Submit Search
Upload
Chap02 presenting data in chart & tables
•
Download as PPT, PDF
•
5 likes
•
2,308 views
Uni Azza Aunillah
Follow
Modul Statistik Bisnis II
Read less
Read more
Technology
Education
Report
Share
Report
Share
1 of 46
Download now
Recommended
Chap01 intro & data collection
Chap01 intro & data collection
Uni Azza Aunillah
Chap03 numerical descriptive measures
Chap03 numerical descriptive measures
Uni Azza Aunillah
Basic business statistics 2
Basic business statistics 2
Anwar Afridi
Basic Probability
Basic Probability
Yesica Adicondro
Bbs11 ppt ch01
Bbs11 ppt ch01
Tuul Tuul
Ch08 ci estimation
Ch08 ci estimation
Mohamed Elias
Numerical Descriptive Measures
Numerical Descriptive Measures
Yesica Adicondro
Introduction to statistics 1
Introduction to statistics 1
Anwar Afridi
Recommended
Chap01 intro & data collection
Chap01 intro & data collection
Uni Azza Aunillah
Chap03 numerical descriptive measures
Chap03 numerical descriptive measures
Uni Azza Aunillah
Basic business statistics 2
Basic business statistics 2
Anwar Afridi
Basic Probability
Basic Probability
Yesica Adicondro
Bbs11 ppt ch01
Bbs11 ppt ch01
Tuul Tuul
Ch08 ci estimation
Ch08 ci estimation
Mohamed Elias
Numerical Descriptive Measures
Numerical Descriptive Measures
Yesica Adicondro
Introduction to statistics 1
Introduction to statistics 1
Anwar Afridi
Chap01 describing data; graphical
Chap01 describing data; graphical
Judianto Nugroho
Some Important Discrete Probability Distributions
Some Important Discrete Probability Distributions
Yesica Adicondro
Chap08 fundamentals of hypothesis
Chap08 fundamentals of hypothesis
Uni Azza Aunillah
Business statistics (Basics)
Business statistics (Basics)
AhmedToheed3
Chap09 2 sample test
Chap09 2 sample test
Uni Azza Aunillah
Chap13 intro to multiple regression
Chap13 intro to multiple regression
Uni Azza Aunillah
Chap04 basic probability
Chap04 basic probability
Uni Azza Aunillah
Torturing numbers - Descriptive Statistics for Growers (2013)
Torturing numbers - Descriptive Statistics for Growers (2013)
jasondeveau
Bbs11 ppt ch07
Bbs11 ppt ch07
Tuul Tuul
Business Statistics
Business Statistics
shorab
Spss training notes
Spss training notes
Mzee Theogene KUBAHONIYESU
Descriptive statistics
Descriptive statistics
Aileen Balbido
chap07.ppt
chap07.ppt
Murat Öztürkmen
The Normal Distribution and Other Continuous Distributions
The Normal Distribution and Other Continuous Distributions
Yesica Adicondro
Chap12 simple regression
Chap12 simple regression
Uni Azza Aunillah
Descriptive statistics
Descriptive statistics
Dr Resu Neha Reddy
Business Statistics Chapter 2
Business Statistics Chapter 2
Lux PP
introduction to statistics
introduction to statistics
mirabubakar1
Categorical data analysis
Categorical data analysis
Sumit Das
Introduction to Statistics .pdf
Introduction to Statistics .pdf
A. Dally Maria Evangeline
Advertising
Advertising
Uni Azza Aunillah
Clr model
Clr model
Uni Azza Aunillah
More Related Content
What's hot
Chap01 describing data; graphical
Chap01 describing data; graphical
Judianto Nugroho
Some Important Discrete Probability Distributions
Some Important Discrete Probability Distributions
Yesica Adicondro
Chap08 fundamentals of hypothesis
Chap08 fundamentals of hypothesis
Uni Azza Aunillah
Business statistics (Basics)
Business statistics (Basics)
AhmedToheed3
Chap09 2 sample test
Chap09 2 sample test
Uni Azza Aunillah
Chap13 intro to multiple regression
Chap13 intro to multiple regression
Uni Azza Aunillah
Chap04 basic probability
Chap04 basic probability
Uni Azza Aunillah
Torturing numbers - Descriptive Statistics for Growers (2013)
Torturing numbers - Descriptive Statistics for Growers (2013)
jasondeveau
Bbs11 ppt ch07
Bbs11 ppt ch07
Tuul Tuul
Business Statistics
Business Statistics
shorab
Spss training notes
Spss training notes
Mzee Theogene KUBAHONIYESU
Descriptive statistics
Descriptive statistics
Aileen Balbido
chap07.ppt
chap07.ppt
Murat Öztürkmen
The Normal Distribution and Other Continuous Distributions
The Normal Distribution and Other Continuous Distributions
Yesica Adicondro
Chap12 simple regression
Chap12 simple regression
Uni Azza Aunillah
Descriptive statistics
Descriptive statistics
Dr Resu Neha Reddy
Business Statistics Chapter 2
Business Statistics Chapter 2
Lux PP
introduction to statistics
introduction to statistics
mirabubakar1
Categorical data analysis
Categorical data analysis
Sumit Das
Introduction to Statistics .pdf
Introduction to Statistics .pdf
A. Dally Maria Evangeline
What's hot
(20)
Chap01 describing data; graphical
Chap01 describing data; graphical
Some Important Discrete Probability Distributions
Some Important Discrete Probability Distributions
Chap08 fundamentals of hypothesis
Chap08 fundamentals of hypothesis
Business statistics (Basics)
Business statistics (Basics)
Chap09 2 sample test
Chap09 2 sample test
Chap13 intro to multiple regression
Chap13 intro to multiple regression
Chap04 basic probability
Chap04 basic probability
Torturing numbers - Descriptive Statistics for Growers (2013)
Torturing numbers - Descriptive Statistics for Growers (2013)
Bbs11 ppt ch07
Bbs11 ppt ch07
Business Statistics
Business Statistics
Spss training notes
Spss training notes
Descriptive statistics
Descriptive statistics
chap07.ppt
chap07.ppt
The Normal Distribution and Other Continuous Distributions
The Normal Distribution and Other Continuous Distributions
Chap12 simple regression
Chap12 simple regression
Descriptive statistics
Descriptive statistics
Business Statistics Chapter 2
Business Statistics Chapter 2
introduction to statistics
introduction to statistics
Categorical data analysis
Categorical data analysis
Introduction to Statistics .pdf
Introduction to Statistics .pdf
Viewers also liked
Advertising
Advertising
Uni Azza Aunillah
Clr model
Clr model
Uni Azza Aunillah
Chap02 presenting data in chart & tables
Chap02 presenting data in chart & tables
Uni Azza Aunillah
Chap16 decision making
Chap16 decision making
Uni Azza Aunillah
Chap11 chie square & non parametrics
Chap11 chie square & non parametrics
Uni Azza Aunillah
Experimental Methods in Mass Communication Research
Experimental Methods in Mass Communication Research
Eisa Al Nashmi
Chap17 statistical applications on management
Chap17 statistical applications on management
Uni Azza Aunillah
Chap05 discrete probability distributions
Chap05 discrete probability distributions
Uni Azza Aunillah
Chap06 normal distributions & continous
Chap06 normal distributions & continous
Uni Azza Aunillah
Confidence interval with Z
Confidence interval with Z
Habiba UR Rehman
Basic concepts of statistics
Basic concepts of statistics
Dr. Trilok Kumar Jain
Accuracy in photojournalism
Accuracy in photojournalism
Eisa Al Nashmi
Chap10 anova
Chap10 anova
Uni Azza Aunillah
Confidence interval
Confidence interval
Shakeel Nouman
Chap07 interval estimation
Chap07 interval estimation
Uni Azza Aunillah
Chap14 multiple regression model building
Chap14 multiple regression model building
Uni Azza Aunillah
Chapter 02
Chapter 02
bmcfad01
Chapter 10
Chapter 10
bmcfad01
Chapter 01
Chapter 01
bmcfad01
Data analysis
Data analysis
Eisa Al Nashmi
Viewers also liked
(20)
Advertising
Advertising
Clr model
Clr model
Chap02 presenting data in chart & tables
Chap02 presenting data in chart & tables
Chap16 decision making
Chap16 decision making
Chap11 chie square & non parametrics
Chap11 chie square & non parametrics
Experimental Methods in Mass Communication Research
Experimental Methods in Mass Communication Research
Chap17 statistical applications on management
Chap17 statistical applications on management
Chap05 discrete probability distributions
Chap05 discrete probability distributions
Chap06 normal distributions & continous
Chap06 normal distributions & continous
Confidence interval with Z
Confidence interval with Z
Basic concepts of statistics
Basic concepts of statistics
Accuracy in photojournalism
Accuracy in photojournalism
Chap10 anova
Chap10 anova
Confidence interval
Confidence interval
Chap07 interval estimation
Chap07 interval estimation
Chap14 multiple regression model building
Chap14 multiple regression model building
Chapter 02
Chapter 02
Chapter 10
Chapter 10
Chapter 01
Chapter 01
Data analysis
Data analysis
Similar to Chap02 presenting data in chart & tables
Presenting Data in Tables and Charts
Presenting Data in Tables and Charts
Yesica Adicondro
Bbs11 ppt ch02
Bbs11 ppt ch02
Tuul Tuul
Newbold_chap02.ppt
Newbold_chap02.ppt
cfisicaster
Graphs, charts, and tables ppt @ bec doms
Graphs, charts, and tables ppt @ bec doms
Babasab Patil
qc-tools.ppt
qc-tools.ppt
Alpharoot
Intro to quant_analysis_students
Intro to quant_analysis_students
mstegman
Ee184405 statistika dan stokastik statistik deskriptif 1 grafik
Ee184405 statistika dan stokastik statistik deskriptif 1 grafik
yusufbf
Source of DATA
Source of DATA
Nahid Amin
Data organization
Data organization
rowell balala
Gse 213 lesson 3
Gse 213 lesson 3
FEDERAL COLLEGE OF EDUCATION, YOLA
Aed1222 lesson 5
Aed1222 lesson 5
nurun2010
frequency distribution
frequency distribution
Unsa Shakir
Analysis of Variance
Analysis of Variance
Yesica Adicondro
lecture 4 Graphs.pptx
lecture 4 Graphs.pptx
ssuser378d7c
Statistics
Statistics
Zahida Pervaiz
Excel tutorial for frequency distribution
Excel tutorial for frequency distribution
S.c. Chopra
Excel tutorial for frequency distribution
Excel tutorial for frequency distribution
S.c. Chopra
Chapter3
Chapter3
Richard Ferreria
Excel Training
Excel Training
Joshua Flewelling
Basic Stat Notes
Basic Stat Notes
roopcool
Similar to Chap02 presenting data in chart & tables
(20)
Presenting Data in Tables and Charts
Presenting Data in Tables and Charts
Bbs11 ppt ch02
Bbs11 ppt ch02
Newbold_chap02.ppt
Newbold_chap02.ppt
Graphs, charts, and tables ppt @ bec doms
Graphs, charts, and tables ppt @ bec doms
qc-tools.ppt
qc-tools.ppt
Intro to quant_analysis_students
Intro to quant_analysis_students
Ee184405 statistika dan stokastik statistik deskriptif 1 grafik
Ee184405 statistika dan stokastik statistik deskriptif 1 grafik
Source of DATA
Source of DATA
Data organization
Data organization
Gse 213 lesson 3
Gse 213 lesson 3
Aed1222 lesson 5
Aed1222 lesson 5
frequency distribution
frequency distribution
Analysis of Variance
Analysis of Variance
lecture 4 Graphs.pptx
lecture 4 Graphs.pptx
Statistics
Statistics
Excel tutorial for frequency distribution
Excel tutorial for frequency distribution
Excel tutorial for frequency distribution
Excel tutorial for frequency distribution
Chapter3
Chapter3
Excel Training
Excel Training
Basic Stat Notes
Basic Stat Notes
More from Uni Azza Aunillah
Chap15 time series forecasting & index number
Chap15 time series forecasting & index number
Uni Azza Aunillah
Equation
Equation
Uni Azza Aunillah
Saluran distribusi
Saluran distribusi
Uni Azza Aunillah
Mengintegrasikan teori
Mengintegrasikan teori
Uni Azza Aunillah
Kepemimpinen Dahlan Iskan
Kepemimpinen Dahlan Iskan
Uni Azza Aunillah
presentation listening and summarize
presentation listening and summarize
Uni Azza Aunillah
Perbankan syariah
Perbankan syariah
Uni Azza Aunillah
Uu bi no 3 tahun 2004
Uu bi no 3 tahun 2004
Uni Azza Aunillah
Modul ekonomi moneter
Modul ekonomi moneter
Uni Azza Aunillah
Teori manajemen klasik
Teori manajemen klasik
Uni Azza Aunillah
Proses pengawasan dalam manajemen
Proses pengawasan dalam manajemen
Uni Azza Aunillah
Etika bisnis dalam lingkungan produksi
Etika bisnis dalam lingkungan produksi
Uni Azza Aunillah
More from Uni Azza Aunillah
(12)
Chap15 time series forecasting & index number
Chap15 time series forecasting & index number
Equation
Equation
Saluran distribusi
Saluran distribusi
Mengintegrasikan teori
Mengintegrasikan teori
Kepemimpinen Dahlan Iskan
Kepemimpinen Dahlan Iskan
presentation listening and summarize
presentation listening and summarize
Perbankan syariah
Perbankan syariah
Uu bi no 3 tahun 2004
Uu bi no 3 tahun 2004
Modul ekonomi moneter
Modul ekonomi moneter
Teori manajemen klasik
Teori manajemen klasik
Proses pengawasan dalam manajemen
Proses pengawasan dalam manajemen
Etika bisnis dalam lingkungan produksi
Etika bisnis dalam lingkungan produksi
Recently uploaded
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
Hervé Boutemy
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
LoriGlavin3
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
Fwdays
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Precisely
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
Curtis Poe
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
LoriGlavin3
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
LoriGlavin3
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
Pixlogix Infotech
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
Dubai Multi Commodity Centre
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
LoriGlavin3
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
UiPathCommunity
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
gvaughan
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
Mattias Andersson
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
BookNet Canada
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
Alex Barbosa Coqueiro
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
DianaGray10
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024
Lonnie McRorey
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
LoriGlavin3
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
Commit University
Recently uploaded
(20)
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
Chap02 presenting data in chart & tables
1.
Statistics for Managers Using
Microsoft® Excel 4th Edition Chapter 2 Presenting Data in Tables and Charts Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-1
2.
Chapter Goals After completing
this chapter, you should be able to: Create an ordered array and a stem-and-leaf display Construct and interpret a frequency distribution, polygon, and ogive Construct a histogram Create and interpret bar charts, pie charts, and scatter diagrams Present and interpret category data in bar charts and pie charts Describe appropriate and inappropriate ways to display data graphically Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-2
3.
Organizing and Presenting Data
Graphically Data in raw form are usually not easy to use for decision making Some type of organization is needed Table Graph Techniques reviewed here: Ordered Array Stem-and-Leaf Display Frequency Distributions and Histograms Bar charts and pie charts Contingency tables Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-3
4.
Tables and Charts
for Numerical Data Numerical Data Frequency Distributions and Cumulative Distributions Ordered Array Stem-and-Leaf Display Histogram Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Polygon Ogive Chap 2-4
5.
The Ordered Array A
sorted list of data: Shows range (min to max) Provides some signals about variability within the range May help identify outliers (unusual observations) If the data set is large, the ordered array is less useful Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-5
6.
The Ordered Array (continued) Data
in raw form (as collected): 24, 26, 24, 21, 27, 27, 30, 41, 32, 38 Data in ordered array from smallest to largest: 21, 24, 24, 26, 27, 27, 30, 32, 38, 41 Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-6
7.
Stem-and-Leaf Diagram A simple
way to see distribution details in a data set METHOD: Separate the sorted data series into leading digits (the stem) and the trailing digits (the leaves) Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-7
8.
Example Data in ordered
array: 21, 24, 24, 26, 27, 27, 30, 32, 38, 41 Here, use the 10’s digit for the stem unit: Stem Leaf 21 is shown as 2 1 38 is shown as 3 8 Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-8
9.
Example (continued) Data in ordered
array: 21, 24, 24, 26, 27, 27, 30, 32, 38, 41 Completed stem-and-leaf diagram: Stem Leaves 2 1 4 4 6 7 7 3 0 2 8 4 1 Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-9
10.
Using other stem
units Using the 100’s digit as the stem: Round off the 10’s digit to form the leaves Stem Leaf 613 would become 6 1 776 would become 7 8 12 2 ... 1224 becomes Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-10
11.
Using other stem
units (continued) Using the 100’s digit as the stem: The completed stem-and-leaf display: Data: Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Leaves 136 7 2258 8 346699 9 13368 10 356 11 47 12 613, 632, 658, 717, 722, 750, 776, 827, 841, 859, 863, 891, 894, 906, 928, 933, 955, 982, 1034, 1047,1056, 1140, 1169, 1224 Stem 6 2 Chap 2-11
12.
Tabulating Numerical Data: Frequency
Distributions What is a Frequency Distribution? A frequency distribution is a list or a table … containing class groupings (categories or ranges within which the data falls) ... and the corresponding frequencies with which data falls within each grouping or category Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-12
13.
Why Use Frequency
Distributions? A frequency distribution is a way to summarize data The distribution condenses the raw data into a more useful form... and allows for a quick visual interpretation of the data Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-13
14.
Class Intervals and Class
Boundaries Each class grouping has the same width Determine the width of each interval by range Width of int erval ≅ number of desired class groupings Use at least 5 but no more than 15 groupings Class boundaries never overlap Round up the interval width to get desirable endpoints Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-14
15.
Frequency Distribution Example Example:
A manufacturer of insulation randomly selects 20 winter days and records the daily high temperature 24, 35, 17, 21, 24, 37, 26, 46, 58, 30, 32, 13, 12, 38, 41, 43, 44, 27, 53, 27 Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-15
16.
Frequency Distribution Example (continued) Sort
raw data in ascending order: 12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58 Find range: 58 - 12 = 46 Select number of classes: 5 (usually between 5 and 15) Compute class interval (width): 10 (46/5 then round up) Determine class boundaries (limits): 10, 20, 30, 40, 50, 60 Compute class midpoints: 15, 25, 35, 45, 55 Count observations & assign to classes Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-16
17.
Frequency Distribution Example (continued) Data
in ordered array: 12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58 Frequency Relative Frequency Percentage 10 but less than 20 20 but less than 30 30 but less than 40 3 6 5 .15 .30 .25 15 30 25 40 but less than 50 50 but less than 60 4 2 .20 .10 20 10 Class Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-17
18.
Graphing Numerical Data: The
Histogram A graph of the data in a frequency distribution is called a histogram The class boundaries (or class midpoints) are shown on the horizontal axis the vertical axis is either frequency, relative frequency, or percentage Bars of the appropriate heights are used to represent the number of observations within each class Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-18
19.
Histogram Example Class Midpoint Frequency 10
but less than 20 20 but less than 30 30 but less than 40 40 but less than 50 50 but less than 60 (No gaps between bars) 15 25 35 45 55 3 6 5 4 2 Histogram : Daily High Tem perature 7 6 6 Frequency Class 5 5 4 4 3 3 2 2 1 0 0 0 Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. 5 15 25 35 45 Class Midpoints 55 More Chap 2-19
20.
Histograms in Excel 1 Select Tools/Data
Analysis Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-20
21.
Histograms in Excel (continued) 2 Choose
Histogram ( 3 Input data range and bin range (bin range is a cell range containing the upper class boundaries for each class grouping) Select Chart Output and click “OK” Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-21
22.
Questions for Grouping
Data into Classes 1. How wide should each interval be? (How many classes should be used?) 2. How should the endpoints of the intervals be determined? Often answered by trial and error, subject to user judgment The goal is to create a distribution that is neither too "jagged" nor too "blocky” Goal is to appropriately show the pattern of variation in the data Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-22
23.
How Many Class
Intervals? Many (Narrow class intervals) 3 2.5 2 1.5 1 0.5 More 60 56 52 48 44 40 36 32 28 24 20 16 8 0 4 may yield a very jagged distribution with gaps from empty classes Can give a poor indication of how frequency varies across classes 12 3.5 Frequency Temperature Few (Wide class intervals) may compress variation too much and yield a blocky distribution can obscure important patterns of variation. 12 10 Frequency 8 6 4 2 0 0 30 60 More Temperature (X axis labels are upper class endpoints) Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-23
24.
Graphing Numerical Data: The
Frequency Polygon Class Midpoint Frequency Class 10 but less than 20 20 but less than 30 30 but less than 40 40 but less than 50 50 but less than 60 15 25 35 45 55 3 6 5 4 2 Frequency Polygon: Daily High Temperature 7 6 (In a percentage polygon the vertical axis would be defined to show the percentage of observations per class) Frequency 5 4 3 2 1 0 Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. 5 15 25 35 Class Midpoints 45 55 More Chap 2-24
25.
Tabulating Numerical Data: Cumulative
Frequency Data in ordered array: 12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58 Class Frequency Percentage Cumulative Cumulative Frequency Percentage 10 but less than 20 3 15 3 15 20 but less than 30 6 30 9 45 30 but less than 40 5 25 14 70 40 but less than 50 4 20 18 90 50 but less than 60 2 10 20 100 20 100 Total Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-25
26.
Graphing Cumulative Frequencies: The
Ogive (Cumulative % Polygon) Less than 10 10 but less than 20 20 but less than 30 30 but less than 40 40 but less than 50 50 but less than 60 10 20 30 40 50 60 0 15 45 70 90 100 Ogive: Daily High Temperature Cumulative Percentage Class Lower Cumulative class boundary Percentage 100 80 60 40 20 0 10 20 30 40 50 60 Class Boundaries (Not Midpoints) Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-26
27.
Scatter Diagrams Scatter Diagrams
are used for bivariate numerical data Bivariate data consists of paired observations taken from two numerical variables The Scatter Diagram: one variable is measured on the vertical axis and the other variable is measured on the horizontal axis Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-27
28.
Scatter Diagram Example Cost
per Day vs. Production Volume Cost per day 23 125 250 26 140 200 29 146 33 160 38 167 42 170 50 188 55 195 60 200 Cost per Day Volume per day 150 100 50 0 0 10 Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. 20 30 40 50 60 70 Volume per Day Chap 2-28
29.
Scatter Diagrams in
Excel 1 Select the chart wizard 2 Select XY(Scatter) option, then click “Next” 3 When prompted, enter the data range, desired legend, and desired destination to complete the scatter diagram Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-29
30.
Tables and Charts
for Categorical Data Categorical Data Graphing Data Tabulating Data Summary Table Bar Charts Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Pie Charts Pareto Diagram Chap 2-30
31.
The Summary Table Summarize
data by category Example: Current Investment Portfolio Investment Amount Percentage Type (in thousands $) (%) (Variables are Categorical) Stocks Bonds CD Savings 46.5 32.0 15.5 16.0 42.27 29.09 14.09 14.55 Total 110.0 100.0 Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-31
32.
Bar and Pie
Charts Bar charts and Pie charts are often used for qualitative (category) data Height of bar or size of pie slice shows the frequency or percentage for each category Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-32
33.
Bar Chart Example Current
Investment Portfolio Investment Type Amount (in thousands $) Percentage (%) Stocks Bonds CD Savings 46.5 32.0 15.5 16.0 42.27 29.09 14.09 14.55 Total 110.0 100.0 Investor's Portfolio Savings CD Bonds Stocks 0 10 20 30 40 50 Amount in $1000's Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-33
34.
Pie Chart Example Current
Investment Portfolio Investment Type Amount (in thousands $) Percentage (%) Stocks Bonds CD Savings 46.5 32.0 15.5 16.0 42.27 29.09 14.09 14.55 Total 110.0 100.0 Savings 15% CD 14% Bonds 29% Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Stocks 42% Percentages are rounded to the nearest percent Chap 2-34
35.
Pareto Diagram Used to
portray categorical data A bar chart, where categories are shown in descending order of frequency A cumulative polygon is often shown in the same graph Used to separate the “vital few” from the “trivial many” Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-35
36.
Pareto Diagram Example Current
Investment Portfolio 100% 40% 90% 80% 35% 70% 30% 60% 25% 50% 20% 40% 15% 30% 10% 20% 5% 10% 0% cumulative % invested (line graph) % invested in each category (bar graph) 45% 0% Stocks Bonds Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Savings CD Chap 2-36
37.
Tabulating and Graphing Multivariate
Categorical Data Contingency Table for Investment Choices ($1000’s) Investment Category Investor A Investor B Investor C Total Stocks 46.5 55 27.5 129 Bonds CD Savings 32.0 15.5 16.0 44 20 28 19.0 13.5 7.0 95 49 51 Total 110.0 147 67.0 324 (Individual values could also be expressed as percentages of the overall total, percentages of the row totals, or percentages of the column totals) Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-37
38.
Tabulating and Graphing Multivariate
Categorical Data (continued) Side by side bar charts Comparing Investors S avings CD B onds S toc k s 0 10 20 Inves tor A Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. 30 Inves tor B 40 50 60 Inves tor C Chap 2-38
39.
Side-by-Side Chart Example Sales
by quarter for three sales territories: East West North 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 20.4 27.4 59 20.4 30.6 38.6 34.6 31.6 45.9 46.9 45 43.9 60 50 40 East West Nor t h 30 20 10 0 1st Qt r 2nd Qt r Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. 3rd Qt r 4t h Qt r Chap 2-39
40.
Principles of Graphical
Excellence Present data in a way that provides substance, statistics and design Communicate complex ideas with clarity, precision and efficiency Give the largest number of ideas in the most efficient manner Excellence almost always involves several dimensions Tell the truth about the data Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-40
41.
Errors in Presenting
Data Using “chart junk” Failing to provide a relative basis in comparing data between groups Compressing or distorting the vertical axis Providing no zero point on the vertical axis Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-41
42.
Chart Junk Bad Presentation Good
Presentation Minimum Wage 1960: $1.00 1970: $1.60 1980: $3.10 $ Minimum Wage 4 2 0 1960 1970 1980 1990 1990: $3.80 Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-42
43.
No Relative Basis listen Bad
Presentation Freq. 300 200 100 0 A’s received by students. Good Presentation % 30% A’s received by students. 20% 10% FR SO JR SR 0% FR SO JR SR FR = Freshmen, SO = Sophomore, JR = Junior, SR = Senior Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-43
44.
Compressing Vertical Axis Bad
Presentation 200 $ Good Presentation Quarterly Sales 50 100 25 0 $ Quarterly Sales 0 Q1 Q2 Q3 Q4 Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Q1 Q2 Q3 Q4 Chap 2-44
45.
No Zero Point
On Vertical Axis Bad Presentation $Good Presentations Monthly Sales 45 45 $ Monthly Sales 39 36 42 0 39 36 42 J or J F M A M J F J F M A M J $ 60 40 Graphing the first six months of sales 20 0 Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. M A M J Chap 2-45
46.
Chapter Summary Data in
raw form are usually not easy to use for decision making -- Some type of organization is needed: ♦ Table ♦ Graph Techniques reviewed in this chapter: Ordered array and stem-and-leaf display Frequency distributions and histograms Percentage polygons and ogives Scatter diagrams for bivariate data Bar charts, pie charts, and Pareto diagrams Contingency tables and side-by-side bar charts Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-46
Editor's Notes
{}
Download now