2. • Types of Data
• Descriptive Statistics
• Inferential Statistics
• Statistical Data Analysis Application
3. • To have knowledge or idea in statistical tests/tools.
• To know when to use each of the statistical tools.
• To know how to differentiate descriptive and
inferential statistics
• To know how to interpret the statistical output.
• To solve for the appropriate number of samples.
• To apply the statistical analysis tools in your
respective field of work.
4. • Is derived from the Latin word “STATUS”,
meaning “state”.
• Is the science of collecting, classifying,
organizing, summarizing, analyzing, and
interpreting data in order to draw
conclusions or make decisions
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8. • Deals with the collection and presentation
of data and collection of summarizing
values to describe its group characteristics
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12. MEAN
• The arithmetic average of a distribution
MEDIAN
• The middle value that separates the higher
values and the lower values equally
MODE
• The most frequently occurring value
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26. STANDARD DEVIATION
• A measure of dispersion around the mean
VARIANCE
• The square of the standard deviation
MINIMUM
• The lowest value
MAXIMUM
• The highest value
RANGE
• The difference between maximum and minimum
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36. - Measures the symmetry of a distribution
• Symmetric – not skewed (mean=median=mode)
• Positively Skewed – skewed to the right; long
right tail (mean>median>mode)
• Negatively Skewed – skewed to the left; long left
tail (mean<median<mode)
37. Two distributions have the same skewness value of 1.0
The standard error for the first distribution is 0.49; while the second
has 0.51.
Which distribution may be considered symmetric?
Which distribution may be considered skewed?
RULE:
• If skewness > 2 * standard error, then skewed.
• If skewness ≤ 2 * standard error, then symmetric.
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39. - Measures the extent to which observations cluster
around a central point
• Mesokurtic – normal distribution
• Platykurtic – cluster less than a normal
distribution; flatter peak around the mean and fat
tails
• Leptokurtic – cluster more than a normal
distribution; higher peak around the mean and
thin tails
40. RULE:
• |kurtosis| ≤ 2 * standard error, then mesokurtic
• |kurtosis| > 2 * standard error, then:
platykurtic if kurtosis is negative (-)
leptokurtic if kurtosis is positive (+)
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42. • Deals with predictions and inferences
based on the analysis and interpretation of
the results of the information gathered by
the statistician.
46. NOMINAL DATA
• Numbers in the variable are used only to classify
the data. In this level of measurement, words,
letters, and alpha-numeric symbols can be used.
Examples:
Gender
Marital Status
Measure of Central Tendency: Mode
47. ORDINAL DATA
• Has all the characteristics of the nominal level
and it has ordering information.
Examples:
Tenure
Position
Likert Scales
Measure of Central Tendency: Median and Mode
49. INTERVAL DATA
• It does not only classify and order the measurements,
but it also specifies the distances between each
interval on the scale are equivalent along the scale
from low interval to high interval.
Examples:
Average Handle Time (AHT)
Temperature
IQ Score
Measure of Central Tendency: 3Ms
50. RATIO DATA
• It has all the characteristics of an interval level
and it can have absolute zero value.
Examples:
Grades
Scores in Test
NAT
Absenteeism
Measure of Central Tendency: 3Ms
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94. Cronbach’s Alpha is a measure of internal consistency, that is, how
closely related a set of items are as a group.
