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MGMT 3101 | MGMT 3101 - Applied Business Statistics, Quizzes of Business Statistics

Class: MGMT 3101 - Applied Business Statistics; Subject: Management; University: Georgia College & State University; Term: Fall 2010;

Typology: Quizzes

2009/2010

Uploaded on 10/05/2010

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TERM 1
sample
DEFINITION 1
In statistics, a sample is a subset of a population.
TERM 2
Elements of Research
Process
DEFINITION 2
Exploration Research Design Conclusion
TERM 3
Explain the element(s) of "Exploration" in the
Research Process
DEFINITION 3
Exploration: Theory Attitudes Beliefs Perception Observation
TERM 4
Elements of "Exploration" are the:
DEFINITION 4
Problems or Opportunity
TERM 5
Explain the element(s) of "Research" in the
Research Process
DEFINITION 5
Research: Hypothesis
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sample

In statistics, a sample is a subset of a population. TERM 2

Elements of Research

Process

DEFINITION 2 Exploration Research Design Conclusion TERM 3

Explain the element(s) of "Exploration" in the

Research Process

DEFINITION 3 Exploration: Theory Attitudes Beliefs Perception Observation TERM 4

Elements of "Exploration" are the:

DEFINITION 4 Problems or Opportunity TERM 5

Explain the element(s) of "Research" in the

Research Process

DEFINITION 5 Research: Hypothesis

Explain the element(s) of "Design" in the

Research Process

Design: Measurement Method Sales, Questions, Observations Data Gathering Tests TERM 7

Explain the element(s) of "Conclusion" in the

Research Process

DEFINITION 7 Conclusion: Employment Reality TERM 8

Employment Reality is known as:

DEFINITION 8 Management Summary TERM 9

Random Variation

DEFINITION 9 In mathematics, a random variable (or stochastic variable) is a way of assigning a value (often, a real number) to each possible outcome of a random event. Therefore, the mean does not equal Mu (u). TERM 10

Inferential Statistics

DEFINITION 10 Statistical inference is the process of drawing conclusions from data that are subject to random variation, for example, observational errors or sampling variation. (all about the distance mean- Mu=0)

Central Limit Theorem

In probability theory, the central limit theorem (CLT) states conditions under which the mean of a sufficiently large number of independent random variables, each with finite mean and variance, will be approximately normally distributed. TERM 17

Point Estimation

DEFINITION 17 In statistics, point estimation involves the use of sample data to calculate a single value (known as a statistic) which is to serve as a "best guess" for an unknown (fixed or random) population parameter. TERM 18

What occurs if we use a large simple random

sample (n

DEFINITION 18 the central limit theorem enables sample distribution of the mean to be approximated by a normal probability distribution. TERM 19

Small (non parametric) simple random

sample(n>30) can only be considered normal

only if:

DEFINITION 19 we assumepopulation has normal probability distribution TERM 20

Take atleast 30 samples where in each

sample we have atleast 3 samples. Resulting:

DEFINITION 20 Distribution of the sample means will be approximately normally distributed

Finite

each sample has the same chance of beig selected TERM 22

What is the probability of the fiite sample

being chosen:

DEFINITION 22 1/N or the Random Number Generator TERM 23

Infinite

DEFINITION 23

  1. All elements come from the same population 2. Selected Independently TERM 24

Population considered infinite if it involves an:

DEFINITION 24 Ongoing Process that makes listing or counting every element impossible TERM 25

Stratified Random Sampling

DEFINITION 25 Population is divided into "groups" of elements called, strata

Cluster Sampling is _____ within, and _____

across. Represents a "______________"

is heterogeneous within, and the same across. Represents a "mini population" TERM 32

Why use cluster sampling?

DEFINITION 32 Close proximity can be cost effective TERM 33

Case when Stratified Random Sampling is

used:

DEFINITION 33 Study of Student Habits TERM 34

Case when Cluster Sampling is used?

DEFINITION 34 Government Surveys TERM 35

Systematic Sampling

DEFINITION 35 Systematic sampling is a statistical method involving the selection of elements from an ordered sampling frame. Select every "pth" element; random start.

Systematic Sampling method i comparison to

Simple Random Sampling:

Method has a proportion of Simple Random Sampling ecspecially if the list of the population elements are a random variable TERM 37

Convenience Sampling

DEFINITION 37 Accidental sampling is a type of nonprobability sampling technique which involves the sample being drawn from that part of the population which is close to hand. Sample is identified primarily by covenience. TERM 38

Advantage and Disadvantage to Convenience

Sampling

DEFINITION 38 Advantage: Sample selection and data collection are relatively easy. Disadvantage: It is impossible to determine how much of a representative of the population the sample is TERM 39

Judgement Sampling

DEFINITION 39 Must be knowledgeable on the subject of the study of selecting sample elements; it is a nonprobability sampling technique. TERM 40

You want the sample to look like the _______

DEFINITION 40 population

Margin of Error

The margin of error is a statistic expressing the amount of random sampling error in a survey's results. Distance * Width TERM 47

Standard Error

DEFINITION 47 Standard deviation (is in units)of the sampling distribution of the mean (it's normally distributed) Width TERM 48

What kind of form do confidence intervals

take?

DEFINITION 48 Confidence intervals take the same form; normally distributed TERM 49

A higher standard deviation causes:

DEFINITION 49 more spread TERM 50

"t" is based on:

DEFINITION 50 degrees of freedom

Why is a t-statistic higher than a z-statistic:

As a sample size goes up it gets closer and closer to z TERM 52

"z" statistic is based on:

DEFINITION 52 number of standard deviations away from the mean TERM 53

The formula for interval estimation of a

population proportion is the:

DEFINITION 53 Standard deviation of the proportion TERM 54

Why use "z" statistic?

DEFINITION 54 You have to have a large binomial from a large sample or the number will be skewed TERM 55

A _____ a mean has a meaning. Give

examples of this:

DEFINITION 55 Variable Examples: MPH, Height, Weight

What is the hypothesised value of Mu:

What we think Mu is TERM 62

Compare the mean to the hypothesised value

of Mu, what do you determine?

DEFINITION 62 If distance is so great the two cannot be the same (statistically)-reject null TERM 63

How are the mean and hypothesised value

compared:

DEFINITION 63 Do this by comparing calculated values (Zcalc--------------

Zcrit) Form a boundary beyond which lies the rejection region TERM 64

Mu is the _______

DEFINITION 64 distance in units TERM 65

Zcrit and Tcrit come from:

DEFINITION 65 alpha

Null Hypothesis:

Tentative assumption about a population parameter (Ho) TERM 67

Alternative Hpothesis:

DEFINITION 67 Not a tentative assumption about a population parameter (Ha) TERM 68

Three different forms of the Hypothesis:

DEFINITION 68

  1. right tail test 2. Left tail test 3: Two tail test TERM 69

Appropriate test statistics:

DEFINITION 69 Mean and Probability TERM 70

Ask if the hypothesised value of Mu is

reasonable:

DEFINITION 70

  1. If it is- fail to reject Ho 2. If it is not-Reject Ho

The t-distribution can:

always be used, whether it's a large n or small n TERM 77

rejecting Null when it's

true=

DEFINITION 77 Alpha (level of significace)-the probability were wrong TERM 78

Rejection Rules of Right Tale Test:

DEFINITION 78 Zcalc>Zcrit TERM 79

Rejection Rules of Left Tale Test

DEFINITION 79 -Zcalc TERM 80

Rejection Rules of The Two Tail

Test:

DEFINITION 80 Either: Zcalc>Zcrit OR -Zcalc

What does the P-value represent:

Area TERM 82

P-value is the:

DEFINITION 82 probability of getting Z or t calc greater than the null (right tail test) When the p-value is smaller than alpha (spreads further from the mean) you can reject the hypothesis, because it's in the rejection region. TERM 83

If you kow sigma you know Mu. Why?

DEFINITION 83 It's in the numerator