LECTURE 1 STATISTICS for data analytics and machine learning

Probability & Statistics
for Engineers & Scientists
Authors: Walpole, Myers, Myers, YE
Instructor:
NADEEM KHAN
Lecturer, S & H Dept.,
FAST-NU, Main Campus, Karachi
Nadeem.arif@nu.edu.pk

MT206 – Probability &
Statistics
(4 Credit Hours)
Week Topics
01 Intro. To Statistics, Measures of Central Tendency &
Dispersion
02 Bar Chart, Histogram, Stem-Leaf Plot, Box Plot, Dot Plot,
Frequency Curves, Ogive, Skewness & Kurtosis
03 Introduction to Probability: Sample Space, Tree
Diagram, Event, Set Theory, Venn Diagram
04 Counting techniques, Kinds of Events, Additive rules
05 Conditional Probability, Independence, Multiplicative
rules, Bayes’ Theorem.
06 1st
Mid-Term Examination
Course Outline:

MT206 – Probability &
Statistics
(4 Credit Hours)
Wee
k
Topics
07 Random Variables & Probability Distributions: PMF,
PDF, CDF, Joint & Marginal Probability Distributions,
Mathematical Expectation
08 Discrete Distributions: Binomial & Multinomial, Poisson,
Geometric, Hypergeometric, and Discrete uniform.
09 Continuous Distributions: Normal, Exponential, Uniform,
Chi-Square
10 Testing of Hypothesis: z-test, t-test
11 Goodness of Fit Test, Chi-Square
test of Independence
12 2nd
Mid-Term Examination
Course Outline
(Contd.)

MT206 – Probability & Statistics
(4 Credit Hours)
 Note: The above course outline & schedule is
tentative.
Wee
k
Topics
13 Correlation & Regression
14 Non-Linear Regression: Polynomial regression
15 ANOVA
16 Final Examination
Course Outline
(Contd.)

Marks Distribution
S. No. Particulars % Marks
01 Assignments 10
02 Quizzes 10
03 1st
Mid Term 15
04 2nd
Mid Term 15
05 Final Exam 50
Total 100

Important Instructions
 Be in the classroom on time.
 All students are required to maintain 80% of attendance. In case students
fail to maintain 80% of attendance, they become ineligible to take the final
exam.
 Turn off your cell phones or any other electronic devices before
entering the class.
 Maintain the decorum of the class room all the time.
 Avoid conversation with your classmates while the lecture is in progress.
 Submit your assignments on time otherwise marks will be deducted after
deadline.

Important Instructions
(Contd.)
 Assignment should include a title page
consisting of your complete Name, Roll
No, Subject Name and date etc.
 Assignment should be submitted in the
Holes clip punch folder (snap attached).
 Incomplete assignments lead to
reduction in marks.
 Avoid plagiarism.
 For Quizzes bring your own loose
pages.
 Violation of any instructions leads to
a reduction in marks.

Introduction and Data Collection
PROBABILITY AND
STATISTICS

Learning Objectives
In this topic you learn:
 How Statistics is used in business
 The sources of data used in business
 The types of data used in business
 The basics of Microsoft Excel
 The basics of Minitab

Why Learn Statistics?
So you are able to make better sense of the
ubiquitous use of numbers:
 Business memos
 Business research
 Technical reports
 Technical journals
 Newspaper articles
 Magazine articles

What is statistics?
 A branch of mathematics taking and
transforming numbers into useful information for
decision makers
 Methods for processing & analyzing numbers
 Methods for helping reduce the uncertainty
inherent in decision making

Why Study Statistics?
Decision Makers Use Statistics To:
 Present and describe business data and information properly
 Draw conclusions about large groups of individuals or items,
using information collected from subsets of the individuals or
items.
 Make reliable forecasts about a business activity
 Improve business processes

Types of Statistics
 Statistics
 The branch of mathematics that transforms data into
useful information for decision makers.
Descriptive Statistics
Collecting, summarizing, and
describing data
Inferential Statistics
Drawing conclusions and/or
making decisions concerning a
population based only on sample
data

Descriptive Statistics
 Collect data
 e.g., Survey
 Present data
 e.g., Tables and graphs
 Characterize data

e.g., Sample mean = i
X
n


Inferential Statistics
 Estimation
 e.g., Estimate the population
mean weight using the sample
mean weight
 Hypothesis testing
 e.g., Test the claim that the
population mean weight is 120
pounds
Drawing conclusions about a large group of
individuals based on a subset of the large group.

Basic Vocabulary of Statistics
VARIABLE
A variable is a characteristic of an item or individual.
DATA
Data are the different values associated with a variable.
OPERATIONAL DEFINITIONS
Data values are meaningless unless their variables have operational
definitions, universally accepted meanings that are clear to all associated
with an analysis.

Basic Vocabulary of Statistics
POPULATION
A population consists of all the items or individuals about which
you want to draw a conclusion.
SAMPLE
A sample is the portion of a population selected for analysis.
PARAMETER
A parameter is a numerical measure that describes a
characteristic of a population.
STATISTIC
A statistic is a numerical measure that describes a characteristic of
a sample.

Population vs. Sample
Population Sample
Measures used to describe the
population are called parameters
Measures computed from
sample data are called statistics

Why Collect Data?
 A marketing research analyst needs to assess the
effectiveness of a new television advertisement.
 A pharmaceutical manufacturer needs to determine
whether a new drug is more effective than those currently
in use.
 An operations manager wants to monitor a manufacturing
process to find out whether the quality of the product
being manufactured is conforming to company standards.
 An auditor wants to review the financial transactions of a
company in order to determine whether the company is in
compliance with generally accepted accounting
principles.

Sources of Data
 Primary Sources: The data collector is the one using the data
for analysis
 Data from a political survey
 Data collected from an experiment
 Observed data
 Secondary Sources: The person performing data analysis is
not the data collector
 Analyzing census data
 Examining data from print journals or data published on the internet.

Sources of data fall into four
categories
 Data distributed by an organization or an
individual
 A designed experiment
 A survey
 An observational study

Types of Variables
 Categorical (qualitative) variables have values that
can only be placed into categories, such as “yes” and
“no.”
 Numerical (quantitative) variables have values that
represent quantities.

Types of Data
Data
Discrete
Continuou
s
Examples:
 Marital Status
 Political Party
 Eye Color
(Defined categories)
Examples:
 Number of Children
 Defects per hour
(Counted items)
Examples:
 Weight
 Voltage
(Measured characteristics)

Levels of Measurement
 A nominal scale classifies data into distinct categories in
which no ranking is implied.
Categorical Variables Categories
Personal Computer
Ownership
Type of Stocks Owned
Internet Provider
Yes / No
Microsoft Network / AOL/ Other
Growth Value Other

 An ordinal scale classifies data into distinct categories
in which ranking is implied
Categorical Variable Ordered Categories
Student class designation Freshman, Sophomore, Junior,
Senior
Product satisfaction Satisfied, Neutral, Unsatisfied
Faculty rank Professor, Associate Professor,
Assistant Professor, Instructor
Standard & Poor’s bond ratings AAA, AA, A, BBB, BB, B, CCC, CC,
C, DDD, DD, D
Student Grades A, B, C, D, F

 An interval scale is an ordered scale in which the difference
between measurements is a meaningful quantity but the
measurements do not have a true zero point.
 A ratio scale is an ordered scale in which the difference
between the measurements is a meaningful quantity and the
measurements have a true zero point.

Chapter Summary
 Reviewed why a manager needs to know statistics
 Introduced key definitions:
 Population vs. Sample
 Primary vs. Secondary data types
 Categorical vs. Numerical data
 Examined descriptive vs. inferential statistics
 Reviewed data types and measurement levels
In this chapter, we have

LECTURE 1 STATISTICS for data analytics and machine learning

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