尊敬的 微信汇率：1円 ≈ 0.046166 元 支付宝汇率：1円 ≈ 0.046257元 [退出登录]
SlideShare a Scribd company logo

Chap02 presenting data in chart & tables

•Download as PPT, PDF•
•
Uni Azza Aunillah
Uni Azza Aunillah

This document provides an overview of techniques for presenting numerical data in tables and charts. It discusses ordered arrays, stem-and-leaf displays, frequency distributions, histograms, polygons, ogives, bar charts, pie charts, and scatter diagrams. The chapter goals are to teach how to create and interpret these various data presentation methods using Microsoft Excel. Examples are provided for frequency distributions, histograms, polygons, and ogives to illustrate how to construct and make sense of these graphical representations of quantitative data.

TechnologyEducation
1 of 46
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Histograms in Excel 1 Select Tools/Data Analysis Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 2-20
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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

Recommended

Chap01 intro & data collection
Chap01 intro & data collectionChap01 intro & data collection
Chap01 intro & data collection
Uni Azza Aunillah
 
Chap03 numerical descriptive measures
Chap03 numerical descriptive measuresChap03 numerical descriptive measures
Chap03 numerical descriptive measures
Uni Azza Aunillah
 
Basic business statistics 2
Basic business statistics 2Basic business statistics 2
Basic business statistics 2
Anwar Afridi
 
Chap06 normal distributions & continous
Chap06 normal distributions & continousChap06 normal distributions & continous
Chap06 normal distributions & continous
Uni Azza Aunillah
 
Numerical Descriptive Measures
Numerical Descriptive MeasuresNumerical Descriptive Measures
Numerical Descriptive Measures
Yesica Adicondro
 
Basic Probability
Basic ProbabilityBasic Probability
Basic Probability
Yesica Adicondro
 
Presenting Data in Tables and Charts
Presenting Data in Tables and ChartsPresenting Data in Tables and Charts
Presenting Data in Tables and Charts
Yesica Adicondro
 
The Normal Distribution and Other Continuous Distributions
The Normal Distribution and Other Continuous DistributionsThe Normal Distribution and Other Continuous Distributions
The Normal Distribution and Other Continuous Distributions
Yesica Adicondro
 

Recommended

Chap01 intro & data collection
Chap01 intro & data collectionChap01 intro & data collection
Chap01 intro & data collection
Uni Azza Aunillah
 
Chap03 numerical descriptive measures
Chap03 numerical descriptive measuresChap03 numerical descriptive measures
Chap03 numerical descriptive measures
Uni Azza Aunillah
 
Basic business statistics 2
Basic business statistics 2Basic business statistics 2
Basic business statistics 2
Anwar Afridi
 
Chap06 normal distributions & continous
Chap06 normal distributions & continousChap06 normal distributions & continous
Chap06 normal distributions & continous
Uni Azza Aunillah
 
Numerical Descriptive Measures
Numerical Descriptive MeasuresNumerical Descriptive Measures
Numerical Descriptive Measures
Yesica Adicondro
 
Basic Probability
Basic ProbabilityBasic Probability
Basic Probability
Yesica Adicondro
 
Presenting Data in Tables and Charts
Presenting Data in Tables and ChartsPresenting Data in Tables and Charts
Presenting Data in Tables and Charts
Yesica Adicondro
 
The Normal Distribution and Other Continuous Distributions
The Normal Distribution and Other Continuous DistributionsThe Normal Distribution and Other Continuous Distributions
The Normal Distribution and Other Continuous Distributions
Yesica Adicondro
 
Chap04 basic probability
Chap04 basic probabilityChap04 basic probability
Chap04 basic probability
Uni Azza Aunillah
 
Newbold_chap08.ppt
Newbold_chap08.pptNewbold_chap08.ppt
Newbold_chap08.ppt
cfisicaster
 
Business Statistics Chapter 2
Business Statistics Chapter 2Business Statistics Chapter 2
Business Statistics Chapter 2
Lux PP
 
Some Important Discrete Probability Distributions
Some Important Discrete Probability DistributionsSome Important Discrete Probability Distributions
Some Important Discrete Probability Distributions
Yesica Adicondro
 
Business Statistics Chapter 8
Business Statistics Chapter 8Business Statistics Chapter 8
Business Statistics Chapter 8
Lux PP
 
Two-sample Hypothesis Tests
Two-sample Hypothesis Tests Two-sample Hypothesis Tests
Two-sample Hypothesis Tests
mgbardossy
 
Chap05 discrete probability distributions
Chap05 discrete probability distributionsChap05 discrete probability distributions
Chap05 discrete probability distributions
Uni Azza Aunillah
 
Econometrics Project
Econometrics ProjectEconometrics Project
Econometrics Project
Uday Tharar
 
Chap09 2 sample test
Chap09 2 sample testChap09 2 sample test
Chap09 2 sample test
Uni Azza Aunillah
 
08 ch ken black solution
08 ch ken black solution08 ch ken black solution
08 ch ken black solution
Krunal Shah
 
Bbs11 ppt ch07
Bbs11 ppt ch07Bbs11 ppt ch07
Bbs11 ppt ch07
Tuul Tuul
 
Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Editio...
Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Editio...Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Editio...
Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Editio...
Shepparded
 
chap07.ppt
chap07.pptchap07.ppt
chap07.ppt
Murat Öztürkmen
 
Bbs11 ppt ch12
Bbs11 ppt ch12Bbs11 ppt ch12
Bbs11 ppt ch12
Tuul Tuul
 
Bbs11 ppt ch03
Bbs11 ppt ch03Bbs11 ppt ch03
Bbs11 ppt ch03
Tuul Tuul
 
Chap01 describing data; graphical
Chap01 describing data; graphicalChap01 describing data; graphical
Chap01 describing data; graphical
Judianto Nugroho
 
Introduction to business statistics
Introduction to business statisticsIntroduction to business statistics
Introduction to business statistics
Aakash Kulkarni
 
Ch08 ci estimation
Ch08 ci estimationCh08 ci estimation
Ch08 ci estimation
Mohamed Elias
 
Simple Linear Regression
Simple Linear RegressionSimple Linear Regression
Simple Linear Regression
Sindhu Rumesh Kumar
 
Chap08 fundamentals of hypothesis
Chap08 fundamentals of hypothesisChap08 fundamentals of hypothesis
Chap08 fundamentals of hypothesis
Uni Azza Aunillah
 
Advertising
AdvertisingAdvertising
Advertising
Uni Azza Aunillah
 
Clr model
Clr modelClr model
Clr modelUni Azza Aunillah
 

More Related Content

What's hot

Chap04 basic probability
Chap04 basic probabilityChap04 basic probability
Chap04 basic probability
Uni Azza Aunillah
 
Newbold_chap08.ppt
Newbold_chap08.pptNewbold_chap08.ppt
Newbold_chap08.ppt
cfisicaster
 
Business Statistics Chapter 2
Business Statistics Chapter 2Business Statistics Chapter 2
Business Statistics Chapter 2
Lux PP
 
Some Important Discrete Probability Distributions
Some Important Discrete Probability DistributionsSome Important Discrete Probability Distributions
Some Important Discrete Probability Distributions
Yesica Adicondro
 
Business Statistics Chapter 8
Business Statistics Chapter 8Business Statistics Chapter 8
Business Statistics Chapter 8
Lux PP
 
Two-sample Hypothesis Tests
Two-sample Hypothesis Tests Two-sample Hypothesis Tests
Two-sample Hypothesis Tests
mgbardossy
 
Chap05 discrete probability distributions
Chap05 discrete probability distributionsChap05 discrete probability distributions
Chap05 discrete probability distributions
Uni Azza Aunillah
 
Econometrics Project
Econometrics ProjectEconometrics Project
Econometrics Project
Uday Tharar
 
Chap09 2 sample test
Chap09 2 sample testChap09 2 sample test
Chap09 2 sample test
Uni Azza Aunillah
 
08 ch ken black solution
08 ch ken black solution08 ch ken black solution
08 ch ken black solution
Krunal Shah
 
Bbs11 ppt ch07
Bbs11 ppt ch07Bbs11 ppt ch07
Bbs11 ppt ch07
Tuul Tuul
 
Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Editio...
Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Editio...Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Editio...
Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Editio...
Shepparded
 
chap07.ppt
chap07.pptchap07.ppt
chap07.ppt
Murat Öztürkmen
 
Bbs11 ppt ch12
Bbs11 ppt ch12Bbs11 ppt ch12
Bbs11 ppt ch12
Tuul Tuul
 
Bbs11 ppt ch03
Bbs11 ppt ch03Bbs11 ppt ch03
Bbs11 ppt ch03
Tuul Tuul
 
Chap01 describing data; graphical
Chap01 describing data; graphicalChap01 describing data; graphical
Chap01 describing data; graphical
Judianto Nugroho
 
Introduction to business statistics
Introduction to business statisticsIntroduction to business statistics
Introduction to business statistics
Aakash Kulkarni
 
Ch08 ci estimation
Ch08 ci estimationCh08 ci estimation
Ch08 ci estimation
Mohamed Elias
 
Simple Linear Regression
Simple Linear RegressionSimple Linear Regression
Simple Linear Regression
Sindhu Rumesh Kumar
 
Chap08 fundamentals of hypothesis
Chap08 fundamentals of hypothesisChap08 fundamentals of hypothesis
Chap08 fundamentals of hypothesis
Uni Azza Aunillah
 

What's hot (20)

Chap04 basic probability
Chap04 basic probabilityChap04 basic probability
Chap04 basic probability
 
Newbold_chap08.ppt
Newbold_chap08.pptNewbold_chap08.ppt
Newbold_chap08.ppt
 
Business Statistics Chapter 2
Business Statistics Chapter 2Business Statistics Chapter 2
Business Statistics Chapter 2
 
Some Important Discrete Probability Distributions
Some Important Discrete Probability DistributionsSome Important Discrete Probability Distributions
Some Important Discrete Probability Distributions
 
Business Statistics Chapter 8
Business Statistics Chapter 8Business Statistics Chapter 8
Business Statistics Chapter 8
 
Two-sample Hypothesis Tests
Two-sample Hypothesis Tests Two-sample Hypothesis Tests
Two-sample Hypothesis Tests
 
Chap05 discrete probability distributions
Chap05 discrete probability distributionsChap05 discrete probability distributions
Chap05 discrete probability distributions
 
Econometrics Project
Econometrics ProjectEconometrics Project
Econometrics Project
 
Chap09 2 sample test
Chap09 2 sample testChap09 2 sample test
Chap09 2 sample test
 
08 ch ken black solution
08 ch ken black solution08 ch ken black solution
08 ch ken black solution
 
Bbs11 ppt ch07
Bbs11 ppt ch07Bbs11 ppt ch07
Bbs11 ppt ch07
 
Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Editio...
Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Editio...Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Editio...
Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Editio...
 
chap07.ppt
chap07.pptchap07.ppt
chap07.ppt
 
Bbs11 ppt ch12
Bbs11 ppt ch12Bbs11 ppt ch12
Bbs11 ppt ch12
 
Bbs11 ppt ch03
Bbs11 ppt ch03Bbs11 ppt ch03
Bbs11 ppt ch03
 
Chap01 describing data; graphical
Chap01 describing data; graphicalChap01 describing data; graphical
Chap01 describing data; graphical
 
Introduction to business statistics
Introduction to business statisticsIntroduction to business statistics
Introduction to business statistics
 
Ch08 ci estimation
Ch08 ci estimationCh08 ci estimation
Ch08 ci estimation
 
Simple Linear Regression
Simple Linear RegressionSimple Linear Regression
Simple Linear Regression
 
Chap08 fundamentals of hypothesis
Chap08 fundamentals of hypothesisChap08 fundamentals of hypothesis
Chap08 fundamentals of hypothesis
 

Viewers also liked

Advertising
AdvertisingAdvertising
Advertising
Uni Azza Aunillah
 
Chap02 presenting data in chart & tables
Chap02 presenting data in chart & tablesChap02 presenting data in chart & tables
Chap02 presenting data in chart & tables
Uni Azza Aunillah
 
Chap16 decision making
Chap16 decision makingChap16 decision making
Chap16 decision making
Uni Azza Aunillah
 
Chap11 chie square & non parametrics
Chap11 chie square & non parametricsChap11 chie square & non parametrics
Chap11 chie square & non parametrics
Uni Azza Aunillah
 
Experimental Methods in Mass Communication Research
Experimental Methods in Mass Communication ResearchExperimental Methods in Mass Communication Research
Experimental Methods in Mass Communication Research
Eisa Al Nashmi
 
Chap17 statistical applications on management
Chap17 statistical applications on managementChap17 statistical applications on management
Chap17 statistical applications on management
Uni Azza Aunillah
 
Confidence interval with Z
Confidence interval with ZConfidence interval with Z
Confidence interval with Z
Habiba UR Rehman
 
Basic concepts of statistics
Basic concepts of statistics Basic concepts of statistics
Basic concepts of statistics
Dr. Trilok Kumar Jain
 
Accuracy in photojournalism
Accuracy in photojournalismAccuracy in photojournalism
Accuracy in photojournalism
Eisa Al Nashmi
 
Chap10 anova
Chap10 anovaChap10 anova
Chap10 anova
Uni Azza Aunillah
 
Confidence interval
Confidence intervalConfidence interval
Confidence interval
Shakeel Nouman
 
Chap07 interval estimation
Chap07 interval estimationChap07 interval estimation
Chap07 interval estimation
Uni Azza Aunillah
 
Chap14 multiple regression model building
Chap14 multiple regression model buildingChap14 multiple regression model building
Chap14 multiple regression model building
Uni Azza Aunillah
 
Chapter 02
Chapter 02Chapter 02
Chapter 02
bmcfad01
 
Chapter 10
Chapter 10Chapter 10
Chapter 10
bmcfad01
 
Chapter 01
Chapter 01Chapter 01
Chapter 01
bmcfad01
 
Data analysis
Data analysisData analysis
Data analysis
Eisa Al Nashmi
 
Chapter 12
Chapter 12Chapter 12
Chapter 12
bmcfad01
 
Analysis of statistical data in heath information management
Analysis of statistical data in heath information managementAnalysis of statistical data in heath information management
Analysis of statistical data in heath information management
Saleh Ahmed
 

Viewers also liked (20)

Advertising
AdvertisingAdvertising
Advertising
 
Clr model
Clr modelClr model
Clr model
 
Chap02 presenting data in chart & tables
Chap02 presenting data in chart & tablesChap02 presenting data in chart & tables
Chap02 presenting data in chart & tables
 
Chap16 decision making
Chap16 decision makingChap16 decision making
Chap16 decision making
 
Chap11 chie square & non parametrics
Chap11 chie square & non parametricsChap11 chie square & non parametrics
Chap11 chie square & non parametrics
 
Experimental Methods in Mass Communication Research
Experimental Methods in Mass Communication ResearchExperimental Methods in Mass Communication Research
Experimental Methods in Mass Communication Research
 
Chap17 statistical applications on management
Chap17 statistical applications on managementChap17 statistical applications on management
Chap17 statistical applications on management
 
Confidence interval with Z
Confidence interval with ZConfidence interval with Z
Confidence interval with Z
 
Basic concepts of statistics
Basic concepts of statistics Basic concepts of statistics
Basic concepts of statistics
 
Accuracy in photojournalism
Accuracy in photojournalismAccuracy in photojournalism
Accuracy in photojournalism
 
Chap10 anova
Chap10 anovaChap10 anova
Chap10 anova
 
Confidence interval
Confidence intervalConfidence interval
Confidence interval
 
Chap07 interval estimation
Chap07 interval estimationChap07 interval estimation
Chap07 interval estimation
 
Chap14 multiple regression model building
Chap14 multiple regression model buildingChap14 multiple regression model building
Chap14 multiple regression model building
 
Chapter 02
Chapter 02Chapter 02
Chapter 02
 
Chapter 10
Chapter 10Chapter 10
Chapter 10
 
Chapter 01
Chapter 01Chapter 01
Chapter 01
 
Data analysis
Data analysisData analysis
Data analysis
 
Chapter 12
Chapter 12Chapter 12
Chapter 12
 
Analysis of statistical data in heath information management
Analysis of statistical data in heath information managementAnalysis of statistical data in heath information management
Analysis of statistical data in heath information management
 

Similar to Chap02 presenting data in chart & tables

Bbs11 ppt ch02
Bbs11 ppt ch02Bbs11 ppt ch02
Bbs11 ppt ch02
Tuul Tuul
 
Newbold_chap02.ppt
Newbold_chap02.pptNewbold_chap02.ppt
Newbold_chap02.ppt
cfisicaster
 
Graphs, charts, and tables ppt @ bec doms
Graphs, charts, and tables ppt @ bec domsGraphs, charts, and tables ppt @ bec doms
Graphs, charts, and tables ppt @ bec doms
Babasab Patil
 
qc-tools.ppt
qc-tools.pptqc-tools.ppt
qc-tools.ppt
Alpharoot
 
Intro to quant_analysis_students
Intro to quant_analysis_studentsIntro 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 grafikEe184405 statistika dan stokastik statistik deskriptif 1 grafik
Ee184405 statistika dan stokastik statistik deskriptif 1 grafik
yusufbf
 
Source of DATA
Source of DATASource of DATA
Source of DATA
Nahid Amin
 
Data organization
Data organizationData organization
Data organization
rowell balala
 
Gse 213 lesson 3
Gse 213 lesson 3Gse 213 lesson 3
Gse 213 lesson 3
FEDERAL COLLEGE OF EDUCATION, YOLA
 
Aed1222 lesson 5
Aed1222 lesson 5Aed1222 lesson 5
Aed1222 lesson 5
nurun2010
 
frequency distribution
frequency distribution frequency distribution
frequency distribution
Unsa Shakir
 
Analysis of Variance
Analysis of VarianceAnalysis of Variance
Analysis of Variance
Yesica Adicondro
 
lecture 4 Graphs.pptx
lecture 4 Graphs.pptxlecture 4 Graphs.pptx
lecture 4 Graphs.pptx
ssuser378d7c
 
Statistics
StatisticsStatistics
Statistics
Zahida Pervaiz
 
Excel tutorial for frequency distribution
Excel tutorial for frequency distributionExcel tutorial for frequency distribution
Excel tutorial for frequency distribution
S.c. Chopra
 
Excel tutorial for frequency distribution
Excel tutorial for frequency distributionExcel tutorial for frequency distribution
Excel tutorial for frequency distribution
S.c. Chopra
 
Chapter3
Chapter3Chapter3
Chapter3
Richard Ferreria
 
Excel Training
Excel TrainingExcel Training
Excel Training
Joshua Flewelling
 
Basic Stat Notes
Basic Stat NotesBasic Stat Notes
Basic Stat Notes
roopcool
 
lecture 2 Centeral Tendency.pptx
lecture 2 Centeral Tendency.pptxlecture 2 Centeral Tendency.pptx
lecture 2 Centeral Tendency.pptx
ssuser378d7c
 

Similar to Chap02 presenting data in chart & tables (20)

Bbs11 ppt ch02
Bbs11 ppt ch02Bbs11 ppt ch02
Bbs11 ppt ch02
 
Newbold_chap02.ppt
Newbold_chap02.pptNewbold_chap02.ppt
Newbold_chap02.ppt
 
Graphs, charts, and tables ppt @ bec doms
Graphs, charts, and tables ppt @ bec domsGraphs, charts, and tables ppt @ bec doms
Graphs, charts, and tables ppt @ bec doms
 
qc-tools.ppt
qc-tools.pptqc-tools.ppt
qc-tools.ppt
 
Intro to quant_analysis_students
Intro to quant_analysis_studentsIntro to quant_analysis_students
Intro to quant_analysis_students
 
Ee184405 statistika dan stokastik statistik deskriptif 1 grafik
Ee184405 statistika dan stokastik statistik deskriptif 1 grafikEe184405 statistika dan stokastik statistik deskriptif 1 grafik
Ee184405 statistika dan stokastik statistik deskriptif 1 grafik
 
Source of DATA
Source of DATASource of DATA
Source of DATA
 
Data organization
Data organizationData organization
Data organization
 
Gse 213 lesson 3
Gse 213 lesson 3Gse 213 lesson 3
Gse 213 lesson 3
 
Aed1222 lesson 5
Aed1222 lesson 5Aed1222 lesson 5
Aed1222 lesson 5
 
frequency distribution
frequency distribution frequency distribution
frequency distribution
 
Analysis of Variance
Analysis of VarianceAnalysis of Variance
Analysis of Variance
 
lecture 4 Graphs.pptx
lecture 4 Graphs.pptxlecture 4 Graphs.pptx
lecture 4 Graphs.pptx
 
Statistics
StatisticsStatistics
Statistics
 
Excel tutorial for frequency distribution
Excel tutorial for frequency distributionExcel tutorial for frequency distribution
Excel tutorial for frequency distribution
 
Excel tutorial for frequency distribution
Excel tutorial for frequency distributionExcel tutorial for frequency distribution
Excel tutorial for frequency distribution
 
Chapter3
Chapter3Chapter3
Chapter3
 
Excel Training
Excel TrainingExcel Training
Excel Training
 
Basic Stat Notes
Basic Stat NotesBasic Stat Notes
Basic Stat Notes
 
lecture 2 Centeral Tendency.pptx
lecture 2 Centeral Tendency.pptxlecture 2 Centeral Tendency.pptx
lecture 2 Centeral Tendency.pptx
 

More from Uni Azza Aunillah

Chap15 time series forecasting & index number
Chap15 time series forecasting & index numberChap15 time series forecasting & index number
Chap15 time series forecasting & index number
Uni Azza Aunillah
 
Chap13 intro to multiple regression
Chap13 intro to multiple regressionChap13 intro to multiple regression
Chap13 intro to multiple regression
Uni Azza Aunillah
 
Chap12 simple regression
Chap12 simple regressionChap12 simple regression
Chap12 simple regression
Uni Azza Aunillah
 
Equation
EquationEquation
Equation
Uni Azza Aunillah
 
Mengintegrasikan teori
Mengintegrasikan teoriMengintegrasikan teori
Mengintegrasikan teori
Uni Azza Aunillah
 
presentation listening and summarize
presentation listening and summarizepresentation listening and summarize
presentation listening and summarize
Uni Azza Aunillah
 
Perbankan syariah
Perbankan syariahPerbankan syariah
Perbankan syariah
Uni Azza Aunillah
 
Teori manajemen klasik
Teori manajemen klasikTeori manajemen klasik
Teori manajemen klasik
Uni Azza Aunillah
 
Proses pengawasan dalam manajemen
Proses pengawasan dalam manajemenProses pengawasan dalam manajemen
Proses pengawasan dalam manajemen
Uni Azza Aunillah
 
Etika bisnis dalam lingkungan produksi
Etika bisnis dalam lingkungan produksiEtika bisnis dalam lingkungan produksi
Etika bisnis dalam lingkungan produksiUni Azza Aunillah
 

More from Uni Azza Aunillah (14)

Chap15 time series forecasting & index number
Chap15 time series forecasting & index numberChap15 time series forecasting & index number
Chap15 time series forecasting & index number
 
Chap13 intro to multiple regression
Chap13 intro to multiple regressionChap13 intro to multiple regression
Chap13 intro to multiple regression
 
Chap12 simple regression
Chap12 simple regressionChap12 simple regression
Chap12 simple regression
 
Equation
EquationEquation
Equation
 
Saluran distribusi
Saluran distribusiSaluran distribusi
Saluran distribusi
 
Mengintegrasikan teori
Mengintegrasikan teoriMengintegrasikan teori
Mengintegrasikan teori
 
Kepemimpinen Dahlan Iskan
Kepemimpinen Dahlan IskanKepemimpinen Dahlan Iskan
Kepemimpinen Dahlan Iskan
 
presentation listening and summarize
presentation listening and summarizepresentation listening and summarize
presentation listening and summarize
 
Perbankan syariah
Perbankan syariahPerbankan syariah
Perbankan syariah
 
Uu bi no 3 tahun 2004
Uu bi no 3 tahun 2004Uu bi no 3 tahun 2004
Uu bi no 3 tahun 2004
 
Modul ekonomi moneter
Modul ekonomi moneterModul ekonomi moneter
Modul ekonomi moneter
 
Teori manajemen klasik
Teori manajemen klasikTeori manajemen klasik
Teori manajemen klasik
 
Proses pengawasan dalam manajemen
Proses pengawasan dalam manajemenProses pengawasan dalam manajemen
Proses pengawasan dalam manajemen
 
Etika bisnis dalam lingkungan produksi
Etika bisnis dalam lingkungan produksiEtika bisnis dalam lingkungan produksi
Etika bisnis dalam lingkungan produksi
 

Recently uploaded

Multivendor cloud production with VSF TR-11 - there and back again
Multivendor cloud production with VSF TR-11 - there and back againMultivendor cloud production with VSF TR-11 - there and back again
Multivendor cloud production with VSF TR-11 - there and back again
Kieran Kunhya
 
Call Girls Kochi 💯Call Us 🔝 7426014248 🔝 Independent Kochi Escorts Service Av...
Call Girls Kochi 💯Call Us 🔝 7426014248 🔝 Independent Kochi Escorts Service Av...Call Girls Kochi 💯Call Us 🔝 7426014248 🔝 Independent Kochi Escorts Service Av...
Call Girls Kochi 💯Call Us 🔝 7426014248 🔝 Independent Kochi Escorts Service Av...
dipikamodels1
 
Chapter 5 - Managing Test Activities V4.0
Chapter 5 - Managing Test Activities V4.0Chapter 5 - Managing Test Activities V4.0
Chapter 5 - Managing Test Activities V4.0
Neeraj Kumar Singh
 
intra-mart Accel series 2024 Spring updates_En
intra-mart Accel series 2024 Spring updates_Enintra-mart Accel series 2024 Spring updates_En
intra-mart Accel series 2024 Spring updates_En
NTTDATA INTRAMART
 
Discover the Unseen: Tailored Recommendation of Unwatched Content
Discover the Unseen: Tailored Recommendation of Unwatched ContentDiscover the Unseen: Tailored Recommendation of Unwatched Content
Discover the Unseen: Tailored Recommendation of Unwatched Content
ScyllaDB
 
Introducing BoxLang : A new JVM language for productivity and modularity!
Introducing BoxLang : A new JVM language for productivity and modularity!Introducing BoxLang : A new JVM language for productivity and modularity!
Introducing BoxLang : A new JVM language for productivity and modularity!
Ortus Solutions, Corp
 
Poznań ACE event - 19.06.2024 Team 24 Wrapup slidedeck
Poznań ACE event - 19.06.2024 Team 24 Wrapup slidedeckPoznań ACE event - 19.06.2024 Team 24 Wrapup slidedeck
Poznań ACE event - 19.06.2024 Team 24 Wrapup slidedeck
FilipTomaszewski5
 
Containers & AI - Beauty and the Beast!?!
Containers & AI - Beauty and the Beast!?!Containers & AI - Beauty and the Beast!?!
Containers & AI - Beauty and the Beast!?!
Tobias Schneck
 
Communications Mining Series - Zero to Hero - Session 2
Communications Mining Series - Zero to Hero - Session 2Communications Mining Series - Zero to Hero - Session 2
Communications Mining Series - Zero to Hero - Session 2
DianaGray10
 
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdfLee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf
leebarnesutopia
 
ScyllaDB Tablets: Rethinking Replication
ScyllaDB Tablets: Rethinking ReplicationScyllaDB Tablets: Rethinking Replication
ScyllaDB Tablets: Rethinking Replication
ScyllaDB
 
Day 4 - Excel Automation and Data Manipulation
Day 4 - Excel Automation and Data ManipulationDay 4 - Excel Automation and Data Manipulation
Day 4 - Excel Automation and Data Manipulation
UiPathCommunity
 
Guidelines for Effective Data Visualization
Guidelines for Effective Data VisualizationGuidelines for Effective Data Visualization
Guidelines for Effective Data Visualization
UmmeSalmaM1
 
Cyber Recovery Wargame
Cyber Recovery WargameCyber Recovery Wargame
Cyber Recovery Wargame
Databarracks
 
APJC Introduction to ThousandEyes Webinar
APJC Introduction to ThousandEyes WebinarAPJC Introduction to ThousandEyes Webinar
APJC Introduction to ThousandEyes Webinar
ThousandEyes
 
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...
DanBrown980551
 
CNSCon 2024 Lightning Talk: Don’t Make Me Impersonate My Identity
CNSCon 2024 Lightning Talk: Don’t Make Me Impersonate My IdentityCNSCon 2024 Lightning Talk: Don’t Make Me Impersonate My Identity
CNSCon 2024 Lightning Talk: Don’t Make Me Impersonate My Identity
Cynthia Thomas
 
Call Girls Chennai ☎️ +91-7426014248 😍 Chennai Call Girl Beauty Girls Chennai...
Call Girls Chennai ☎️ +91-7426014248 😍 Chennai Call Girl Beauty Girls Chennai...Call Girls Chennai ☎️ +91-7426014248 😍 Chennai Call Girl Beauty Girls Chennai...
Call Girls Chennai ☎️ +91-7426014248 😍 Chennai Call Girl Beauty Girls Chennai...
anilsa9823
 
QA or the Highway - Component Testing: Bridging the gap between frontend appl...
QA or the Highway - Component Testing: Bridging the gap between frontend appl...QA or the Highway - Component Testing: Bridging the gap between frontend appl...
QA or the Highway - Component Testing: Bridging the gap between frontend appl...
zjhamm304
 
From NCSA to the National Research Platform
From NCSA to the National Research PlatformFrom NCSA to the National Research Platform
From NCSA to the National Research Platform
Larry Smarr
 

Recently uploaded (20)

Multivendor cloud production with VSF TR-11 - there and back again
Multivendor cloud production with VSF TR-11 - there and back againMultivendor cloud production with VSF TR-11 - there and back again
Multivendor cloud production with VSF TR-11 - there and back again
 
Call Girls Kochi 💯Call Us 🔝 7426014248 🔝 Independent Kochi Escorts Service Av...
Call Girls Kochi 💯Call Us 🔝 7426014248 🔝 Independent Kochi Escorts Service Av...Call Girls Kochi 💯Call Us 🔝 7426014248 🔝 Independent Kochi Escorts Service Av...
Call Girls Kochi 💯Call Us 🔝 7426014248 🔝 Independent Kochi Escorts Service Av...
 
Chapter 5 - Managing Test Activities V4.0
Chapter 5 - Managing Test Activities V4.0Chapter 5 - Managing Test Activities V4.0
Chapter 5 - Managing Test Activities V4.0
 
intra-mart Accel series 2024 Spring updates_En
intra-mart Accel series 2024 Spring updates_Enintra-mart Accel series 2024 Spring updates_En
intra-mart Accel series 2024 Spring updates_En
 
Discover the Unseen: Tailored Recommendation of Unwatched Content
Discover the Unseen: Tailored Recommendation of Unwatched ContentDiscover the Unseen: Tailored Recommendation of Unwatched Content
Discover the Unseen: Tailored Recommendation of Unwatched Content
 
Introducing BoxLang : A new JVM language for productivity and modularity!
Introducing BoxLang : A new JVM language for productivity and modularity!Introducing BoxLang : A new JVM language for productivity and modularity!
Introducing BoxLang : A new JVM language for productivity and modularity!
 
Poznań ACE event - 19.06.2024 Team 24 Wrapup slidedeck
Poznań ACE event - 19.06.2024 Team 24 Wrapup slidedeckPoznań ACE event - 19.06.2024 Team 24 Wrapup slidedeck
Poznań ACE event - 19.06.2024 Team 24 Wrapup slidedeck
 
Containers & AI - Beauty and the Beast!?!
Containers & AI - Beauty and the Beast!?!Containers & AI - Beauty and the Beast!?!
Containers & AI - Beauty and the Beast!?!
 
Communications Mining Series - Zero to Hero - Session 2
Communications Mining Series - Zero to Hero - Session 2Communications Mining Series - Zero to Hero - Session 2
Communications Mining Series - Zero to Hero - Session 2
 
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdfLee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf
 
ScyllaDB Tablets: Rethinking Replication
ScyllaDB Tablets: Rethinking ReplicationScyllaDB Tablets: Rethinking Replication
ScyllaDB Tablets: Rethinking Replication
 
Day 4 - Excel Automation and Data Manipulation
Day 4 - Excel Automation and Data ManipulationDay 4 - Excel Automation and Data Manipulation
Day 4 - Excel Automation and Data Manipulation
 
Guidelines for Effective Data Visualization
Guidelines for Effective Data VisualizationGuidelines for Effective Data Visualization
Guidelines for Effective Data Visualization
 
Cyber Recovery Wargame
Cyber Recovery WargameCyber Recovery Wargame
Cyber Recovery Wargame
 
APJC Introduction to ThousandEyes Webinar
APJC Introduction to ThousandEyes WebinarAPJC Introduction to ThousandEyes Webinar
APJC Introduction to ThousandEyes Webinar
 
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...
 
CNSCon 2024 Lightning Talk: Don’t Make Me Impersonate My Identity
CNSCon 2024 Lightning Talk: Don’t Make Me Impersonate My IdentityCNSCon 2024 Lightning Talk: Don’t Make Me Impersonate My Identity
CNSCon 2024 Lightning Talk: Don’t Make Me Impersonate My Identity
 
Call Girls Chennai ☎️ +91-7426014248 😍 Chennai Call Girl Beauty Girls Chennai...
Call Girls Chennai ☎️ +91-7426014248 😍 Chennai Call Girl Beauty Girls Chennai...Call Girls Chennai ☎️ +91-7426014248 😍 Chennai Call Girl Beauty Girls Chennai...
Call Girls Chennai ☎️ +91-7426014248 😍 Chennai Call Girl Beauty Girls Chennai...
 
QA or the Highway - Component Testing: Bridging the gap between frontend appl...
QA or the Highway - Component Testing: Bridging the gap between frontend appl...QA or the Highway - Component Testing: Bridging the gap between frontend appl...
QA or the Highway - Component Testing: Bridging the gap between frontend appl...
 
From NCSA to the National Research Platform
From NCSA to the National Research PlatformFrom NCSA to the National Research Platform
From NCSA to the National Research Platform
 

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

  1. {}
  翻译：