商业应用统计英文版ch01

1、Applied Statistics for Business,Dr xxx Associate Professor School of Economics and Management xxxx,Applied Business Statistics, 7th ed.by Ken Black,Chapter 1 Introduction to Statistics,What is statistics? Become aware of the varied applications of statistics in business. Differentiate between descri

2、ptive and inferential statistics. Identify types of variables.,Learning Objectives,Science of gathering, presenting, analyzing, and interpreting data Uses mathematics and probability Branches of statistics: Descriptive graphical or numerical summaries of data Inferential making a decision based on d

3、ata,What is Statistics?,Statistics in Business,Accounting auditing and cost estimation Economics local, regional, national, and international economic performance Finance investments and portfolio management Management human resources, compensation, and quality management Management Information Syst

4、ems performance of systems which gather, summarize, and disseminate information to various managerial levels Marketing market analysis and consumer research International Business market and demographic analysis,Statistics in Business,Inferences about parameters made under conditions of uncertainty

5、(which are always present in statistics) Uncertainty can be caused by Randomness in selection of a sample lack of knowledge about the source of the inferences change in conditions not accounted for,Statistics in Business,Probability is used in statistics To estimate the level of confidence in a conf

6、idence interval To calculate the p-value in hypothesis testing,Population the whole a collection of all persons, objects, or items under study Census gathering data from the entire population Sample gathering data on a subset of the population Use information about the sample to infer about the popu

7、lation,Population Versus Sample,Population,Population and Census Data,Sample and Sample Data,Parameter vs. Statistic,Parameter descriptive measure of the population Usually represented by Greek letters Statistic descriptive measure of a sample Usually represented by Roman letters,Process of Inferent

8、ial Statistics,Variable, Measurement and Data,Variable- a characteristic of any entity being studied that is capable of taking on different values. Measurement- a standard process which is used to assign numbers to particular attributes or characteristics of a variable. Data- recorded measurements.,

9、Nominal In nominal measurement the values just name the attribute uniquely. No ordering of the cases is implied. For example, a persons gender is nominal. It doesnt matter whether you call them boys vs. girls or males vs. females or XY vs. XX chromosomes. Another example is religion Catholic, Protes

10、tant, Muslim, etc.,Levels of Data Measurement,Nominal Level Data,Numbers are used to classify or categorize Example: Employment Classification 1 for Educator 2 for Construction Worker 3 for Manufacturing Worker,Ordinal - A variable is ordinal measurable if ranking is possible for values of the varia

11、ble. For example, a gold medal reflects superior performance to a silver or bronze medal in the Olympics. You cant say a gold and a bronze medal average out to a silver medal, though. Preference scales are typically ordinal how much do you like this cereal? Like it a lot, somewhat like it, neutral,

12、somewhat dislike it, dislike it a lot.,Levels of Data Measurement,Ordinal Level Data,Numbers are used to indicate rank or order Relative magnitude of numbers is meaningful Differences between numbers are not comparable Example: Ranking productivity of employees Example: Position within an organizati

13、on 1 for President 2 for Vice President 3 for Plant Manager 4 for Department Supervisor 5 for Employee,Faculty and staff should receive preferential treatment for parking space.,Ordinal Data,Interval - In interval measurement the distance between attributes does have meaning. Numerical data typicall

14、y fall into this category For example, when measuring temperature (in Fahrenheit), the distance from 30-40 is same as the distance from 70-80. The interval between values is interpretable.,Levels of Data Measurement,Interval Level Data,Interval Level data - Distances between consecutive integers are

15、 equal Relative magnitude of numbers is meaningful Differences between numbers are comparable Location of origin, zero, is arbitrary Vertical intercept of unit of measure transform function is not zero Example: Fahrenheit Temperature Example: Monetary Utility,Ratio in ratio measurement there is alwa

16、ys a reference point that is meaningful (either 0 for rates or 1 for ratios) This means that you can construct a meaningful fraction(or ratio) with a ratio variable. In applied social research most count variables are ratio, for example, the number of clients in past six months.,Levels of Data Measu

17、rement,Highest level of measurement Relative magnitude of numbers is meaningful Differences between numbers are comparable Location of origin, zero, is absolute (natural) Vertical intercept of unit of measure transform functionis zero Examples: Height, Weight, and Volume Example: Monetary Variables, such as Profit and Loss, Revenues, Expenses, Financial ratios - such as P/E Ratio, Inventory Turnover, and Quick Ratio.,Ratio Level Data,Data Level, Operations, and Statistical Methods,Two Categories of Statistics Techniques,According to the levels of data to be analyzed, statistics techn