Chapter One: What is Statistics? PDF

Chapter One What is Statistics? GOALS 1. Understand why we study statistics. 2. Explain what is meant by descriptive statistics and inferential statistics. 3. Distinguish between a qualitative variable and a quantitative variable. 4. Distinguish between a discrete variable and a contin...

Chapter One What is Statistics? GOALS 1. Understand why we study statistics. 2. Explain what is meant by descriptive statistics and inferential statistics. 3. Distinguish between a qualitative variable and a quantitative variable. 4. Distinguish between a discrete variable and a continuous variable. 5. Distinguish among the nominal, ordinal, interval, and ratio levels of measurement. 6. List different approaches and tools for collecting data. Chap 1-1  What is Meant by Statistics? Statistics is the science of collecting, organizing, presenting, analyzing, and Interpreting numerical data (making inferences) to assist in making more effective decisions. Chap 1-2  Who Uses Statistics? Statistical techniques are used extensively by Economists Marketers Accountants Quality control people Consumers Professional sports people Hospital administrators Educators Politicians Physicians Chap 1-3  Who Uses Statistics? In the business community, managers must make decisions based on what will happen to such things as demand costs profits These decisions are important to shape the future of the organization. If the managers make no effort to look at the past and extrapolate into the future, the likelihood of achieving their goals becomes less. Chap 1-4  Why do we need to understand Statistics? We are constantly deluged with statistics in the media (newspapers, magazines, journals, text books, etc.). We need to condense large quantities of information into a few facts or figures. We need to predict what will likely occur given what has occurred in the past. We need to generalize what we have learned in specific situations to the more general case. Chap 1-5  Types of Statistics Descriptive Statistics: Methods of organizing, summarizing, and presenting data in an informative way. Inferential Statistics: A decision, estimate, prediction, or generalization about a population, based on a sample. A population is a collection of all possible individuals, objects, or measurements of interest. A sample is a portion, or part, of the population of interest. Chap 1-6  Types of Statistics (examples of descriptive and inferential statistics) Examples of descriptive statistics: 1. In our class, the average score in the Stat Analysis quiz1 is 3.5. 2. According to Consumer Reports, General Electric washing machine owners reported 9 problems per 100 machines during 2001. Examples of inferential statistics: 1. The accounting department of a large firm will select a sample of the invoices to check for accuracy for all the invoices of the company. 2. Based upon a sample of voters, the A party is likely to get 30% to 40% of votes in the coming elections. Chap 1-7  Types of Variables For a Qualitative variable the characteristic being studied is nonnumeric in nature. Qualitative variables can be classified as either nominal or ordinal. nominal: attributes or names which can not be ordered. ordinal: attributes or names which can be ordered according to some logic. Chap 1-8  Types of Variables Examples of nominal data Examples of ordinal data Gender (female/male). Grades of students in a Religious affiliation. Math course (A/B/C …). Type of automobile owned. Educational level. Place of birth. Satisfaction level for a service. Eye color. the order of runners finishing a race. Sometimes we convert qualitative variables to numbers for convenience of calculating summary statistics. For example, Yes may be coded 1, No may be coded 0. But the coding does not change the nature of the variable. Chap 1-9  Types of Variables In a Quantitative variable information is reported numerically. Quantitative variables can be classified as either discrete or continuous. Discrete variables: can only assume certain values and there are usually “gaps” between values. A continuous variable can assume any value within a specified range. Chap 1-10  Types of Variables Examples of discrete data Examples of continuous data The number of bedrooms in Weight. a house. Height. The number of car accidents Age. per year (1,2,3,…,etc). Income. The number of students in a class. Qualitative or Nominal non-numeric Ordinal Data Quantitative or Discrete numeric Continuous Chap 1-11  Levels of Measurement There are four levels of data. 1. Nominal level 2. Ordinal level 3. Interval level 4. Ratio level Nominal level: Data that is classified into categories and cannot be arranged in any particular order. Ordinal level: involves data arranged in some order, but the differences between data values cannot be determined or are meaningless. Chap 1-12  Levels of Measurement Interval level: similar to the ordinal level, with the additional property that meaningful amounts of differences between data values can be determined. There is no natural zero point. Temperature on the Time of day on a 12-hour Fahrenheit scale. clock. Chap 1-13  Levels of Measurement Ratio level: the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement. Hours spent on studying Monthly income of per week. surgeons in US dollars. Weight in kilograms. distance (in kilometers) Height in centimeters. traveled by sales representatives per month. Chap 1-14  Collecting data Before collection of data , a decision maker needs to: Determine the type of analysis needed. Determine what data are required. There are two basic approaches for collecting data: Census: collecting data for all items in the population under study. Sampling: collecting data only for some items which represent the population under study.  Collecting data Sources of data (tools for collecting data): Primary Data Experimental Design Survey Direct Observation Secondary Data governmental or industrial reports.