This document provides an introduction to statistics. It defines statistics as the science of data that involves collecting, classifying, summarizing, organizing, and interpreting numerical information. It outlines key terms such as data, population, sample, parameter, and statistic. It describes different types of variables like independent and dependent variables. It discusses descriptive statistics, inferential statistics, and predictive modeling. Finally, it explains important concepts like measures of central tendency, measures of variation, and statistical distributions like the normal distribution.

1. Introduction to
statistics
By Dr. Amira Talic

2. What is “Statistics”?
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•Statistics is the science of data that involves:
•Collecting
•Classifying
•Summarizing
•Organizing and
•Interpretation
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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

8. Statistical analysis
• Descriptive Statistics
• Inferential statistics
• Predictive modeling

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.

15. Gaussian distribution

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

19. Standard deviation

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

23. Festina lente! make haste slowly