Introduction to statistics
Abdiweli Hassan
Master of Research and statistics
Lecturer Vision International College
WHAT IS STATISTICS?
Data
Science
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Collection
Summarization
Presentation
Interpretation
Use
• Testing
• Estimating
• Predicting
Associ
ations
Variation
Account
• Random errors
• Systematic errors
BRANCHES
1 Descriptive statistics
2 Inferential statistics
ROLES IN RESEARCH
1 Choice of appropriate design
2 Conduct of a research
3 Analysis of results
BASIC CONCEPTS
1
Scales of measurement
2
Populations Vs samples
3
Variables
4
Statistic Vs parameter
SCALES OF MEASUREMENT
1
2
Nominal
e.g. ethnicity, nationality, gender
Ordinal
e.g. disease severity (mild, moderate,
severe)
3 Interval
e.g. temperature
in Celsius
4
Ratio
e.g. weight, height
POPULATIONS AND SAMPLES
1
2
Population
- Entire collection of objects
- Subset of a collection without the intent
to generalize to the whole
Sample
- Subset of a whole with the intention
to generalize the results to the whole
POPULATION
SAMPLE
VARIABLES
1
Types of variables
- Numerical
- Categorical
Data
Science
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Collection
Summarization
Presentation
Interpretation
UNDERSTANDING DATA
1
2
Components of data
- Observations
- Variables
Types of data
- Numerical
- Continuous
- Discrete
- Categorical
- Nominal
- Ordinal
4
Analysis
- Descriptive
- Predictive
- Prescriptive
5
Interpretation
- Conclusions
- decision-making
Data
Science
SUMMARIZING DATA
1
Measures of central tendency
- Mean
- Median
2 Measures of spread
- mode
- Quantiles
- Percentiles
- Standard deviation
- Interquartile range
- Range
- Variance
- Coefficient of variation
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Collection
Summarization
Presentation
Interpretation
Data
Science
DATA PRESENTATION
1
2
Tables
- Frequency
- Frequency distributions
- cumulative frequency distributions
- Cross tabultions
Graphs
- Histograms
- Frequency polygon
- Box and whisker plot
- Scatter plot
- Stem-leaf plot
- Pie chart
- Bar chart
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Collection
Summarization
Presentation
Interpretation
Data
Science
WHICH PLOT TO USE WHEN
• Categorical (C) versus categorical (C):
barcharts
• Quantitative (Q) versus quantitative
(Q): scatterplots
• Quantitative (Q) versus categorical (C):
boxplots.
• Categorical (C) versus quantitative (Q):
boxplots.
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Collection
Summarization
Presentation
Interpretation
Data
Science
CATEGORICAL DATA
Example: The housing variable
with three categories (for free,
own and rent).
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Collection
Summarization
Presentation
Interpretation
Data
Science
NUMERICAL DATA
Example: Comparing two
groups of children: one
reporting respiratory
symptoms and the other not
reporting
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Collection
Summarization
Presentation
Interpretation
THANKS
Questions
1. What is the difference between data and
information?
- Data = facts and figures
- Information = processed data which is
meaningful