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Chapter 1

MGMT 1050 Chapter Notes - Chapter 1: Statistical Inference, Descriptive Statistics, The Technique

Course Code
MGMT 1050
Hila Cohen

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MGMT 1050 Reading Notes: Week 1
Chapter 1: What is Stascs?
Stascs is a way to get informaon from data
Student obtains a list of %nal marks
The student has the data (marks) and needs to apply stascal techniques to get
informaon her requires – descripve stascs
Descriptive Statistics
Deals with methods of organizing, summarizing, and presenng data in a convenient and
informave way
2 forms of descripve stascs
oUses graphical techniques that allow stascs praconers to present data in
ways that make it easy for the reader to extract useful informaon
oUses numerical techniques to summarize data (mean, median etc.)
The technique we use depends on what speci%c informaon we would like to extract
Measure of central locaon – mean, median, mode etc.
Measure of variability – range
Inferential Statistics
Another branch of stascs
A body of methods used to draw conclusions or inferences about characteriscs of
populaon based on sample data
Using results from a sample to apply to the populaon
A random sample of voters who exit the polling booth are asked for whom they voted
1.1 Key Statistical Concepts
Stascal inference problems involve 3 key concepts; the populaon, the sample and
the stascal inference
1.1a Population
Populaon: The group of all items of interest to a stascs praconer
Populaon is frequently very large and may be in%nitely large
Does not necessarily refer to a group of people
Parameter: A descripve measure of a populaon (Ex. The proporon of the populaon
that voted for Candidate B)
1.1b Sample
Sample: A set of data drawn from the studied populaon
Stasc: A descripve measure of a sample
Use stascs to make inferences about parameters (Ex. The proporon of the 900
people surveyed that voted for Candidate B represents the proporon of the populaon
that voted for Candidate B)
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