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Data Acquisition and Analysis part second

Types of Data, Descriptive Statistics techniques as applicable to different types of data, Types of graphs as applicable to different types of data, Usage of Microsoft Excel tool for descriptive statistics, Data Acquisition (Temperature and humidity) using Sensors interfaced with Arduino, Exporting acquired data to Microsoft Excel and analysis using visual representation.

Data & Analytics◦
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Data Acquisition and Analysis Session-II
2 Content Types of Data, Descriptive Statistics techniques as applicable to different types of data, Types of graphs as applicable to different types of data, Usage of Microsoft Excel tool for descriptive statistics, Data Acquisition (Temperature and humidity) using Sensors interfaced with Arduino, Exporting acquired data to Microsoft Excel and analysis using visual representation.
RIT, Rajaramnagar 3 Unit Learning Outcomes • Apply the Descriptive Statistics techniques on different types of data. • Show the descriptive statistics using Microsoft Excel Tool. • Analyze the data acquired through sensors/mobiles and represent it graphically
Data Acquisition System • Data acquisition is the process of measuring physical quantities such as voltage, current, temperature, pressure, or sound with the help of electronic and mechanical devices. The process is usually carried out by using an array of sensors connected with a computer. • A data acquisition system is a combination of hardware and software components that enable a computer to receive and interpret some physical form of signal.
Why is Data Acquisition necessary? • The first requirement for any scientific claim to be acceptable is to be backed by some physical observations and measured data. This is where data acquisition techniques come into play.
What are the basic components of a Data acquisition system In short, a data acquisition system can be divided into three parts: • Sensors: Sometimes also called transducers, convert some physical quantity, such as pressure, temperature etc. to a corresponding voltage signal. Sensors are mainly of two types, analog and digital sensors. • Processing Unit: Receives the sensor input and is used for signal conditioning and analog to digital conversion. • Computer: Acts as a means to process, interpret, store and display the data received from the processing unit to a user readable format with the help of some suitable software.
Data • Data is a collection of figures and facts, and is raw, unprocessed, a • Information isn’t just data that’s been neatly filed away, it has to be ordered in a way that gives meaning and contextnd unorganized.
Types of Data 1. Categorical data 2. Measurement data
Categorical Data • The objects being studied are grouped into categories based on some qualitative trait. • The resulting data are merely labels or categories. Examples • Hair color – blonde, brown, red, black, etc. • Opinion of students about riots – ticked off, neutral, happy
Categorical data
• Nominal Data – A type of categorical data in which objects fall into unordered categories. – Ex. Hair color – blonde, brown, red, black, etc • Ordinal Data – A type of categorical data in which order is important – Ex. Class– fresh, sophomore, junior, senior, super senior
Categorical data • Binary Data – A type of categorical data in which there are only two categories. – Binary data can either be nominal or ordinal. – Ex. Attendance– present, absent Class– lower classman, upper classman
Measurement Data • The objects being studied are “measured” based on some quantitative trait. • The resulting data are set of numbers. • Example – Cholesterol level – Height – Age – Number of students late for class – Time to complete a homework assignment
Measurement Data Discrete Measurement Data Only certain values are possible (there are gaps between the possible values). Continuous Measurement Data Theoretically, any value within an interval is possible with a fine enough measuring device.
Discrete and Continuous data
Examples • Discrete Data – Number of students late for class – Number of crimes reported to SC police – Number of times the word number is used Generally, discrete data are counts. • Continuous Measurement Data – Cholesterol level – Height – Age – Time to complete a homework assignment Generally, continuous data come from measurements.
Who cares? The type(s) of data collected in a study determine the type of statistical analysis used.
Statistics • Statistics is concerned with the scientific method by which information is collected, organised, analysed and interpreted for the purpose of description and decision making.
Types of Statistics/Analyses Descriptive Statistics – Frequencies – Basic measurements Inferential Statistics – Hypothesis Testing – Correlation – Confidence Intervals – Significance Testing – Prediction Describing a phenomena How many? How much? BP, HR, BMI, IQ, etc. Inferences about a phenomena Proving or disproving theories Associations between phenomena If sample relates to the larger population E.g., Diet and health
Sample vs. Population Population Sample Population - A population is the group from which data are to be collected. Sample - A sample is a subset of a population.
Variable • A variable is a feature characteristic of any member of a population differing in quality or quantity from one member to another.
Variable • Quantitative variable - A variable differing in quantity is called quantitative variable, for example, the weight of a person, number of people in a car. • Qualitative variable - A variable differing in quality is called a qualitative variable or attribute, for example, color, the degree of damage of a car in an accident. • Discrete variable - A discrete variable is one which no value may be assumed between two given values, for example, number of children in a family. • Continuous variable - A continuous variable is one which any value may be assumed between two given values, for example, the time for 100-meter
Primary and Secondary Data • When data is used for the purpose for which it was originally collected it is known as primary data; • When it is used for any other purpose subsequently, it is termed secondary data.
Micro Activity Read it
“A picture is worth a thousand words”
Graphical Descriptions of Data • A graph is a method of presenting statistical data in visual form. • The main purpose of any chart is to give a quick, easy-to-read-and-interpret pictorial representation of data which is more difficult to obtain from a table or a complete listing of the data.
Types of graphs • Pie chart • Simple bar chart • Multiple bar chart • Component bar chart • Other type of graphs
Jan Feb M ar Apr M ay Jun Jul Aug Sep Oct Nov Dec Average Users / M onth Average users / Day 0 500 1000 1500 2000 2500 Chart Title Staff Students Staff 01/01/2017 02/01/2017 03/01/2017 04/01/2017 05/01/2017 06/01/2017 07/01/2017 08/01/2017 09/01/2017 10/01/2017 11/01/2017 12/01/2017
Activity • Create chart for the given data.
Activity Team Activity Duration : 60 minutes Rainfall Analysis for a given area Note: Rainfall data is given for an area for a particular year Students have to find out the following Average rainfall To draw hydrograph To draw flow duration curve Average Function of MS Excel Graph Function of MS Excel Mode Min, Max Function of Excel
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Frequency Histograms
Relative Frequency Histograms • construct a diagram by drawing for each group, or class, a vertical bar whose length is the relative frequency of that group.
Histogram
Central Tendency • Arithmetic Mean • The arithmetic population mean, μ, or simply called mean, is obtained by adding together all of the measurements and dividing by the total number of measurements taken. Mathematically it is given as • i
Central Tendency • Median – Median is defined as the middle item of all given observations arranged in order. – the median is obtained by taking the average of the middle • Mode – Mode is the value which occurs most frequently. The mode may not exist, and even if it does, it may not be unique
Dispersion and Skewness • Range – Range is the difference between two extreme values. • Deciles, Percentile, and Fractile – Decile divides the distribution into ten equal parts while percentile divides the distribution into one hundred equal parts. • Quartiles – Quartiles are the most commonly used values of position which divides distribution into four equal parts • Mean absolute deviation – Mean absolute deviation is the mean of the absolute values of all deviations from the mean. • Variance and Standard Deviation
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