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QMS 102 (186)
Lecture

Business Statistics 1 (QMS 102) Chapter 1

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Department
Quantitative Methods
Course
QMS 102
Professor
Jason Chan
Semester
Fall

Description
Business Statistics – intro Variable – characteristic of an item or an individual (eg. Plane trip in 6 months yes or no?) Population – entire group that you are concerned about (eg. Sears store -> concerned about people that shop at Eaton centre. Sample – representation of the population; sample of the population Parameter – the description of numeric value that goes with the population (eg. Class average ) Statistic – what the sample tells you (percentage) draws conclusion in the population (how much you intend to spend at eaton centre) Branches of data Descriptive stats – describes the data you got; makes decision to rank the data in order, tells a story Inferential stats – how to make conclusion from the stats. From small trying to make a judgement from large Types of Data Categorical – eg. Colour of your car (how many?) Nominal – no order in categorical Ordinal – it is in some order in categorical Categorical can be represented in 2 ways either nominal or ordinal Quantitative – eg. What is your GPA in university – two types discrete and continuous Discrete – can only be in 1 solid number (eg. Mark A, B, C, D, F) Continuous – can be ranged in decimals or data points (eg. Height 1.75cm) Interval – temperature Ratio – hours of study (4 hours of study is twice as much 2 hours studied) Categorical data ordinal could be graphed in bar charts Revi
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