# INTRODUCTION TO STATISTICS - PowerPoint PPT Presentation

PPT – INTRODUCTION TO STATISTICS PowerPoint presentation | free to download - id: 68a16b-MmI4N

The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
Title:

## INTRODUCTION TO STATISTICS

Description:

### INTRODUCTION TO STATISTICS INTRODUCTION (1) In early 1987, the US Food and Drug Administration (FDA) was faced a unprecedented situation. Thousands of people were ... – PowerPoint PPT presentation

Number of Views:117
Avg rating:3.0/5.0
Slides: 29
Provided by: AndiKo8
Category:
Tags:
Transcript and Presenter's Notes

Title: INTRODUCTION TO STATISTICS

1
INTRODUCTION TO STATISTICS
2
INTRODUCTION (1)
• In early 1987, the US Food and Drug
Administration (FDA) was faced a unprecedented
situation. Thousands of people were dying of
acquired immunodeficiency syndrome (AIDS).
• Not only was there no known, but there was not
even a drug available to slow the developmental
of the disease.
• Early clinical trials of an experimental
antiviral drug known then as azidothymidine (AZT)
were promising
• Only 1 of 145 AIDS patients on AZT had died,
compared to 19 of 137 patients in a control
groups given a placebo.

3
INTRODUCTION (2)
• There were medical questions remaining to be
answered. What was the optimal dose? For how long
would the drug continue to thwart the virus?
• There was also an important statistical question,
and ethical questions could be addressed. Was the
fewer number of deaths among AIDS patients using
AZT the result of the drug, or was it due just to
chance?

4
INTRODUCTION (3)
• Statistical test showed that the differences
between the two groups was so great that the
probability of their having occurred by chance
was less than one in a thousand (Fischl et al.,
1987).
• Armed with these statistics, the FDA gave final
approval of the use of AZT in March of 1987,
after only 21 months of testing

5
What is STATISTICS?
• A set of mathematical procedure for organizing,
summarizing, and interpreting information
(Gravetter, 2004)
• A branch of mathematics which specializes in
enumeration data and their relation to metric
data (Guilford, 1978)
• Any numerical summary measure based on data from
a sample contrasts with a parameter which is
based on data from a population (Fortune, 1999)
• etc.

6
Two General Purpose of Statistics (Gravetter,
2007)
1. Statistic are used to organize and summarize the
information so that the researcher can see what
happened in the research study and can
communicate the result to others
2. Statistics help the researcher to answer the
general question that initiated the research by
determining exactly what conclusions are
justified base on the result that were obtained

7
DESCRIPTIVE STATISTICS
• The purpose of descriptive statistics is to
organize and to summarize observations so that
they are easier to comprehend

8
INFERENTIAL STATISTICS
• The purpose of inferential statistics is to draw
an inference about condition that exist in the
population (the complete set of observation) from
study of a sample (a subset) drawn from population

9
SOME TIPS ON STUDYING STATISTICS
• Is statistics a hard subject?
• IT IS and IT ISNT
• In general, learning how-to-do-it requires
attention, care, and arithmetic accuracy, but it
is not particularly difficult.
• LEARNING THE WHY OF THINGS MAY BE HARDER

10
SOME TIPS ON STUDYING STATISTICS
• Some parts will go faster, but others will
• Work enough of questions and problems to feel
comfortable
• What you learn in earlier stages becomes the
foundation for what follows
• Try always to relate the statistical tools to
real problems

11
POPULATIONS and SAMPLES
THE POPULATION is the set of all the individuals
of interest in particular study
The sample is selected from the population
The result from the sample are generalized from
the population
THE SAMPLE is a set of individuals selected from
a population, usually intended to represent the
population in a research study
12
PARAMETER and STATISTIC
• A parameter is a value, usually a numerical
value, that describes a population.
• A parameter may be obtained from a single
measurement, or it may be derived from a set of
measurements from the population
• A statistic is a value, usually a numerical
value, that describes a sample.
• A statistic may be obtained from a single
measurement, or it may be derived from a set of
measurement from sample

13
SAMPLING ERROR
• It usually not possible to measure everyone in
the population
• A sample is selected to represent the population.
By analyzing the result from the sample, we hope
to make general statement about the population
• Although samples are generally representative of
their population, a sample is not expected to
give a perfectly accurate picture of the whole
population
• There usually is some discrepancy between sample
statistic and the corresponding population
parameter called sampling error

14
TWO KINDS OF NUMERICAL DATA
• Generally fall into two major categories
• Counted ? frequencies ? enumeration data
• Measured ? metric or scale values ? measurement
or metric data

Statistical procedures deal with both kinds of
data
15
DATUM and DATA
• The measurement or observation obtain for each
individual is called a datum or, more commonly a
score or raw score
• The complete set of score or measurement is
called the data set or simply the data
• After data are obtained, statistical methods are
used to organize and interpret the data

16
VARIABLE
• A variable is a characteristic or condition that
changes or has different values for different
individual
• A constant is a characteristic or condition that
does not vary but is the same for every
individual
• A research study comparing vocabulary skills for
12-year-old boys

17
QUALITATIVE and QUANTITATIVE Categories
• Qualitative the classes of objects are different
in kind.
• There is no reason for saying that one is
greater or less, higher or lower, better or worse
than another.
• Quantitative the groups can be ordered according
to quantity or amount
• It may be the cases vary continuously along a
continuum which we recognized.

18
DISCRETE and CONTINUOUS Variables
• A discrete variable. No values can exist between
two neighboring categories.
• A continuous variable is divisible into an
infinite number or fractional parts
• It should be very rare to obtain identical
measurements for two different individual
• Each measurement category is actually an interval
that must be define by boundaries called real
limits

19
CONTINUOUS Variables
• Most interval-scale measurement are taken to the
nearest unit (foot, inch, cm, mm) depending upon
the fineness of the measuring instrument and the
accuracy we demand for the purposes at hand.
• And so it is with most psychological and
educational measurement. A score of 48 means from
47.5 to 48.5
• We assume that a score is never a point on the
scale, but occupies an interval from a half unit
below to a half unit above the given number.

20
FREQUENCIES, PERCENTAGES, PROPORTIONS, and RATIOS
• Frequency defined as the number of objects or
event in category.
• Percentages (P) defined as the number of objects
or event in category divided by 100.
• Proportions (p). Whereas with percentage the base
100, with proportions the base or total is 1.0
• Ratio is a fraction. The ratio of a to b is the
fraction a/b.
• A proportion is a special ratio, the ratio of a
part to a total.

21
MEASUREMENTS and SCALES (Stevens, 1946)
Ratio
Interval
Ordinal
Nominal
22
NOMINAL Scale
• Some variables are qualitative in their nature
rather than quantitative. For example, the two
categories of biological sex are male and female.
Eye color, types of hair, and party of political
affiliation are other examples of qualitative or
categorical variables.
• The most limited type of measurement is the
distinction of classes or categories
(classification).
• Each group can be assigned a number to act as
distinguishing label, thus taking advantage of
the property of identity.
• Statistically, we may count the number of cases
in each class, which give us frequencies.

23
ORDINAL Scale
• Corresponds to was earlier called quantitative
classification. The classes are ordered on some
continuum, and it can be said that one class is
higher than another on some defined variable.
• All we have is information about serial
arrangement.
• We are not liberty to operate with these numbers
by way of addition or subtraction, and so on.

24
INTERVAL Scale
• This scale has all the properties of ordinal
scale, but with further refinement that a given
interval (distance) between scores has the same
meaning anywhere on the scale. Equality of unit
is the requirement for an interval scales.
• Examples of this type of scale are degrees of
temperature. A 100 in a reading on the Celsius
scale represents the same changes in heat when
going from 150 to 250 as when going from 400 to
500

25
INTERVAL Scale
• The top of this illustration shows three
temperatures in degree Celsius 00, 500, 1000. It
is tempting to think of 1000C as twice as hot as
500.
• The value of zero on interval scale is simply an
arbitrary reference point (the freezing point of
water) and does not imply an absence of heat.
• Therefore, it is not meaningful to assert that a
temperature of 1000C is twice as hot as one of
500C or that a rise from 400C to 480C is a 20
increase

26
INTERVAL Scale
• Some scales in behavioral science are measurement
of physical variables, such as temperature, time,
or pressure.
• However, one must ask whether the variation in
the psychological phenomenon is being measured
indirectly is being scaled with equal units.
• Most measurements in the behavioral sciences
cannot posses the advantages of physical scales.

27
RATIO Scale
• One thing is certain Scales the kinds just
mentioned HAVE ZERO POINT.

28
Confucius, 451 B.C
• What I hear, I forget
• What I see, I remember
• What I do, I understand