Q.M SAMENVATTING:

Chapter 2:

Observation: A single member of a collection of items that we want to study such as person, firm or a region. Variable: A characteristic of the subject or individual, such as an eployee’s income or an invoice amount. Data set: Consists all the values of all of the variables for all the observations we have chosen to observe. Univariate data set: one variable. Bivariate data set: two variables. Multivariate data set: More than two variables. Data Types: Categorical Data: Have values that are describes by words rather than numbers.

Verbal Label: Example – Vechile Type (Car, Truck, SUV).

Coded(Binary): Example – Vechile type (1, 2, 3). Numerical Data: Arise from counting, measuring something, or some kind of mathematical operation.

Discrete: Example – Broken eggs in a carton (1,2,3,4…N).

Continuous: Example – Patient waiting time (14.22 Min). Time Series Data: If each observation in the sample represents a different equally spaced point in time. The periodicity is the time between observations (mothly, weekly, yearly, etc.). Cross-­‐Sectional Data: If each observation represents a different individual unit (e.g., a person, firm, geographic area) at the same point of time. For cross-­‐sectional data we are intersted in variation among observations. For example; GPAs of students in a statistics class. Lebels of measurement: Nominal Data: The only thing a nominal scale does is to say that items being measured have something in common, although this may not be described. Nominal items may have numbers assigned to them. This may appear ordinal but is not -­‐ these are used to simplify capture and referencing. Nominal items are usually categorical, in that they belong to a definable category, such as 'employees'. Ordinal Data: A ranming of data values, but the difference between values is unknown. Example – Moody’s bond ratings: Aaa, Aa, A, Baa, Ba, Caa. Interval Data: Data is not only ranked but also has meaningful intervals between scale points. Example – Temperature in Celsius or Fahrenheit – 10, 20, 30. Ratio Data: Have all the properties of the other datya types, but in addition possess a meaningful zero that represents the absence of quantity being measured. Example – 20million profit is twice as much as 10million profit. Rule of Thumb: When the sample is less that 5% of the population(n/N