基础统计学英文版

1、基础统计学英文版Chapter OneWhat is Statistics?ONEUnderstand why we study statistics.TWOExplain what is meant by descriptive statistics and inferential statistics.THREEDistinguish between a qualitative variable and a quantitative variable.FOUR Distinguish between a discrete variable and a continuous variable

2、.FIVEDistinguish among the nominal, ordinal, interval, and ratio levels of measurement.SIXDefine the terms mutually exclusive and exhaustive.GOALSWhen you have completed this chapter, you will be able to:2Why study statistics?Numerical info is everywhereBut how do we know if conclusions reported are

3、 accurate?Statistical techniques are used to make decisions that affect our livesThis is why younger people pay more for insuranceKnowledge of statistical methods at least helps you understand why decisions are made In future you will make decisions that involve data3What is Meant by Statistics?Stat

4、istics is the science of collecting, organizing, presenting, analyzing, and interpreting numerical data to assist in making more effective decisions. In common usage statistics refers to numerical information. But in this course the term has a wider meaning.4Who Uses Statistics?Statistical technique

5、s are used extensively by managers in marketing, accounting, quality control, consumers, professional sports people, hospital administrators, educators, politicians, physicians, gamblers, etc.5Types of StatisticsDescriptive Statistics: Methods of organizing, summarizing, and presenting data in an in

6、formative way.EXAMPLE 1: A Gallup poll found that 49% of the people in a survey knew the name of the first book of the Bible. The statistic 49 describes the number out of every 100 persons who knew the answer.6Types of StatisticsDescriptive Statistics: Methods of organizing, summarizing, and present

7、ing data in an informative way.EXAMPLE 2: According to Consumer Reports, General Electric washing machine owners reported 9 problems per 100 machines during 2002. The statistic 9 describes the number of problems out of every 100 machines.7Types of StatisticsDescriptive Statistics: Methods of organiz

8、ing, summarizing, and presenting data in an informative way.EXAMPLE 3: The Canadian government reports that the population of Canada was 18,238,000 in 1961, 21,568,000 in 1971, 24,820,000 in 1981, 28,031,000 in 1991, and 31,050,700 in 2001. If we calculate percentage growth over the decades it is al

9、so descriptive statistics.8Types of Statistics Inferential Statistics: The methods used to determine something about a population, based on a sample.O EXAMPLE 1: In the preceding example on Canadian population changes, if you use the past data to forecast the population of Canada in the year 2010 or

10、 expected percentage of growth from 2000 to 2010, then this is considered inferential statistics.9Types of Statistics Inferential Statistics: The methods used to determine something about a population, based on a sample.EXAMPLE 2: The accounting department of a large firm will select a sample of the

11、 invoices to check for accuracy for all the invoices of the company.10Types of Statistics Inferential Statistics: The methods used to determine something about a population, based on a sample.EXAMPLE 3: Wine tasters sip a few drops of wine to make a decision with respect to all the wine waiting to b

12、e released for sale. 11Population vs. SamplePopulation is the entire set of individuals or objects of interest or the measurements obtained from all individuals or objects of interest.Sample is a portion, or part, of the population of interest12See also p.7Population: All itemsSample: Items selected

13、 from the population13NB: dont confuse population in statistics with a countrys population!A population might consist of all the people in Nanaimo but also may mean the PE ratios for all chemical stocks, or total assets of the 20 largest banks in North America, total collection of prices, ages, squa

14、re footage of retail space in Nanaimo, and so on.14Types of Variables For a Qualitative or Attribute variable the characteristic being studied is nonnumeric.EXAMPLES: Gender, religious affiliation, type of automobile owned, country of birth, eye colour are examples. 15Types of VariablesIn a Quantita

15、tive variable information is reported numerically.EXAMPLES: balance in your chequing account, minutes remaining in class, or number of children in a family. 16Types of VariablesQuantitative variables can be classified as either discrete or continuous.Discrete variables: can only assume certain value

16、s and there are usually “gaps” between values.EXAMPLE: the number of bedrooms in a house, or the number of hammers sold at the local Home Depot (1,2,3,etc). But you cannot have 2.3 bedrooms or 10.6 hammersThus discrete variables result from counting.17Types of VariablesA continuous variable can assu

17、me any value within a specified range. Examples are: The pressure in a tire, the weight of a pork chop, or the height of students in a class. Typically, continuous variables are the result of measuring something.18Summary of Types of Variables19Levels of MeasurementData may be classified into four c

18、lasses or levels of measurement: nominal, ordinal, interval, and ratio.Nominal level: Data that is classified and counted. EXAMPLES: eye colour, gender, religious affiliation.20Levels of MeasurementMutually exclusive: An individual, object, or measurement is included in only one category.Exhaustive:

19、 Each individual, object, or measurement must appear in one of the categories.Nominal data have no particular order or rank and are mutually exclusive.21Marital Status 2000 (population 15 years and older)StatusNumberSingle (never married)7,285,560Married (legally, separated, common-law)14,614,564Div

20、orced1,452,000Widowed1,527,075Total24,879,199These data are a nominal level of measurement because it can only be classified into classes and the order of the marital status is not important. The classes are also mutually exclusive and exhaustive.22Levels of MeasurementOrdinal level: involves data arranged in some order, but the differences between data values cannot be determined or are meaningless.EXAMPLE: During a taste test of 4 soft drinks, Mountain Dew was