DESCRIPTIVE ANALYSIS BY R

1. Fundamentals of Business Statistics
Descriptive Statistics

2. Data types
Data types
Categorical
Numeric
Discrete
Counting
process
Continuous
Measuring
process
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3. Data Measurement Scales
Nominal
• Used only for
labeling data
Ordinal
• Numbers to rank
objects or
attributes
• Distance between
objects/attributes
cannot be
measured
Interval
• Distance between
objects/attributes
can be measured
• Have arbitrary
zero point
• Basic arithmetic
operations
possible
Ratio
• Highest level of
measurement
scale
• Have fixed zero
point
• All arithmetic
operations
possible
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4. Agenda
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Descriptive Statistics
• Graphical Representation
• Tabular Representation
• Summarization
Inferential Statistics
• Conditional Probability
• Bayes’ Theorem
• Random Variables: Mean
and Variance
• Binominal Distribution
• Poisson Distribution
• Normal Distribution

5. Graphical representation
Numerical Categorical
Univariate
Multivariate
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6. Tabular representation
Freq
distribution
Table that displays the frequency of outcomes in a data
sample.
Each entry in the table contains frequency or count of
occurrences of values within a particular group or
interval.
Cross
tab
Allows comparison between two or more variables
basis multiple parameters.
For example, Pivot table in excel.
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7. Frequency distribution
• Absolute frequency distribution
• Relative frequency distribution
• Cumulative frequency distribution
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8. Roadmap for descriptive measures
Type of analysis Numerical data Categorical data
Tabulate, organize,
graphically present values
of a variable
Freq distribution,
histogram
Summary table, bar chart
Graphically represent
relationship between 3
variables
Scatter plot Contingency table
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9. Summarization
Central tendency
• Mean
• Median
• Mode
Variation
• Range
• Interquartile
range
• Variance
• Standard
deviation
• Coef of variation
Skewness
• Symmetrical
• Left skewed
• Right skewed
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10. Skewness
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Positive skew or
left skew
Symmetrical Negative skew
or right skew

11. Mean, median & extreme values - example

12. Mode
• Most frequently bought item decided based on mode
• Important for categorical data

13. Interquartile range
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14. Significance of SD compared to variance
• Why do we take sum of squares for variance and then find
sqrt for SD?
• Why is SD more useful than variance?
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15. Five number summary & Boxplot
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16. Outlier formula
• < Q1 – 1.5IQR
• > Q3 + 1.5IQR
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17. Coefficient ofVariation
Data1 Data2
SD 292 292
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Mean 536 3336
CoV 54% 8.7%

18. Example problems in R
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19. Questions?
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