2. What is “Statistics”?
•
•
•
•
•
•
•Statistics is the science of data that involves:
•Collecting
•Classifying
•Summarizing
•Organizing and
•Interpretation
•
•
•
•
•
•
•
Of numerical information.
•Examples:
•Cricket batting averages
•Stock price
•Climatology data such as rainfall amounts, average temperatures
•Marketing information
•Gambling?
3. Key Terms
• What is Data?
facts or information that is relevant or
appropriate to a decision maker
• Population?
•the totality of objects under consideration
• Sample?
•a portion of the population that is selected
for analysis
4. Key Terms
• Parameter?
a summary measure (e.g., mean) that is
computed to describe a characteristic of
the population
• Statistic?
a summary measure (e.g., mean) that is
computed to describe a characteristic of
the sample
5. Variables
• Traits or characteristics that can change
values from case to case.
• A variable is what is measured or
manipulated in an experiment
•Examples:
•Age
•Gender
•Income
•Social class
6. Types Of Variables
• In causal relationships:
• CAUSE =>EFFECT
independent variable & dependent variable
•Independent variable: is a variable that can be
controlled or manipulated.
An independent variable is the variable you have control
over (dose of drug)
•Dependent variable: is a variable that cannot be
controlled or manipulated. Its values are predicted
from the independent variable ( effect on the
condition)
7. Types Of Variables
•Discrete variables are measured in units
that cannot be subdivided. Example:
Number of children
•Continuous variables are measured in a
unit that can be subdivided infinitely.
Example: Height
9. Descriptive Statistics
•Gives us the overall picture about data
•Presents data in the form of tables, charts and
graphs
•Includes summary data
•Avoids inferences
Examples:
•Measures of central location
Mean, median, mode and midrange
•Measures of Variation
•Variance, Standard Deviation, z-scores
10. Inferential Statistics
•Take decision on overall population using a
sample
• “Sampled” data are incomplete but can still
be representative of the population
•Permits the making of generalizations
(inferences) about the data
• Probability theory is a major tool used
to analyze sampled data
11. Predictive Modeling
• The science of predicting future outcomes
based on historical events.
• Model Building: “Developing set of
equations or mathematical formulation to
forecast future behaviors based on current
or historical data.”
• Regression, logistic Regression, time
series analysis etc.,
12. Calculation of the probability
• Based on the characteristics of the
population for the observed parameter
• (e.g. . Duration of the pregnancy, duration
of the first labor stage, height, et cetera)
• To describe the population, “distribution”
will be used
13. Distribution
• A statistical distribution describes the
numbers of times each possible outcome
occurs in a sample
• Distributions for continuous variables are
called continuous distributions ( e.g.
height)
• They also carry the fancier
name probability density
14. Distribution
• Some probability densities have particular
importance in statistics. A very important
one is shaped like a bell, and called
the normal ( Gaussian) distribution.
• Many naturally-occurring phenomena can
be approximated surprisingly well by this
distribution. It will serve to illustrate some
features of all continuous distributions.
16. What are the Components of A
Distribution?
• Measures of central tendency
• Suppose we have a sample with 4
observations: 4, 1, 4, 3
• Mean = the sum of a set of numbers divided
by the number of observations
(4+1+4+3=12:4=3)
Median - the middle point of a set of
numbers(3.5)
17. Components of distribution
• Mode - the most frequently occurring
number. Mode=4
• Median - the middle point of a set of
numbers(3.5)
18. Components of distribution
Measures of variation
Range - the maximum value minus the
minimum value in a set of numbers.
Range = 4-1 = 3
Standard Deviation - the average
distance a data point is away from the
mean.
[ (4- 3)+( 1 -3)+ (4- 3)+ (3- 3)]: 4=1
standard deviation= 1
20. Why to know about it ?
• Mean, Median, Mode, Range, and
Standard Deviations are measurements in
a sample (statistics) and can
also be used to make inferences on a
population.
21. What do we expect from the
statistical analysis?
• To find out whether there is a statistically
significant difference between our sample
(e.g. pregnancy loss in Al Ain Hospital
Patient) and general population
22. How to perform the statistical
analysis?
• Statistics can take us to a beautiful journey
of understanding ,but
