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Unformatted text preview: Chapter 1 Describing Data ¾ Data Data is a set of numeric observations. Sources of data: x Government statistical agencies (Statistics Canada) x Stock market activity x Surveys Statistical methods can be used to summarize the information in the data. 1E con 325 – Chapters 1, 2 ¾ Population and Sample The population is the complete set of numeric observations. A sample is an observed subset of observations taken from the population. A random sample contains a set of observations that are representative of the population. The data for the population may not be available for research work. Economic data sets may be viewed as a sample from the population. The challenge is to use the sample to make statements about the behaviour of population. Some degree of uncertainty must be recognized since the sample does not contain the same information as the population. What types of analysis can be done with a sample of data ? x Graphical presentation of data gives a visual display. x Statistics give numerical summary measures of the information in the data. 2E con 325 – Chapters 1, 2 Chapter 1.2 Variables A variable is any characteristic of a population or sample that is of interest to study. Data are the numerical observations. Example A data set contains facts about a sample of 79 companies selected from the Forbes 500 list for the year 1986. The variables in the data set are: 1.Amount of assets (in millions $) 2.Amount of sales (in millions $) 3.Number of employees (in thousands) 4.Sector code: 1 Energy 2 Transportation 3 Communication 4 HiTech 5 Finance 6 Retail 7 Manufacturing 8 Medical 9 Other 3E con 325 – Chapters 1, 2 The numeric observations for the first 20 companies in the data set are: Assets Sales Employees Sector code 2687 1870 18.2 9 13271 9115 143.8 9 13621 4848 23.4 1 3614 367 1.1 5 6425 6131 49.5 2 1022 1754 4.8 4 1093 1679 20.8 7 1529 1295 19.4 9 2788 271 2.1 5 19788 9084 79.4 3 327 542 2.8 5 1117 1038 3.8 1 5401 550 4.1 5 1128 1516 13.2 7 1633 701 2.8 1 44736 16197 48.5 5 5651 1254 6.2 1 5835 4053 10.8 1 278 205 3.8 8 5074 2557 21.9 3 4E con 325 – Chapters 1, 2 Classification of Variables ¾ Quantitative variables Continuous – the numeric observations can take any value in some interval. Example: for the company data set assets, sales and number of employees are all continuous variables. Discrete – the numeric observations can take a limited number of values. Example: consider a survey of students in Economics 325. The student age in years is a discrete variable with typical values 18, 19, 20, 21 etc. ¾ Qualitative or categorical variables – responses do not have a numerical meaning. Example 1: for the company data set the sector code is a categorical variable. Example 2: for a survey of students in Economics 325 categorical variables include gender (Male/Female), mode of transportation to UBC (bus, car, bicycle, walk), etc. 5E con 325 – Chapters 1, 2 Units of measurement for quantitative variables x Levels Example 1: for the company data set assets is reported...
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This note was uploaded on 01/28/2011 for the course ECON 325 taught by Professor Whistler during the Spring '10 term at UBC.
 Spring '10
 WHISTLER
 Economics

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