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BBA182 Applied Statistics Week 2 (1) Types of Data – categorical data Dr Susanne Hansen Saral Email: susanne.saral@okan.edu.tr

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NEW IN CLASS? Send me an email to the following address: susanne.saral@okan.edu.tr

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Activation of piazza.com account Enter your first and last name Select : Undergraduate Select : Economy Select : Class 1 and add BBA 182 and click “join the class”

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Where does data come from? Market research Survey (online questionnaires, paper questionnaires, etc.) Interviews Research experiments (medicine, psychology, economics) Databases of companies, banks, insurance companies Internet other sources

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Random Sampling Simple random sampling is a procedure in which: Each member/item in the population is chosen strictly by chance Each member/item in the population has an equal chance to be chosen Each member/item has to be independent from each other Every possible sample of n objects is equally likely to be chosen The resulting sample is called a random sample.

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Convenience sample A sample where subjects are not chosen strictly by chance. The researchers choses the sample (bias) Advantage to collect a convenience sample: - Convenient, less work load - Fast, provides a fast answer - Provides a trend or indication Disadvantage: - The data collected is not statistically valid and reliable. Cannot draw conclusions about the population based on a convenience sample.

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Data - Information The objective of statistics is to extract information from data so that we can make business decisions that increase company profits As we saw in last class, data can be numbers and data can be categories. Therefore we divide them into different types. Each type requires a specific statistical technique for analysis. To help explain this important principle, we need to define a few terms:

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Variables A variable is any characteristic, number, or quantity that can be measured or counted. Age, gender, business income and expenses, country of birth, capital expenditure, class grades, car model, nationality are examples of variables. They are called variables, because they can vary: Country of birth can vary from person to person, not all class grades are the same, gender can be either female or male. A variable can take on more than one characteristic and therefore is called a variable

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Variables and values (continued) Values of a variable are the possible observations of the variable. Examples: The values of religious orientation: Muslim, Buddhist, Protestant, Catholic, Agnostic, etc. The values of a statistics exam are the integers between 0 and 100 The values of gender: Male or female The size of buildings: 10 – 100 meters tall

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Data = variable - values When we talk about data we talk about observed values of a variable: Example, we observe the midterm exam grades (a variable) of 10 students: 67 74 71 83 93 55 48 81 68 62 From this set of data we can extract information. who - what - when

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Data – observed values of a variable Data = values – information Data can be numbers (quantitative): Number of daily flight departures at Sabiha Gökçen airport, size of a person, number of products sold annually in a store, number of trucks arriving at a warehouse, price of gold, etc. Data can be categories (qualitative): Religious orientation, countries, customer preference, tourist attractions, codes, gender, etc.

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Classification of variables Knowledge about the type of variable we are working with is necessary, because each type of variable requires a different statistical technique. If we use the wrong statistical technique to present data the information we are giving will be misleading.

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Why classify variables? Correctly classifying data is an important first step to selecting the correct statistical procedures needed to analyze and interpret data. Some graphs are appropriate for categorical/qualitative variables, and others appropriate for quantitative/numerical variables

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Classification of Variables

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Categorical/qualitative When the values of a variable are simply names of categories or codes, we call it a categorical or a qualitative variable

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Classification of Variables Categorical/qualitative data – nominal Categorical data generate responses that belong to categories: Responses to yes/no questions: Do you have a credit card? What are the different academic departments of IYBF faculty? ( IR, Logistics, Business Administration, etc. ) Transportations means (truck, ship, plane, etc.) Product codes, country codes (0090 for Turkey), postal codes (34730 Göztepe, Istanbul), ID numbers, telephone number, number on a football players’ shirt, etc. The responses produce names, words or codes and are therefore called nominal data

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Classification of Variables Categorical/qualitative data – Ordinal Ordinal data includes an ordered range of choices, such as : strongly disagree – disagree – indifferent – agree - strongly agree or large-medium-small Example: Size of a T-shirt: Small – medium - large How do you rate the quality of meals in OKAN cafeterias on a scale from 1 to 5? Where 1 = Very bad 5 = very good How do you rate the latest Star Wars movie «Rouge One» on a scale from 1 to 5? Where 1 = very boring 5 = very entertaining

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Classification of Variables

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Classification of Variables Numerical/quantitative data Many variables are quantitative: Price of a product, quantity of a product and time spent on a website, are all quantitative values with units. For quantitative variables, units such as TL or $, kilogram, minutes, liter or degree Celsius tell us the scale of measurement. Without units, the values of measurement have no meaning. Example: It does little good to be promised a salary increase of 5000 a year if you do not know whether it is paid in EUROS, TL or kilograms of rice

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Classification of Variables

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Classification of Variables Numerical/quantitative data For quantitative variables, units such as TL or $, kilogram, minutes, liter or degree Celsius tell us the scale of measurement. Without units, the values of measurement have no meaning. An essential part of a quantitative variable is it’s units!

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Classification of Variables Numerical/quantitative data – discrete Discrete variables are countable. They represent whole numbers – integers: Examples: Number of trucks leaving a warehouse between 8:00 – 8:30 hours Number of different nationalities living in Turkey in February 2017 Number of cars crossing the Bosphorus bridge in one day

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Classification of Variables Numerical data – continuous Continuous variables may take on any value within a given range or interval of real numbers….and units are attached to continuous variables Examples: The age of a building, 14 years (14 – 15 years) Temperature of a day in February in Istanbul, 6 degrees ( -1 – 10 degrees) Distance travelled by car in one day, 55 km ( 54.30 – 55.64 km)

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For each of the following, identify the type of variable (categorical or numerical) the responses represent: Do you own a car? _______________________________________________________ The number of newspapers sold per day in a shop_______________________________ How would you rate the quality of the service you received in the restaurant? (poor, fair, good, very good, excellent) _________________________________________________ The age of car?_________________________________________________________ How tall are the trees in the park? ____________________________________________ Rate the availability of parking spaces: (Excellent, good, fair, poor)________________ Number of newspaper subscriptions__________________________________________ The average annual income of employees in a company___________________________ Have you ever visited Berlin, Germany? _______________________________________ What is your major in the university? _________________________________________

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Classification of Variables

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Graphical Presentation of Categorical Data Data in raw form are usually not easy to use for decision making We need to make sense out of the data by some type of organization: Frequency Table - to compress and summarize the data Graph - to make a picture and present the data

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Raw data – data that is not yet organized Example: Football World cup champions (1930 – 2014) Year Champions Year Champions 1930 Uruguay 1974 W. Germany 1934 Italy 1978 Argentina 1938 Italy 1982 Italy 1950 Uruguay 1986 Argentina 1954 W. Germany 1990 W. Germany 1958 Brazil 1994 Brazil 1962 Brazil 1998 France 1966 England 2002 Brazil 1970 Brazil 2006 Italy 2010 Spain 2014 Germany

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Tables and Graphs for Categorical Variables

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Organizing categorical data Categorical data produce values that are names, words or codes, but not real numbers. Only calculations based on the frequency of occurrence of these names, words or codes are valid. We count the number of times a certain value occurs and add the frequency in the table.

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The Frequency and relative frequency - Distribution Table Summarizing categorical data A frequency table organizes data by recording totals and category names. The variable we measure here is the number of times a country became world champion in football: