Statistics Vocab

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larry.gish89
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Statistics Vocab
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2013-08-23 19:58:26
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Business Stats 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.

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