Complete The Following Problems At The Chapter's End

Complete The Following Problems At The Conclusion Of Chapter Onepage

Complete the following problems at the conclusion of chapter one: Which of these data are categorical and which are numerical? a. A cell phone’s brand. b. A cell phone’s estimated battery life. c. A cell phone’s screen size. d. A cell phone’s carrier.

1.4 Identify the following data as cross-section, time series, or panel data: a. Unemployment rates in Germany, Japan, and the United States in 2015. b. Inflation rates in Germany, Japan, and the United States in 2009. c. Unemployment rates in Germany in 2012, 2013, 2014, and 2015. d. Unemployment rates in Germany and the United States in 2012, 2013, 2014, and 2015.

1.6 The Framingham Heart Study began in 1948 with 5209 adult subjects, who have returned every 2 years for physical examinations and laboratory tests. The study now includes the children and grandchildren of the original participants. Identify the following data as cross-section, time series, or panel data: a. The number of people who died each year from heart disease. b. The number of men who smoked cigarettes in 1948, 1958, 1968, and 1978. c. The ages of the women in 1948. d. Changes in HDL cholesterol levels between 1948 and 1958 for each of the females. 1.7 (continuation) Identify the following data as cross-section, time series, or panel data: a. Blood pressure of one woman every 2 years. b. The average HDL cholesterol level of the men in 1948, 1958, and 1968. c. The number of children each woman had in 1958. d. The age at death of the 5209 subjects. 1.8 Table 1.12 lists the reading comprehension scores for nine students at a small private school known for its academic excellence. Twenty students were admitted to the kindergarten class, and the nine students in the table stayed at the school through eighth grade. The scores are percentiles relative to students at suburban public schools; for example, Student 1 scored in the 98th percentile in first grade and in the 53rd percentile in second grade. Identify the following data as cross-section, time series, or panel data: a. Student 4’s scores in grades 1 through 8. b. The nine students’ eighth grade scores. c. The nine students’ scores in first grade and eighth grade. d. The nine students’ scores in first grade through eighth grade.

1.9 Table 1.13 shows index data on the overall CPI and three items included in the CPI. Explain why you either agree or disagree with these statements: a. Food cost more than housing in 2010. b. Housing cost more than food in 2000 but cost less than food in 2010. c. The cost of food went up more than the cost of housing between 2000 and 2010.

1.10 (continuation) Explain why you either agree or disagree with these statements: a. Apparel cost less than food in 2000. b. The cost of apparel went down between 2000 and 2010. c. The cost of food went up more than the overall CPI between 2000 and 2010. 1.12 Table 1.14 shows data on four apparel price indexes that are used to compute the CPI. Explain why you either agree or disagree with these statements: a. Men’s apparel cost more than women’s apparel in 2000. b. Men’s apparel cost more than women’s apparel in 2010. c. Men’s apparel cost more in 2000 than in 2010. 1.30 A researcher wants to see if U.S. News & World Report rankings influence the number of applications received by colleges and universities. He obtains the unadjusted data shown in Table 1.17 on applications to one small liberal arts college. To account for the nationwide growth of college applications, this researcher uses data from the COFE showing an average 4% annual increase. He then divides each application number by 1.04 to adjust for growth. Explain his mistake and describe how to correctly calculate the adjusted values without performing the calculations.

1.34 The Dow Jones Industrial Average was 838.92 on December 31, 1970, and 11,577.51 on December 31, 2010. The CPI was 39.8 in December 1970 and 219.2 in December 2010. Did the Dow increase more or less than consumer prices over this period? If the Dow had increased at the same rate as consumer prices, what would its 2010 value be?

1.45 Mr. Bunker lives on beer and pretzels. In 2000, he bought 1000 six-packs of beer at $2.00 each and 500 bags of pretzels at $1.00 each. In 2010, beer costs $3.00 per six-pack and pretzels cost $2.00 per bag. To calculate his inflation rate, we consider the total cost of his purchases in each year. What was the percentage increase in Bunker's cost of living from 2000 to 2010? If we create a Bunker Price Index scaled to 100 in 2000, what is its value in 2010?

Paper For Above instruction

The tasks outlined in the chapter require a comprehensive understanding of data classification, types of data, and economic indicators over time. This paper addresses these components systematically, emphasizing the distinction between categorical and numerical data, the classification of data structures such as cross-sectional, time series, and panel data, and the analysis of economic measures like CPI and stock indexes to understand inflation and market trends. The discussion also explores the methodology of adjusting data for growth, interpreting index data, and calculating inflation rates, illustrating these concepts with real-world examples and applying proper analytical techniques.

Understanding Data Types: Categorical versus Numerical

In data analysis, identifying whether data are categorical or numerical is fundamental. Categorical data represent qualities or categories that do not have a numerical value, such as a cell phone's brand or carrier, which are labels used to distinguish categories. Conversely, numerical data quantify measurements or counts, such as a cell phone's estimated battery life or screen size, which can be expressed in numbers and subjected to operations like addition or averaging. Recognizing these distinctions helps in selecting appropriate statistical methods; for example, frequency counts and mode calculations suit categorical data, while mean and standard deviation computations are suitable for numerical data (Hair, Wolfinbarger, Money, Samouel, & Page, 2011).

Classifying Data Structures: Cross-Section, Time Series, and Panel Data

The classification of data as cross-section, time series, or panel depends on the nature of observations over entities and time frames. Cross-sectional data capture information at a single point in time across multiple entities—such as unemployment rates in different countries in 2015. Time series data entail observations over time for a single entity, for example, unemployment rates in Germany across several years. Panel data combine both approaches, tracking multiple entities over multiple time periods, as seen in the Framingham Heart Study which records health metrics of individuals over decades. Correct classification enables appropriate econometric modeling and inference (Baltagi, 2008).

Economic Indicators and Index Data Interpretation

The Consumer Price Index (CPI) and stock market indices reflect inflation and market performance. Analyzing CPI data involves understanding whether prices of specific goods, like food or housing, rose or fell over time. For instance, if food's index increases more than housing's, it suggests food inflation outpaced housing costs. When comparing CPI data across years, it is crucial to consider base years and how indices are scaled. For example, if the Dow Jones increased from 838.92 to 11,577.51 while CPI rose from 39.8 to 219.2, comparing these increases reveals whether market gains outstripped inflation. Calculating real growth involves adjusting nominal figures by inflation rates (Bureau of Labor Statistics, 2020).

Adjusting Data for Growth and Correct Methodologies

Adjusting data like college applications requires considering the consistent growth rate. Dividing each year's applications by a fixed factor like 1.04 assumes a constant growth rate, but this method is flawed because it does not account for compounding effects. Correct adjustment involves dividing each annual application figure by (1 + r) raised to the number of years since the base year, where r is the annual growth rate expressed as a decimal. This approach accurately accounts for compound growth, providing a true basis for comparison across years (Cochrane & Harrison, 2008).

Calculating Inflation and Price Indices

Calculating inflation involves determining the percentage change in price levels over time. For Mr. Bunker's purchases, the total expenditure in each year is computed, and the percentage increase is derived from the difference relative to 2000. To create a Price Index, we scale the total cost in each year relative to the baseline year (2000), setting the index to 100 for 2000. The index value in 2010 indicates how much prices have increased since 2000, reflecting inflationary pressures on Bunker's cost of living (Mankiw, 2015). This process highlights how individual consumption patterns can be analyzed for inflationary trends.

Conclusion

Understanding the classification and analysis of different data types, along with the interpretation of economic indices, is crucial for effective economic analysis. Proper data classification guides the selection of statistical methods, while accurate adjustments and interpretations of indices enable better insights into economic trends like inflation and market performance. The real-world examples provided illustrate these principles in practice, offering a comprehensive overview of essential data analysis techniques in economics and finance.

References

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