Introduction to Statistics

1. Cambodian Mekong University MB102 Introduction to Statistics Chapter 1 Introduction to Statistics Prepared by: Mr. Reach Phanith

2. Learning Objectives • Identify and understand various types and applications of statistics • Understand the types of work undertaken by a statistician • Understand statistics in economics and commerce • Recognize various types of data

3. 1. Why Learn Statistics? • Statistics is the branch of mathematics that transforms numbers into useful information for decision makers. • Statisticslets you know about the risks associated with making a business decision and allows you to understand and reduce the variation in the decision-making process. • Statisticsprovides you with methods for making better sense of the numbers used every day to describe or analyze the world we live in.

4. 2. Statistics in Business • In the business world, statistics has these important specific uses: • To summarize business data • To draw conclusions from those data • To make reliable forecasts about business activities • To improve business processes • The statistical methods you use for these tasks come from one of the two branches of statistics: descriptive statistics and inferential statistics.

5. 2. Statistics in Business DESCRIPTIVE STATISTICS Descriptive statistics are the methods that help collect, summarize, present, and analyze a set of data. INFERENTIAL STATISTICS Inferential statistics are the methods that use the data collected from a small group to draw conclusions about a larger group.

6. 2. Statistics in Business DESCRIPTIVE STATISTICS Descriptive statistics are the methods that help collect, summarize, present, and analyze a set of data.

7. 2. Statistics in Business INFERENTIAL STATISTICS Inferential statistics are the methods that use the data collected from a small group to draw conclusions about a larger group.

8. 3. Basic Vocabulary of Statistics VARIABLE A variable is a characteristic of an item or individual. DATA Data are the different values associated with a variable. PARAMETER A parameter is a measure that describes a characteristic of a population. STATISTIC A statistic is a measure that describes a characteristic of a sample.

9. 3. Basic Vocabulary of Statistics POPULATION (N) A population consists of all the items or individuals about which you want to reach conclusions. SAMPLE (n) A sample is the portion of a population selected for analysis.

10. 3. Basic Vocabulary of Statistics POPULATION (N) A population consists of all the items or individuals about which you want to reach conclusions. Example • Names of all registered voters in Cambodia • Incomes of all families living in Phnom Penh • Annual returns of all stocks traded on the New York Stock Exchange • Grade point averages of all the students in Cambodian Mekong University

11. 3. Basic Vocabulary of Statistics SAMPLE (n) A sample is the portion of a population selected for analysis. Random Sampling Simple random sampling is a procedure in which • each member of the population is chosen strictly by chance, • each member of the population is equally likely to be chosen, and • every possible sample of n objects is equally likely to be chosen The resulting sample is called a RANDOM SAMPLE

12. 4. The Decision Making Process

13. 5. Identifying Types of Variables

14. 5. Identifying Types of Variables CATEGORICAL VARIABLES Categorical variables (also known as QUALITATIVE VARIABLES) have values that can only be placed into categories such as yes and no. “Do you currently own bonds?” (yes or no) and the level of risk of a bond fund (below average, average, or above average) are examples of categorical variables.

15. 5. Identifying Types of Variables NUMERICAL VARIABLES Numerical variables (also known as QUANTITATIVE VARIABLES) have values that represent quantities. Numerical variables are further identified as being either discreteor continuous variables.

16. 5. Identifying Types of Variables DISCRETE VARIABLES Discrete variables have numerical values that arise from a counting process. “The number of premium cable channels subscribed to” is an example of a discrete numerical variable because the response is one of a finite number of integers. You subscribe to zero, one, two, or more channels. “The number of items purchased” is also a discrete numerical variable because you are counting the number of items purchased.

17. 5. Identifying Types of Variables CONTINUOUS VARIABLES Continuous variables produce numerical responses that arise from a measuring process. The time you wait for teller service at a bank is an example of a continuous numerical variable because the response takes on any value within a continuum, or an interval, depending on the precision of the measuring instrument.

18. 5. Identifying Types of Variables PRIMARY DATA Primary data are information collected by the person or organisation that will be using the information. • This includes the case of a researcher who wants to undertake statistical analysis involving information that has not yet been collected. SECONDARY DATA Secondary data are information already collected by someone else. • In this case the researcher has little or no control over the design or method of data collection, which may turn out to be unsatisfactory since it may be inappropriate.

19. 6. Measurement Scales NOMINAL SCALE A nominal scale classifies data into distinct categories in which no ranking is implied. Examples of a nominal scaled variable are your favorite soft drink, your political party affiliation, and your gender • Nominal scaling is the weakest form of measurement because you cannot specify any ranking across the various categories.

20. 6. Measurement Scales ORDINAL SCALE An ordinal scale classifies values into distinct categories in which ranking is implied. For example, suppose that Good Tunes & More conducted a survey of customers who made a purchase and asked the question “How do you rate the overall service provided by Good Tunes & More during your most recent purchase?” to which the responses were “excellent,” “very good,” “fair,” and “poor.” The answers to this question would constitute an ordinal scaled variable because the responses “excellent,” “very good,” “fair,” and “poor” are ranked in order of satisfaction.

21. 6. Measurement Scales • Ordinal scaling is a stronger form of measurement than nominal scaling becausean observed value classified into one category possesses more of a property than does an observed value classified into another category. • However, ordinal scaling is still a relatively weak form of measurement because the scale does not account for the amount of the differences between the categories. The ordering implies only which category is “greater,” “better,” or “more preferred”—not by how much.

22. 6. Measurement Scales INTERVAL SCALE An interval scale is an ordered scale in which the difference between measurements is a meaningful quantity but does not involve a true zero point. For example, a noontime temperature reading of 67 degrees Fahrenheit is 2 degrees warmer than a noontime reading of 65 degrees. In addition, the 2 degrees Fahrenheit difference in the noontime temperature readings is the same as if the two noontime temperature readings were 74 and 76 degrees Fahrenheit because the difference has the same meaning anywhere on the scale.

23. 6. Measurement Scales RATIO SCALE A ratio scale is an ordered scale in which the difference between the measurements involves a true zero point, as in height, weight, age, or salary measurements.

24. SUMMARY • You can begin to understand how statistics helps you make better sense of the world. Businesses use statistics to summarize and reach conclusions from data, to make reliable forecasts, and to improve business processes. • You learned some of the basic vocabulary used in statistics and the various types of data used in business. • In the next two chapters, you will study data collection and a variety of tables and charts and descriptive measures that are used to present and analyze data.