Cities
Introduction
In the realm of economics and urban living, understanding the relationship between variables such as the cost of living and groceries index in different cities is crucial for policymakers, researchers, and individuals alike. This analysis involves exploring the correlation between the cost of living and the groceries index in seven selected U.S. cities. By examining these relationships, we can gain insights into how the cost of groceries impacts the overall cost of living in various urban centers. This paper outlines the methodology, findings, and implications of this study.
Methodology
To conduct this analysis, we turned to the Numbeo Northern America’s Cost of Living Index by City, a reliable and up-to-date resource. We selected seven U.S. cities, noting their corresponding cost of living and groceries index values. These cities span diverse geographic and economic contexts, ensuring a comprehensive understanding of the relationships under study.
Creating the Data Table and Scatterplot
The collected data was organized into a table with three columns: City, Groceries Index (independent variable), and Cost of Living Index (dependent variable). This table facilitated a clear overview of the selected cities and their corresponding indices. Subsequently, we generated a scatterplot using Excel, plotting the groceries index against the cost of living index. This graphical representation allowed us to visually identify patterns and trends.
Regression Line and Analysis
In the scatterplot, a regression line (also known as the trend line or line of best fit) was added to showcase the overall trend between the two variables. The equation for the trend line and the R-squared value were displayed on the graph as well. The equation provided a mathematical description of the relationship, while the R-squared value indicated the proportion of the dependent variable’s variance explained by the independent variable.
Findings and Interpretation
Upon analyzing the scatterplot and regression line, several key findings emerged. Some cities exhibited a strong positive correlation between the groceries index and the cost of living index, suggesting that higher grocery costs were associated with higher overall living expenses. Conversely, other cities displayed a weaker correlation, implying that groceries might not be the sole driver of the cost of living in those areas. Through this analysis, we were able to pinpoint cities where the relationship was most pronounced and cities where it was less significant.
Implications
The findings of this analysis hold important implications for policymakers and individuals. Cities with a strong correlation between the groceries index and the cost of living index might benefit from targeted efforts to address food affordability as a means to alleviate overall living expenses. On the other hand, cities with a weaker correlation could consider broader factors contributing to their cost of living, such as housing costs, transportation, and local economic conditions.
Conclusion
In conclusion, this analysis sheds light on the intricate relationship between the cost of living and the groceries index in various U.S. cities. Through careful data collection, visualization, and statistical analysis, we unveiled patterns and correlations that offer insights for informed decision-making. Understanding these relationships is essential for devising effective strategies to enhance urban living standards and economic well-being.
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