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ams 102 > Chapter 1 Vocabulary > Flashcards

Chapter 1 Vocabulary Flashcards

1
Q

Parameter

A

Numerical measurement describing some characteristic of a population

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2
Q

Population

A

EVERY individual in a group of interest

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3
Q

Population mean

A

µ (mu)

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4
Q

Population standard deviation

A

σ (sigma)

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5
Q

Population proportion

A

π (pi)

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6
Q

Statistic

A

Numerical measurement describing some aspect of a sample

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7
Q

Sample

A

Any subset of a population (not everyone)

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8
Q

Sample mean

A

x̄ (x-bar)

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9
Q

Sample standard deviation

A

s

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10
Q

Sample proportion

A

p̂ (p-hat)

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11
Q

Inferential statistics

A

Sample statistics used to make inferences about a population parameter

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12
Q

Quantitative data

A

Numerical number representing counts, measurements, quantities etc.

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13
Q

What are some examples of quantitative data?

A
  • Weights of athletes
  • Ages
  • Anything that you can take an average of
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14
Q

Categorical data

A

Names or labels of categories

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15
Q

What are some examples of categorical data?

A
  • Gender
  • Shirt numbers of professional athletes
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16
Q

Discrete data

A

Quantitative data values that are finite (countable)

17
Q

What are some examples of discrete data?

A
  • Number of tosses of a coin before getting tails
  • Number of pennies
18
Q

Continuous data

A

Infinitely many possible quantitative values

19
Q

What are some examples of continuous data?

A
  • Lengths of distances from 0 to 12
  • Scales of measurement
20
Q

What are the 4 scales of measurement?

A
  1. Nominal
  2. Ordinal
  3. Interval
  4. Ratio
21
Q

Nominal

A
  • Data cannot be arranged in some order
  • Ex: names, label, categories
22
Q

Ordinal

A
  • Data can be arranged in some order but differences between data values are meaningless
  • Ex: Course grades A, B, C, D
23
Q

Interval

A
  • Data is arranged in order and the differences between data values is meaningful however, there is no neutral 0 (starting point)
  • Ex: Years
24
Q

Ration

A
  • Data can be arranged in order, differences can be found and are meaningful, there is a natural 0 (starting point)
  • You can divide the numbers and get a value
  • Ex: number of sibling that a person has
25
Q

Simple random sample

A

Sample of n subjects selected so that every individual has the same chance of being chosen

26
Q

Systematic sampling

A

Choose a starting point and select every kth element in the population (assuming random order)

27
Q

Convenience sampling

A
  • Utilizing data that is very easy to get
  • This is not a statistically sound method of data collection
  • Voluntary response sampling
28
Q

Why is convenience sampling a bad sampling method?

A

You will get results from extremes of a population

29
Q

Stratified sampling

A
  • Divide the population into groups
  • Subjects within the same group must have the same characteristics (ex: men and women)
  • Draw a sample from each group
30
Q

What is the purpose of stratified sampling?

A

To reduce variation within a sample

31
Q

Cluster sampling

A
  • Divide the population area into sections (clusters)
  • Randomly select some clusters and choose all the members from the selected cluster
  • Differences between groups must be small
  • Differences within groups must be large
32
Q

Multistage sampling

A

Combination of basic sampling methods with multiple stages. Each stage uses a different sampling method.

33
Q

Random sampling Error

A
  • Sample was selected with random method but there is a discrepancy between sample result and true population result
  • Ex: 2016 election (Trump vs. Clinton)
34
Q

Non-random sampling error

A

Utilizing a non-random sampling method

35
Q

Non-sampling error

A

Result of human error including
- Wrong data entry
- Computing error
- Questions with based writing
- False data provided by respondents

36
Q
A

ams 102 (1 decks)

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