Introduction to Statistics Descriptive Statistics by q8a1ML

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									  Introduction to Statistics
       Descriptive Statistics

Statistics Terminology
Scales of Measurements
Measures of Central
Tendencies
Skewness
Measures of Variability
  Statistics: The Course
 Descriptive       Inferential
 Statistics         Statistics
Deals with         Deals with data
 describing data    collected from
 that has been      samples so one
 collected.         can generalize
                    to populations.
Populations vs Samples
 Population        Sample
A population       A sample is a
 consists of all    subset of a
 members of         population. It
 some specified     has the same
 group.             characteristics
                    as the
                    population.
Parameters vs Statistics
 Parameters          Statistic
A measure of a       A measure of a
 characteristic of    characteristic
 an entire            of a sample.
 population.
 Parameters vs Statistics
In research, we obtain information about
population parameters by using
information from samples that represent
the population.
We apply the laws of probability to the
data collected from the samples to
generalize to the population..
    Populations versus Samples
 Population – all the people or things
  involved in a particular study
 Sample – a subset of a population
 Samples are used to estimate the population
  value of some parameter.
 A population characteristic under study is a
  population parameter. It is estimated
  using a sample statistic.
      Measures of Central
         Tendencies
What is happening around the center of the
                  data?
 Mean: the arithmetic average
 Median: the point where 50% of the
 data is above and 50% is below
 Mode: occurs most frequently
              The Mode

 The value that occurs most frequently
 Is the simplest of the central tendencies.
 No computation is required.
 Very unstable information about the data
 Only average that can be used with
  nominal data.
             The Median

 The point at which 50% of the data is
  above and 50% is below
 Data is sorted and middle point is found.
 Is not sensitive to extreme scores.
 It is not appropriate to average median
  scores.
                  The Mean

   The arithmetic average
   Add the values (data) and divide by the
    number of values
   Takes into account the value of every data
    point.
   Is considered the most stable central tendency.
     Measures of Variability

 Variabilitymeasures describe the
 distribution of the data.
 Examples:
    Range
    Standard Deviation
              The Range

 Simplest measure of variability
 (Highest value minus the lowest value)
  plus 1
 Unreliable because it is based on only two
  values
    The Standard Deviation

 Very useful measure of variability
 Shows the difference between a raw score
  and the mean of a set of data.
 All the data is included in its
  computation.
              Skewness
 Describes visually the distribution of
                the data.
Examples:
 Symmetrical distribution: two halves
 of data are mirror images.
 Skewed distribution: Data is pulled
 by extreme scores to the left or right.
Symmetrical Distribution
6
5
4
3
2
1
0
    1   2   3   4   5   6   7
       Skewed Distributions
        Hint: The tail points to the skew.

Negatively Skewed           6
                            5
                            4

Median right of mean        3
                            2
                            1
                            0
More high scores                1   2   3       4       5       6       7




                            6
                            5

Positively Skewed           4
                            3
                            2

Median left of mean         1
                            0
                                1   2   3   4       5       6       7

More low scores
The End

								
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