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Department
Economics
Course
EC255
Professor
Alex Lun
Semester
Fall

Description
EC255 Week 1 1.1 STATISTICS IN BUSINES Virtually every area of business uses statistics in decision making, Several examples are as follows: -Marketing -Management -Finance -Operations and Supply Chain Management -Economics -Accounting -Management Information Systems 1.2 BASIC STATISTICAL CONCEPTS Statistics – a science dealing with the collection, analysis, interpretation, and presentation of numerical data Population – a collection of persons, objects, or items of interest Census – gathering data from the whole population for a given measurement o interest Sample – a portion of the whole -The study of statistics can be organized in a variety of was -Statistics can be subdivided into two branches: descriptive statistics and inferential statistics Descriptive Statistics – Using data gathered on a group to describe or reach conclusions about that same group -Descriptive statistics can be used for batting averages, save percentages, and first downs because they describe an individual or team effort Inferential Statistics – Gathering data from a sample and using the statistics generated to reach conclusions about the population from which the sample was taken -The data gathered from the sample are used to infer something about the larger group -Inferential statistics are sometimes referred to as inductive statistics -One application of inferential statistics is in pharmaceutical research. Some new drugs are expensive to produce, and therefore tests must be limited to small samples of patients -Market researchers use inferential stats to study the impact of advertising on various market segments -The advantage to using inferential statistics is that they enable the researcher to effectively study a wide range of phenomena without having to conduct a census Parameter – a descriptive measure of the population Statistic – a descriptive measure of a sample -Differentiation between the terms parameter and statistic is important only in the use of inferential statistics. A business researcher often wants to estimate the value of a parameter or conduct tests about the parameter. However, the calculation of parameters is usually either impossible or infeasible because of the amount of time and money required to take a censure. In such cases, the business EC255 Week 1 researcher can take a random sample of the population, calculate a statistic on the sample, and infer by estimation the value of the parameter. 1.3 DATA MEASUREMENT Nominal Level -Numbers representing nominal-level data can be used only to classify or categorize -Ex. Employee ID numbers, sex, religion, ethnicity, geographic location place of birth, SIN, telephone numbers -Statistical techniques that are appropriate for analyzing nominal data are limited Ordinal Level -Can be used to rank or order data -With ordinal data, the distances between consecutive numbers are not always equal Interval Level -The distances between consecutive numbers have meaning and the data are always numerical -Ex. Temperature -The existence of 0 actually means something with interval level data Ratio Level -Have an absolute zero and the ratio of two numbers is meaningful -The zero value in the data represents the absence of the characteristic being studied -The value of zero cannot be arbitrarily assigned because it represents a fixed point -Ex. Height, mass, time, volume, production cycle time, work measurement time, passenger distance, etc. -Statistical techniques can be separated into two categories: parametric statistics and nonparametric statistics Parametric statistics – require that data be interval or ratio Nonparametric statistics – nominal or ordinal data 2.1 FREQUENCY DISTRIBUTIONS Frequency distribution – a summary of data presented in the form of class intervals and frequencies -Frequency distributions are relatively easy to construct – they vary in shape and design Range – the difference between the largest and smallest number -Select between 5 and 15 classes when making a frequency distribution chart -The frequency distribution must start at a value equal to or lower than the lowest number of the ungrouped data and end at a value equal to or lower than the lowest number and vice versa -Class endpoints are selected so that no value of the data can fit into more than one class EC255 Week 1 Class Midpoint -The midpoint of each class interval is called the class midpoint and is sometimes referred to as the class mark. It is the value halfway across the class interval and can be calculated as the average of the two class endpoints -The class midpoint is important because it becomes the representative value for each class in most group statistics calculations Relative Frequency -The proportion of the total frequency that is in any given class interval in a frequency distribution -It is the individual class frequency dived by the total frequency Cumulative Frequency -A running total of frequencies through the classes of a frequency distribution -It is the frequency for that class internal added to the preceding cumulative total 2.2 GRAPHICAL DEPICTION OF DATA Histogram -A type of vertical bar chat that is used to depict a frequency distribution -It is a useful tool for differentiating the frequencies of class intervals Frequency Polygons -A graph in which line segments “connecting the dots” depict a frequency distribution -It is plotted in the same way that a histogram is Ogives -A cumulative frequency polygon -The use of cumulative frequency values requires that the scale along the y axis be great enough to include the frequency total -A dot of zero frequency is plotted at the beginning of the first class and construction proceeds by marking a dot at the end of each class interval for the cumulative value – connecting the dots completes the ogive -Ogives are useful when the decision maker wants to see running totals Pie Charts -A circular depiction of data where the area of the whole pie repr
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