Data Analysis and Decision Making-1 Session-2- 1 Understanding Data Session 2
Data Analysis and Decision Making-1 Session-2- 2 Objectives In this chapter you learn: To understand issues that arise when defining variables. How to define variables. To understand the different measurement scales How to collect data. To identify different ways to collect a sample. To understand the issues involved in data preparation.
Data Analysis and Decision Making-1 Session-2- 3 Data Sets, Variables, and Observations A data set is usually a rectangular array of data, with variables in columns and observations in rows. A variable (or field or attribute ) is a characteristic of members of a population, such as height, gender, or salary. An observation (or case or record ) is a list of all variable values for a single member of a population.
Data Analysis and Decision Making-1 Session-2- 4 Example: Partial results from a student survey Gender M for male and F for female Smoke Does the student smoke: yes or no Award Award the student prefers to win: Academy Award, Olympic gold medal, or Nobel Prize Exercise Number of hours spent exercising per week TV Number of hours spent watching television per week GPA Current grade point average on a 4-point scale Pulse Pulse rate in number of beats per minute at the time of the survey Birth Birth order: 1 for first/oldest, 2 for second born, etc.
Data Analysis and Decision Making-1 Session-2- 5 Classifying Variables By Type Categorical ( qualitative ) variables take categories as their values such as “yes”, “no”, or “blue”, “brown”, “green”. Numerical ( quantitative ) variables have values that represent a counted or measured quantity. Discrete variables arise from a counting process . Continuous variables arise from a measuring process . D COVA
Data Analysis and Decision Making-1 Session-2- 6 Examples of Types of Variables D COVA Question Responses Variable Type Do you have a Facebook profile? Yes or No Categorical How many text messages have you sent in the past three days? --------------- Numerical (discrete) How long did the mobile app update take to download? --------------- Numerical (continuous)
Data Analysis and Decision Making-1 Session-2- 7 Types of Variables D COVA Variables Categorical Numerical Discrete Continuous Examples: Marital Status Political Party Eye Color (Defined Categories) Examples: Number of Children Defects per hour (Counted items) Examples: Weight Voltage (Measured characteristics) Nominal Ordinal Examples: Ratings Good, Better, Best Low, Med, High (Ordered Categories)
Data Analysis and Decision Making-1 Session-2- 8 Other types Cross-sectional data are data on a cross section of a population at a distinct point in time.
- Spring '17
- DR. JAYANTHI RANJAN
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