2. Preface
What is Statistics?
Statistics is not just about analyzing the data.
It’s about the whole process of using the
scientific method to answer questions and make
decisions. That process involves
designing studies, collecting good data,
describing the data with numbers and graphs,
analyzing the data, and then making
conclusions.
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3. What is Statistics?
Statistics is a way to get information from data
data”
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Data
Statistics
Information
Data: Facts, especially
numerical facts, collected
together for reference or
information.
Information: Knowledge
communicated concerning
some particular fact.
Statistics is a tool for creating new understanding from a set of numbers.
4. Why do you analyse data?
1- To choose the correct statistical technique.
2- to compute the statistics.
3- to interpret the statistical results.
Stage 2 can be done manually to help students to
understand techniques and concepts, or using Excel,
Minitab, or SPSS.
Stage 1&3 provide students with practical skills to
apply to real problems.
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5. • Selecting the right technique depends on the
problem objective & data type.
• We are going to focus on how the techniques
and concepts introduced are applied to real-
world problems.
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6. Descriptive Statistics
It deals with methods of organizing,
summarizing, and presenting data in a
convenient and informative way, using
graphical techniques that make it easy to
extract useful information, or numerical
techniques to summarize data.
Selecting the appropriate technique depends on what
specific information we would like to extract.
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7. Inferential Statistics
It is a body of methods used to draw conclusions
or inferences about characteristics of
populations based on sample data.
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8. Key Statistical Concepts…
•
Population
— A population is the group of all items of interest
to a statistics practitioner.
— frequently very large; sometimes infinite.
E.g. All 5 million Florida voters can vote on election day
_ A descriptive measure of a population is called a
parameter.
_ In most applications, the parameter represents
the information we need.
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9. •
Sample
— A sample is a set of data drawn from the population.
— Potentially very large, but less than the population.
E.g. a sample of 765 voters who exit the polling
booth on an election day.
_ A descriptive measure of a sample is called a statistic.
_ We use statistics to make inferences about
parameters.
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11. Statistical Inference…
Statistical inference is the process of making an
estimate, prediction, or decision about a population
based on a sample data.
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Parameter
Sample
Statistic
Inference
What can we infer about a Population’s Parameters
based on a Sample’s Statistics?
12. • This can be done with a measure of reliability
called the confidence level, which is the
proportion of times that an estimating
procedure will be correct.
• Or the significance level, which measures how
frequently the conclusion will be wrong in the
long run.
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13. Types of Data and Information
• A variable is some characteristic of a
population or a sample.
• The values of the variable are the possible
observations of the variable.
• Data are the observed values of a variable.
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15. Types of Data and Information
Nominal Data ( also called categorical or
qualitative)
Such as marital status, gender,......
We often record nominal data by arbitrarily
assigning a number to each category (codes)
to store this type of data.
Male 1
Female 2
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16. Interval data ( quantitative or numerical)
Are real numbers, such as weights, heights, and
incomes.
Ordinal Data appear to be nominal, but their numerical
codes are in order.
Because the codes are arbitrarily assigned, we cannot
calculate and interpret the differences.
When assigning codes to the values, we can use any set
of codes , but we should maintain the order of the
values.
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17. Examples of Ordinal data
• Degree of illness: none, mild, moderate, acute,
chronic.
• Opinion of students about Stat. classes:
Very unhappy, unhappy, neutral, happy, Very happy.
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18. Calculations for Types of Data
Interval Data :values are real numbers, all
calculations are valid.
Ordinal data : The only permissible calculations
are ones involving the ranking process .
Nominal Data : values are the arbitrary numbers
that represent categories to be used to store
data. Only calculations based on the
frequencies of occurrence are valid( to count
the occurrence of each category).
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19. Hierarchy of Data
• The data types can be placed in order of the
permissible calculations. Higher-level data
may be treated as lower-level ones, but not
the vice versa.
Interval data
Ordinal data
Nominal data
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20. Types of Variables
• The variables whose observations
constitute our data will be given the
same name as the type of data.
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