Simple Linear
Regression
APPLIED STATISTICS
L ECTURE 19
01-April-17
Todays Topics
Regression Analysis: A Complete Example
(Reading: Chapter 13)
Regression Analysis:
A Complete Example
Example 13-8
A random sample of eight drivers insured with a company a
Simple Linear
Regression
APPLIED STATISTICS
L ECTURE 17
23-Mar-17
Todays Topics
Scatter Diagram
Least Squares Regression Line
Assumptions of the Regression Model
(Reading: Chapter 13)
Scatter Diagram
Scatter Diagram: A plot of paired observations is ca
Time-series analysis and
Forecasting
APPLIED STATISTICS
L ECTURE 22
13-April-17
Todays Lecture
Introduction to Forecasting
Time Series vs Cross-sectional data analysis
Time series components
The measurement of forecasting error
Smoothing Techniques
Multiple Regression
APPLIED STATISTICS
L ECTURE 20
06-April-17
Todays Topics
Multiple regression
(Reading: Chapter 14)
Multiple Regression Analysis
Definition
A regression model that includes two or more independent
variables is called a multiple regress
Time-series analysis and
Forecasting
APPLIED STATISTICS
L ECTURE 23
15-April-17
Todays Lecture
Introduction to Forecasting
Time Series vs Cross-sectional data analysis
Time series components
The measurement of forecasting error
Smoothing Techniques
Time-series analysis and
Forecasting
APPLIED STATISTICS
L ECTURE 21
08-April-17
Todays Lecture
Introduction to Forecasting
Time Series vs Cross-sectional data analysis
Time series components
The measurement of forecasting error
Reading: Chapter 15 (Ke
Introduction to Statistics
Before we start off talking statistics, we should first get acquainted to
the terminologies conventionally used in the field of statistics.
The term statistics actually convey two distinct synonyms:
In simple English statistics
Discrete Random
Variable & Their
Probability Distribution2
Factorials
The value of the factorial of a number is obtained by multiplying
all the integers from that number to 1.
Factorials The symbol n!, read as n factorial, represents the
product of all th
ORGANIZING & GRAPHING
DATA
In this chapter we shall see how through the techniques provided by
Descriptive Statistics, we can organize and display data using bargraphs, pie-charts, histograms, polygons & stem & leaf displays.
Raw Data: Data recorded in t
PROBABILITY-PART 1
WHY NEED
PROBABILITY?
Probability, which measures the likelihood that an event will occur, is an important part of statistics. It is
the basis of inferential statistics, which will be introduced in later chapters. In inferential statis
Discrete Random Variable &
Their Probability Distribution
We know that any given statistical experiment has more than one
outcome. It is impossible to predict which of the many possible
outcomes will occur if an experiment is performed. Consequently,
deci
Probability:Part
-2
Marginal & Conditional
Probabilities
Marginal Probability: Marginal probability is the
probability of a single event without consideration of any other
event. Marginal probability is also called simple probability.
Conditional Probab
ORGANIZING & GRAPHING DATA( PART-2)
SHAPES OF
HISTOGRAM
Histograms can take up one of the following shapes:
I. Symmetric
II. Skewed
III. Uniform or rectangular
Symmetric histogram is identical on both sides of its
central point.
Skewed histogram is non-sy
Sampling Distribution-1
We have talked about probability distribution
of random variables, we now extend that
probability distribution concept to the concept
of sample statistics. If you can recall, sample
statistics are sample mean, sample median,
sampl
PRBABILITY3
INTERSECTION OF
EVENTS & THE
MULTIPLICATION RULE
Intersection of Events Let A and B be two events defined in a sample
space. The intersection of A and B represents the collection of all
outcomes that are common to both A and B and is denoted
Numerical Descriptive Measure:
Part-3
Measure of Position
Measure of position determines the position
of a single value in relation to other values in a
sample or a population data set. There are
many measures of position; however, only
quartiles, percen
Numerical Descriptive Measure:
Part-2
Central Tendency & Measure of Dispersion
for Grouped Data
Previously we saw how to obtain numerical
summary measures of ungrouped data, now
we extend the analysis to grouped data.
Mean, Median & Mode
Unlike the mean
Continuous Random Variable & the
Normal Distribution-1
The possible values that a continuous random variable can
assume are infinite and uncountable. For example, the
variable that represents the time taken by a worker to
commute from home to work is a c
Sampling Distribution-2
Applications of the Sampling Distribution of X
Given a normally distributed population, or a non-normal
distributed population and using the Central Limit Theorem
we can say that:
If we take all possible samples of the same (large
Continuous Random Variable &
the Normal Distribution-2
Standardizing a Normal Distribution
In real-world applications, a (continuous) random
variable may have a normal distribution with values
of the mean and standard deviation that are
different from 0
Introduction to Statistics!
BUS (172)!
2015"
Professor Golam Moinuddin, PhD."
CHAPTER 1: !
!
INTRODUCTION
"
WHAT IS STATISTICS?"
Definition"
Statistics is a group of methods used to collect,
analyze, present, and interpret data and to make
decisions."
Has
Assignment 1
Department of Public Health
Course: Biostatistics I (AH)
Due: February 12 (F) and February 15 (M)
Q1 In a study of physical endurance levels of male college freshman in NSU, the endurance scores based
on several exercise routines were collect
Bar Chart
Bar charts are commonly used to describe categorical data. Height of bar shows the
frequency for each category.
Example: NSU students by Departments
Departments
BBA
EEE
CSE
ECON
PHARMACY
ENV
ARCHITECTURE
ENGLISH
9000
8000
7000
6000
5000
4000
No.
Linear Regression
Bo Han
Texas A&M University-Commerce
Simple Linear Regression
Regression Model: The equation that describes how y is
related to x and an error term.
Simple Linear Regression Model:
y = 0 + 1x +
Parameters: The characteristics of the pop