# Statistics Vocab

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1. Statistics:
• The
• science that deals with the analysis and classification of empirical data.  It also attempts to draw conclusions based on
• past or present experience.
2. Population
• The
• totality of all data studied.
3. Sample
• A
• subset of data drawn from a population.
4. Parameter
• A numerical measurement referring to a
• population.
5. Statistic:
A numerical measurement referring to a sample.
6. Census:
The collection of data from every member of the population.
7. Variable
• It
• assumes different values. We use letters to indicate variables.
8. Constant
• It
• has a fixed value. It is the opposite of variable.
9. Random Variable
• A
• variable that assumes values depending on chance.
10. Discrete
Variable
• It assumes a finite number of values, or if it
• assumes infinitely many values, the values can be counted using the counting
• numbers 1, 2, 3 … etc.
11. Continuous
Variable
• It assumes infinitely many values that cannot be
• counted. There are no gaps between the values.
12. Scales
or levels of measurement: Nominal
• Non-numerical data such as names, labels, categories,
• etc. They cannot be ordered.
13. Scales
or levels of measurement:

Interval
• Like ordinal but differences make sense.  There is no natural starting point, i.e.
• there is no zero.  Ratios are
• meaningless.  For example body
• temperatures.
14. Scales
or levels of measurement:

Ordinal
• They can be ordered, but differences either they
• cannot be determined or they are meaningless, i.e. rating movies using stars.
15. Scales
or levels of measurement: Ratio
• This
• is the highest level of measurement for numerical data. There is a zero
• starting point and differences and ratios are meaningful.
16. Types
of data:
Categorical or
Qualitative:
• Non-numerical
• data, i.e. color, party or religious affiliation, etc.

• Categorical
• data use either the nominal or ordinal scale of measurement.
17. Types
of data:
Quantitative or Numerical:
• Numerical
• data i.e. test scores, incomes figures, etc.

• Quantitative
• data use either the interval or ratio scale of measurement.
18. Types
of data:
Categorical
variable
• :  A variable with
• categorical data.

• Statistical analysis for categorical variables is
• limited to summarizing the data by category of computing the proportion of the
• observations in each category.
19. Types
of data:
Quantitative
variable
• : A variable with
• numerical data.

• The data can be manipulated mathematically and the
• results are meaningful. For example we can add the data and divide by the
• number of observations to arrive at the average value.
20. Types
of data:
Cross-sectional
data
Data collected at one point in time.

• For example a media research company calls up 5,000
• households at random to determine the proportion of households tuned to NBC to
• watch the opening ceremony of the 2012 Olympic Games.
21. Types of data: Time
series data
Data collected at regular intervals over time.

• Typical measuring points are months, for example monthly unemployment figures for the last three
• years, quarters, for example company
• quarterly reports for the last two years etc.
• The best way to represent time series data is by a
• line graph.
22. For statistical studies first we must identify what we want to study. This is referred to as...
The variable of interest
23. Observational statistics:
We observe and measure specific characteristics, but we do not attempt to control or modify the subjects being studied. A Gallup poll is an example of an observational study.
24. Understand
all key terms
25. Experimental statistics:
• We conduct an experiment or as we say in Statistics,
• we apply some treatment and then we observe its effects on the subjects. (The
• subjects are usually called experimental units).
• Pharmaceutical companies conduct such experiments
• when they test new drugs.
26. One of the two parts of statistics: Descriptive Statistics
This part of Statistics attempts to summarize or describe the important characteristics of a set of data.

• Methods of summarizing data include tables, pictures such as bar charts, pies, histograms, frequency polygons,
• line-charts, etc, and numbers that
• measure a specific characteristic of the data. For example, the mean or average
• measures the center of a set of data.
27. One of the two parts of statistics: Inferential
Statistics or Statistical Inference
• This part of Statistics attempts to make inferences or draw conclusions or generalizations
• about a large population, based on a sample drawn from that population.

• The tools used are based on Probability and Probability
• Distributions and are extremely sophisticated.

• The methods used in Statistical Inference have solid
• Mathematical foundation and they will yield valid results provided of course
• that the sample is representative of the population.

• So the weak link in Statistical Inference is the sample and the sample size. Obviously a biased sample will yield unreliable
• results.

• How to choose a “good” sample is a science in
• itself.

28. Methods of sampling:Random sampling
Each member from the population has an equal chance of being selected.
29. Methods
of sampling:
Simple random
sample of size n
• Every
• possible sample of the same size n has an equal chance of being selected.

• Notice
• that there is difference between a random sample and a simple random sample.
30. Methods
of sampling:
Stratified
• Divide
• the population into sub-populations or strata, and then draw a sample from each
• stratum.

• Note: If the sample
• selected from each stratum is a random sample, then this procedure, first
• stratification and then random sampling is called stratified random sampling. This is a subgroup of stratified
• sampling.
31. Methods
of sampling:
Systematic
• Choose
• a starting point then select a specified element, say the kth
• element.
32. Methods
of sampling:Cluster
• Divide
• the population into sections or clusters, choose a few clusters at random, and
• then perform a census within each
• selected cluster. This means select all
• the elements from the chosen clusters.

• A
• special case of cluster sampling is area
• sampling, where the clusters are geographic subdivisions.
33. Methods
of sampling:
Convenient
• Just
• choose data readily and conveniently available. This does not yield
• statistically valid results.
34. Methods
of sampling:
Voluntary
Response
• A voluntary response sampling is one in which the
• respondents themselves decide whether to be included or not.

• Such
• a sample is flawed and should not be used for making general statements about a
• population.
35. Methods
of sampling:
Multistage Sampling
Sampling schemes that combine several sampling methods are called multistagesamples.
 Author: larry.gish89 ID: 231095 Card Set: Statistics Vocab Updated: 2013-08-23 23:58:26 Tags: Statistics gish Folders: Description: Business Stats vocab Show Answers: