ENGINEERING DATA ANALYSIS
STATISTICS
 derived from the word “state”, was used to refer to a collection of facts of interest to the state.
 the science that deals with the systematic method of collecting, classifying, presenting, analyzing
and interpreting qualitative and numerical data.
 The art of learning from data.
 It is concerned with the collection of data, their subsequent description, and their analysis, which
often leads to the drawing of conclusions.
POPULATION:
 The collection of all individuals or items under consideration in a statistical study.
SAMPLE:
 That part of the population from which information is obtained.
DATA:
 a set of observations
 a set of possible outcomes.
PARAMETER:
 a number that is used to represent a population characteristic and that generally cannot be
determined easily.
STATISTIC:
 a numerical characteristic of the sample; a statistic estimates the corresponding population
parameter.
DESCRIPTIVE STATISTICS
 This refers to the methods of summarizing and presenting data in the form which will make them
easier to analyze and interpret.
 It characterizes the distribution of a set of observations on a specific variable or variables.
 Includes the construction of graphs, charts, and tables and the calculation of various descriptive
measures such as averages, measures of variation, and percentiles.
INFERENTIAL STATISTICS
 Consists of methods for drawing and measuring the reliability of conclusions about a population
based on information obtained from a sample of the population.
 This refers to the drawing of valid conclusions or inferences about a population based on
representative sample systematically taken from the same population.
This study source was downloaded by 100000858453199 from CourseHero.com on 12-13-2022 10:59:53 GMT -06:00
https://www.coursehero.com/file/45825333/REVIEWER-FINAL-Engineering-Data-Analysisdocx/
ENGINEERING DATA ANALYSIS
 If the purpose of the study is to examine and explore information for its own intrinsic interest only,
the study is descriptive.
 If the information is obtained from a sample of a population and the purpose of the study is to use
that information to draw conclusions about the population, the study is inferential.
 Thus, a descriptive study may be performed either on a sample or on a population.
 Only when an inference is made about the population, based on information obtained from the
sample, does the study become inferential.
VARIABLES
 any characteristics, number, or quantity that can be measured or counted.
 A variable may also be called a data item
 It is called a variable because the value may vary between data units in a population, and may
change in value over time.
TYPES OF VARIABLES
NUMERIC VARIABLES
 have values that describe a measurable quantity as a number, like 'how many' or 'how much'.
Therefore numeric variables are quantitative variables.
CATEGORICAL VARIABLES
 have values that describe a 'quality' or 'characteristic' of a data unit, like 'what type' or 'which
category'.
 fall into mutually exclusive (in one category or in another) and exhaustive (include all possible
options) categories.
 categorical variables are qualitative variables and tend to be represented by a non-numeric value.
NUMERIC/ QUANTITATIVE VARIABLES
CONTINUOUS VARIABLE.
 Observations can take any value between a certain set of real numbers.
 The value given to an observation for a continuous variable can include values as small as the
instrument of measurement allows.
DISCRETE VARIABLE.
 Observations can take a value based on a count from a set of distinct whole values.
 A discrete variable cannot take the value of a fraction between one value and the next closest
value.
CATEGORICAL/ QUALITATIVE VARIABLES
This study source was downloaded by 100000858453199 from CourseHero.com on 12-13-2022 10:59:53 GMT -06:00
https://www.coursehero.com/file/45825333/REVIEWER-FINAL-Engineering-Data-Analysisdocx/
ENGINEERING DATA ANALYSIS
Ordinal variable
 Observations can take a value that can be logically ordered or ranked.
 The categories associated with ordinal variables can be ranked higher or lower than another, but
do not necessarily establish a numeric difference between each category.
Nominal variable.
 Observations can take a value that is not able to be organized in a logical sequence
FREQUENCY DISTRIBUTION
 It is a tabular arrangement of data showing its classification or grouping according to magnitude
or size.
Class Interval
– This refers to the grouping defined by a lower limit and an upper limit
Class frequency
– refers to the number of observations belonging to a class interval
Class mark
– is the midpoint or middle value of the class interval
Class boundary
– is the more precise expressions of the class limits also called the true limits.
Class size
– is the width of each class interval
GRAPHS
 Data can be summarized or presented in two ways:
1. Tabular
2. Charts/graphs.
BAR CHART:
used to display the frequency distribution in the graphical form.
PIE CHART:
used to display the frequency distribution.
It displays the ratio of the observations.
LINE CHART:
used to display the trend of observations.
It is a very popular display for the data which represent time.
HISTOGRAM:
Looks like the bar chart except that the horizontal axis represent the data which is
quantitative in nature.
There is no gap between the bars.
FREQUENCY
POLYGON:
looks like the line chart except that the horizontal axis represent the class mark of
data whichfrom
is quantitative
nature.
This study source was downloadedthe
by 100000858453199
CourseHero.com onin
12-13-2022
10:59:53 GMT -06:00
https://www.coursehero.com/file/45825333/REVIEWER-FINAL-Engineering-Data-Analysisdocx/
ENGINEERING DATA ANALYSIS
OGIVE:
line graph with the horizontal axis represent the upper limit of the class interval
while the vertical axis represent the cummulative frequencies.
MEASURES OF CENTRAL TENDENCY
 Offer us a “point'' estimate, or single number, which we can use as a summary of a distribution of
scores.
 One number which represents or characterizes the entire distribution (as best as one number
can).
 Keep in mind, the center point of scores in a distribution may not be in the middle of the scale of
those scores.
MODE:
The most frequently occurring score in a distribution.
MEDIAN:
The “middle'' score of a distribution.
The point that lies in the middle of a distribution.
MEAN:
The arithmetic average of the scores of a distribution.
The sum of the observations divided by the number of observations in a data set.
TRIMMED MEAN
 simply refers to a mean calculated after “trimming'' a certain percentage of extreme scores.
MEDIAN
 is an extreme example of a trimmed mean; the median trims all but the middle score or middle
two scores.
M-ESTIMATORS
 are weighted means; meaning scores near the middle are given more weight and scores at the
extremes are given less weight.
VARIATION
a way to show how data is dispersed, or spread out.
MEASURES OF SPREAD
A proper description of a set of data should include both these characteristics.
RANGE
 The difference between the largest and the smallest sample values.
This study source was downloaded by 100000858453199 from CourseHero.com on 12-13-2022 10:59:53 GMT -06:00
https://www.coursehero.com/file/45825333/REVIEWER-FINAL-Engineering-Data-Analysisdocx/
ENGINEERING DATA ANALYSIS
 It depends only on extreme values and provides no information about how the remaining data are
distributed.
MEAN ABSOLUTE DEVIATION
 All items in the distribution must be taken into account and determine the amount by which each
item value varies from the mean of the distribution
 the average distance between each data value and the mean.
VARIANCE
 It is the average of the squared deviation values from the distribution’s mean. If all values are
identical, the variance is zero, the greater the dispersion of values, the greater the variance.
 general idea of the spread of your data.
STANDARD VARIATION
 indicates how far, on average, the observations in the sample are from the mean of the sample.
This study source was downloaded by 100000858453199 from CourseHero.com on 12-13-2022 10:59:53 GMT -06:00
https://www.coursehero.com/file/45825333/REVIEWER-FINAL-Engineering-Data-Analysisdocx/
ENGINEERING DATA ANALYSIS
____________________
derived from the word “state”, was used to refer to a collection of facts of
interest to the state.
____________________
classifying,
the science that deals with the systematic method of collecting,
presenting, analyzing and interpreting qualitative and numerical
data.
____________________
The art of learning from data.
____________________
It is concerned with the collection of data, their subsequent description,
and their analysis, which often leads to the drawing of conclusions.
This study source was downloaded by 100000858453199 from CourseHero.com on 12-13-2022 10:59:53 GMT -06:00
https://www.coursehero.com/file/45825333/REVIEWER-FINAL-Engineering-Data-Analysisdocx/
ENGINEERING DATA ANALYSIS
____________________
The collection of all individuals or items under consideration in a statistical
study.
____________________
That part of the population from which information is obtained.
____________________
a set of observations
____________________
a set of possible outcomes
____________________
This refers to the methods of summarizing and presenting data in the
form
____________________
which will make them easier to analyze and interpret.
It characterizes the distribution of a set of observations on a specific
variable or variables.
____________________
Includes the construction of graphs, charts, and tables and the calculation
of various descriptive measures such as averages, measures of variation,
and percentiles.
____________________
Consists of methods for drawing and measuring the reliability of
conclusions about a population based on information obtained from a
sample of the population.
____________________
This refers to the drawing of valid conclusions or inferences about a
population based on representative sample systematically taken from the
same population.
____________________
any characteristics, number, or quantity that can be measured or counted.
____________________
A _________ may also be called a data item
____________________
It is called a _______ because the value may vary between data units in a
population, and may change in value over time.
____________________
have values that describe a measurable quantity as a number, like 'how
many' or 'how much'. Therefore numeric variables are quantitative
variables.
____________________
have values that describe a 'quality' or 'characteristic' of a data unit, like
'what type' or 'which category'.
____________________
fall into mutually exclusive (in one category or in another) and exhaustive
(include all possible options) categories.
____________________
_______________ are qualitative variables and tend to be represented
by a non-numeric value.
____________________
Observations can take any value between a certain set of real numbers.
____________________
The value given to an observation for a _______________ can include
values as small as the instrument of measurement allows.
____________________
Observations can take a value based on a count from a set of distinct
whole values.
____________________
A ____________ cannot take the value of a fraction between one value
and the next closest value.
This study source was downloaded by 100000858453199 from CourseHero.com on 12-13-2022 10:59:53 GMT -06:00
https://www.coursehero.com/file/45825333/REVIEWER-FINAL-Engineering-Data-Analysisdocx/
ENGINEERING DATA ANALYSIS
____________________
Observations can take a value that can be logically ordered or ranked.
____________________
The categories associated with ordinal variables can be ranked higher or
lower than another, but do not necessarily establish a numeric difference
between each category.
____________________
Observations can take a value that is not able to be organized in a logical
sequence
____________________
Data can be summarized or presented in two ways and Tabular and
Charts/graphs.
____________________
used to display the frequency distribution in the graphical form.
____________________
used to display the frequency distribution.
____________________
It displays the ratio of the observations.
____________________
used to display the trend of observations.
____________________
It is a very popular display for the data which represent time.
____________________
Looks like the bar chart except that the horizontal axis represent the data
which is quantitative in nature.
____________________
There is no gap between the bars.
____________________
looks like the line chart except that the horizontal axis represent the class
mark of the data which is quantitative in nature.
____________________
line graph with the horizontal axis represent the upper limit of the class
interval while the vertical axis represent the cummulative frequencies.
____________________
Offer us a “point'' estimate, or single number, which we can use as a
summary of a distribution of scores.
____________________
One number which represents or characterizes the entire distribution (as
best as one number can).
____________________
Keep in mind, the center point of scores in a distribution may not be in the
middle of the scale of those scores.
____________________
The most frequently occurring score in a distribution.
____________________
The “middle'' score of a distribution.
____________________
The point that lies in the middle of a distribution.
____________________
The arithmetic average of the scores of a distribution.
____________________
The sum of the observations divided by the number of observations in a
data set.
____________________
simply refers to a mean calculated after “trimming'' a certain percentage
of
extreme scores.
____________________
is an extreme example of a trimmed mean; the median trims all but the
middle score or middle two scores.
____________________
are weighted means; meaning scores near the middle are given more
weight and scores at the extremes are given less weight.
____________________
The difference between the largest and the smallest sample values.
This study source was downloaded by 100000858453199 from CourseHero.com on 12-13-2022 10:59:53 GMT -06:00
https://www.coursehero.com/file/45825333/REVIEWER-FINAL-Engineering-Data-Analysisdocx/
ENGINEERING DATA ANALYSIS
____________________
It depends only on extreme values and provides no information about how
the remaining data are distributed.
____________________
All items in the distribution must be taken into account and determine the
amount by which each item value varies from the mean of the distribution
____________________
the average distance between each data value and the mean.
____________________
It is the average of the squared deviation values from the distribution’s
mean. If all values are identical, the variance is zero, the greater the
dispersion of values, the greater the variance.
____________________
general idea of the spread of your data.
This study source was downloaded by 100000858453199 from CourseHero.com on 12-13-2022 10:59:53 GMT -06:00
https://www.coursehero.com/file/45825333/REVIEWER-FINAL-Engineering-Data-Analysisdocx/
Powered by TCPDF (www.tcpdf.org)