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Clare Chua
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Quantitative Methods

QMS 102

Clare Chua

Fall

Description

1. Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making effective decisions.
What are data?
2. Data collection is a process of gathering information using questionnaires, interviews, experiments and field study.
3. In business world information is usually gathered using questionnaires.
4. Data are a collection of numbers and/or attributes of an entity.
Data Collection
5. Data maybe collected from two main sources, primary or secondary.
6. the primary data involves raw data or original data that are collected directly from the respondents using various instruments such as interviews,
surveys, observations and laboratory experiments.
7. the secondary data are data that have been collected by another party or source.
8. secondary data is also recognized as "recycled" data.
Definition of Measurement Scales
9. Nominal- a qualitative data that have no particular order or ranking to the categories. marital status, sports, color of your eye, postal code, car brand,
country.
10. Ordinal- a qualitative data that have a natural order to the categories. we can say that one category is higher or better than another. For ex:
professorial rank, rating of a service, letter grades, medals awarded in Olympic games.
11. Interval- it is the data that has units of measurement. In this case the value "0" is only an arbitrary reference point, a value of zero doesn't mean there
is no amount of the characteristic being measured. For ex: temperature, calendar scale.
12. Ratio- the "0" value does mean the absence of the characteristic being measured, meaning 0=nothing. For example of discrete ratio data: # f vocations
you have taken in the past 10 years, # of part-time employees in your company, # of DVD movies you owned. Examples of continuous ratio data: size of
your house (in square meters), gas price (in cents), sales (in dollars), distance (in km).
13. Classification of Data
CLASSIFICATION OF DATA
Types of Data Qualitative (Categorical) Quantitative (Numerical)
Measurement Scale Nominal Ordinal Interval Ratio
Numerical data value Continues/ Continues/
Discrete Discrete
Stem-and-Leaf Plot
14. The purpose of plotting a stem-and-leaf plot is to summarize the distribution (or shape) of a set of quantitative data and at the same time still retain
most of the data values.
15. The advantages can be that it can be quickly constructed by hand.
16. It also helps you to find out the maximum and the minimum values, and the most frequently occurring value.
17. You must have a title for your stem-and-leaf plot.
Rules and conventions to construct a Stem-and-Leaf Plot
18. rules for stems: -the number of stems should be from 6-13 stems.
-the stem values should be consecutive numbers or repeated numbers.
-the numbers may each be repeated twice or 5 times.
-the stem units must be indicated if stem not to be taken at face value.
-there must be at least on leaf associated with the first and the last stem.
rules from leaves: -the leaf for each data value is the next single digit after the stem.
-when the stems are repeated twice, the leaves values 0-4 go to the first stem and the leaves
values 5-9 go to the second stem. This order is reversed when the stem are negative values.
-when the stems are repeated 5 times, the leaves values 0-1 go to the first stem, values 2-3 go to
the second stem, values 4-5 go to the third stem, values 6-7 go to the fourth stem, and values 8-9
go to the fifth stem. This order is reversed when the stems are negative values.
-there is no rounding off.
-the leaf values are written in ascending order.
-the values must be evenly spaced.
-there are no commas o dashes between the numbers are allowed.
-for the negative stem values, leaves are arranged in the descending order . better way
How to construct stem-and-leaf plots
19. 1. Sort the Annual Return data in ascending order
2. Find the maximum and minimum values for ex: 0.79
9.80
3. Split the minimum and maximum values into stem-and-leaf. 0.7 9

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