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ECON 3790 Statistics for Business and Economics

ECON 3790 Statistics for Business and Economics. Instructor: Ou Hu, Ph.D., CFA Youngstown State University Summer 2014. Chapter 1 Data and Statistics. Outline – Statistics in the world of business - examples What does business statistics entail? Data set as the object of statistics

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ECON 3790 Statistics for Business and Economics

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  1. ECON 3790 Statistics for Business and Economics Instructor: Ou Hu, Ph.D., CFA Youngstown State University Summer 2014

  2. Chapter 1 Data and Statistics Outline – • Statistics in the world of business - examples • What does business statistics entail? • Data set as the object of statistics • Scales of measurement • Classifications of data and statistics • Data sources

  3. Statistics in the Business and Economy • Examples • The U.S. real GDP has grown about 1.8% in the past decade; • The sales of iPhone accounts for about 25% in the global smartphone market; • In the U.S., a person with 4-year college education earns averagely twice as much as the one with a high school degree does; • By the time the lecture was prepared, the Dow Jones Industrial Average reached its historical highest point of 16167.97 on December 18, 2013.

  4. What does business statistics entail? • Statistics is the art and science of collecting, presenting, analyzing, and interpretingdata. • In business, statistics is not just about numbers and mathematics, but a tool that analyzes the available data and helps make informed and better business decisions.

  5. Data Set as the Object of Statistics • The structure of a data set: • Elements – the entities on which data are collected; • Variables – characteristics of the elements; • Observations – the set of measurements for an element.

  6. Data Set – An Example Variables An Observation Data Set Names of Elements

  7. Scales of Measurement • Data of different scales of measurement require different statistical analyses. • There are four scales of measurement: • Nominal • Ordinal • Interval • Ratio

  8. Nominal Scale • Names or labels that show the attributes of elements, variables such as • Names of companies; • Gender of employees; • Data of nominal scale can be numeric. ( For instance, ‘0’ denotes female and ‘1’ denotes male.)

  9. Ordinal Scale • It has the properties of nominal scale and the order or rank matters. For example, • Customer service rating ( poor, average, good, outstanding); • Variables of ordinal scale can assume numeric data values.

  10. Interval Scale • It has the properties of ordinal scale and the interval/difference between values is measured in the same unit. For example, • Temperatures – oF or oC • SAT scores • Data of interval scale are always numeric. • For interval data, zeros do not mean nothing. For instance, a temperature of 0 degree does not mean there is no temperature.

  11. Ratio Scale • It has the properties of interval scale and the ratio of of two values are meaningful. For example, • Weight, height, distance, time, income, etc. • Data of ratio scale are always numeric. • For ratio data, zeros do mean nothing. For instance, a profit of zero $ means there is no profit.

  12. Q1.1 – Which of the following variables uses the ordinal scale of measurement? • Paint colors • Social Security Numbers • Letter grades (A,B,C,…) • Monthly return of S&P 500 Index

  13. Q1.1 – Which of the following variables uses the ordinal scale of measurement? • Paint colors • Social Security Numbers • Letter grades (A,B,C,…) • Monthly return of S&P 500 Index Answer:c

  14. Classifications of Data • Qualitative vs. Quantitative data • Nominal and ordinal data are qualitative, while interval and ratio data are quantitative. • Mathematical operations don’t apply to qualitative data even if they are numeric. • Cross-Sectional vs. Time Series data

  15. Classifications of Statistics • Descriptive Statistics • To summarize data in an informative way; • Demonstrate patterns; • Use tables, graphs, and numerical measures.

  16. Classifications of Statistics • Inferential Statistics • A population is the entire set of data in a particular study. • Its characteristics are called population parameters. • The study of a population is probably very daunting, time-consuming, and costly. • A sample is a subset of a population. • Its characteristics are called sample statistics. • The study of a sample is much more manageable.

  17. Inferential Statistics To estimate population parameters based on sample statistics Population Parameters Sample Statistics • Sample mean: • Sample variance: • Sample proportion: • Population mean: • Population variance: • Population proportion:

  18. Data Sources • Nowadays, the public can get easy access to business and economics data online. For instance: • Yahoo! Finance • Federal Reserve Economic Data • Census Bureau

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