ENGINEERING DATA ANALYSIS
ENGINEERING DATA ANALYSIS
ENGINEERING DATA ANALYSIS
ENGR. JUDE ROVIN B. SANTILLAN
ENGINEERING
DATA
ANALYSIS
ENGINEERING DATA ANALYSIS
is the process of using mathematical and statistical methods to
examine and interpret data in order to solve engineering problems. In
simple terms, it's about taking the numbers and information you collect
during an engineering project, analyzing them to understand patterns,
trends, or relationships, and then using that understanding to make
decisions or improve designs.
ENGINEERING DATA ANALYSIS
STATISTICS
is the science concerned with the techniques and processes of
gathering, presentation, analysis, and interpretation of data.
ENGINEERING DATA ANALYSIS
GATHERING
PRESENTATION
ANALYSIS
INTERPRETATION
DATA COLLECTION OR GATHERING
Data collection involves methodically acquiring and analyzing information from
different sources to answer research questions, evaluate results, and anticipate
trends and probabilities. Data gathering methods include quantitative techniques
like surveys, experiments, and observational studies to collect structured data.
Qualitative approaches involve interviews, focus groups, and case studies for
detailed insights. Mixed methods combine both types, while other methods such
as historical data collection and data mining offer additional ways to analyze
information.
ENGINEERING DATA ANALYSIS
GATHERING
ENGINEERING DATA ANALYSIS
GATHERING
PRESENTATION
ANALYSIS
INTERPRETATION
DATA PRESENTATION
Involves organizing and displaying information using
charts, graphs, and tables to clearly communicate
findings and insights. The goal is to make complex data
easily understandable and actionable.
ENGINEERING DATA ANALYSIS
PRESENTATION
ENGINEERING DATA ANALYSIS
GATHERING
PRESENTATION
ANALYSIS
INTERPRETATION
DATA ANALYSIS
is the process of inspecting, cleaning, and interpreting data to uncover
meaningful patterns, trends, and insights. It involves applying statistical and
computational techniques to transform raw data into actionable information for
decision-making.
ENGINEERING DATA ANALYSIS
ANALYSIS
ENGINEERING DATA ANALYSIS
GATHERING
PRESENTATION
ANALYSIS
INTERPRETATION
DATA INTERPRETATION
involves examining and making sense of analyzed data to draw meaningful
conclusions and insights or finding and relating existing theories (should be
related to your RRL or Review of Related Literature) and earlier studies. It is the
process of explaining the results of data analysis, identifying trends, and
understanding implications in the context of the research or business objectives.
ENGINEERING DATA ANALYSIS
INTERPRETATION
ENGINEERING DATA ANALYSIS
GATHERING
PRESENTATION
ANALYSIS
INTERPRETATION
ENGINEERING
DATA
ANALYSIS
VARIETIES OF STATISTICAL METHODS AND
TECHNIQUES
ENGINEERING DATA ANALYSIS
DESCRIPTIVE
INFERENTIAL
DESCRIPTIVE ANALYSIS
involves summarizing data using measures like mean, median, and
mode, along with assessing variability through range and standard
deviation. It also includes visual tools like histograms and bar charts
to provide a clear overview of the data’s main features.
ENGINEERING DATA ANALYSIS
DESCRIPTIVE
DESCRIPTIVE ANALYSIS
Mean: The average value of a dataset,
calculated by summing all the values and
dividing by the number of values.
Example 1:
Dataset: 11, 3, 9, 5, 7
Mean = (11 + 3 + 9 + 5 + 7) / 5 = 35 / 5
=7
ENGINEERING DATA ANALYSIS
DESCRIPTIVE
DESCRIPTIVE ANALYSIS
Median: The middle value in a dataset when the values are
arranged in ascending or descending order. If there is an even
number of values, the median is the average of the two middle
values.
Example 2:
ENGINEERING DATA ANALYSIS
DESCRIPTIVE
Dataset: 10, 2, 6, 4, 8
Sorted Dataset: 2, 4, 6, 8, 10
Median = 6 (the middle value)
Dataset: 7, 1, 5, 3
Sorted Dataset: 1, 3, 5, 7
Median = (3 + 5) / 2 = 8 / 2 = 4
(average of the two middle values)
DESCRIPTIVE ANALYSIS
Mode: The value that appears most frequently in a
dataset. A dataset can have one mode, more than one
mode, or no mode if no value repeats.
Example 3:
ENGINEERING DATA ANALYSIS
Dataset: 7, 5, 4, 4, 6
Mode = 4 (the value that appears most
frequently)
Dataset: 2, 3, 1, 5, 4
Mode = None (no value repeats)
DESCRIPTIVE
ENGINEERING DATA ANALYSIS
DESCRIPTIVE
INFERENTIAL
INFERENTIAL STATISTICS
uses sample data to make predictions about a larger population. It includes
hypothesis testing, confidence intervals, regression analysis, and ANOVA
to draw conclusions, estimate ranges, and understand relationships between
variables.
ENGINEERING DATA ANALYSIS
INFERENTIAL
INFERENTIAL STATISTICS
Predictions about a larger population
Example 4:
The Problem: The Second-year civil engineering students are playing an
online gambling game. Rumors suggest that 90 percent of the bettors win or
achieve a high success rate. To assess the accuracy of these claims and for
the benefit of the students' future, you conducted a survey to determine if
the rumors are true.
ENGINEERING DATA ANALYSIS
The answer: After analyzing the survey, you found that 50 out of 87 people
responded. Of the respondents, 75% believe there is indeed a 90 percent
chance of winning, 15% say these claims are false because there is only a
20% chance of winning, and the remaining 10% of the respondents claim
that the game actually has a 50% chance of winning and a 50% chance of
losing.
INFERENTIAL
ENGINEERING DATA ANALYSIS
TYPES OF DATA
ENGINEERING DATA ANALYSIS
QUALITATIVE
QUANTITATIVE
QUALITATIVE DATA
Also known as categorical data, is used to describe
characteristics or qualities that can be observed but not
measured. This type of data categorizes and groups
information into distinct categories, and it can be further
divided into two main types:
ENGINEERING DATA ANALYSIS
QUALITATIVE
NOMINAL DATA
consists of categories that do not have a natural order, ranking
or hierarchy. Each category is unique and mutually exclusive,
meaning that each data point can only belong to one category.
ENGINEERING DATA ANALYSIS
Example 5:
Gender: Male, Female, Non-binary
Color: Red, Blue, Green
City Name: New York, London, Tokyo
QUALITATIVE
ORDINAL DATA
consists of categories that have a meaningful order or ranking, but
the intervals between the categories are not necessarily equal or
known. It allows for the arrangement of data into a sequence based
on a specific criterion
Examples 6:
ENGINEERING DATA ANALYSIS
Education Level: High School, Bachelor's, Master's, Doctorate
QUALITATIVE
Survey Ratings: Very Unsatisfied, Unsatisfied, Neutral, Satisfied,
Very Satisfied
Socioeconomic Status: Low, Middle, High
ENGINEERING DATA ANALYSIS
QUALITATIVE
QUANTITATIVE
QUANTITATIVE DATA
is numerical and can be measured or counted. It is
divided into two main types: discrete and continuous
data.
ENGINEERING DATA ANALYSIS
QUANTITATIVE
DISCRETE DATA
data that can only assume specific, distinct values and cannot be
meaningfully divided into smaller parts. This kind of data usually arises from
counting items or events and is commonly represented by whole numbers.
Example 7:
a. Number of Students:
i. If you count the number of students in different classes, the values
could be 20, 25, 30, etc. Each value is a whole number and represents
a distinct count.
b. Number of Cars:
i. Counting the number of cars in a parking lot gives discrete data, such
as 10 cars, 15 cars, 20 cars.
c. Number of Defects:
i. In quality control, the number of defective items produced in a batch
can be counted, yielding values like 2 defects, 5 defects, 7 defects.
d. Number of Calls:
i. The number of phone calls received in a day can be counted, resulting
in discrete data like 50 calls, 60 calls, 70 calls.
ENGINEERING DATA ANALYSIS
QUANTITATIVE
CONTINUOUS DATA
a type of numerical data that can take any value
within a specified range. Unlike discrete data, which
consists of distinct and separate values, continuous
data is measurable and can be infinitely precise,
depending on the accuracy of the measurement
instrument. Examples of continuous data include
height, weight, temperature, and time. These values
can be expressed in fractions and decimals, allowing
for a high degree of precision
ENGINEERING DATA ANALYSIS
QUANTITATIVE
ENGINEERING DATA ANALYSIS
QUALITATIVE
QUANTITATIVE
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