Evans Analytics2e Ppt 03

7/23/2019 Evans Analytics2e Ppt 03

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Chapter 3

Visualizing andExploring Data


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Data visualization - the process of displayingdata (often in large quantities) in a meaningfulfashion to provide insights that will support betterdecisions. Data visualization improves decision-making, provides

managers with better analysis capabilities that reducereliance on ! professionals, and improves collaborationand information sharing.

Data Visualization


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!abular data can be used to determine e"actly howmany units of a certain product were sold in a particularmonth, or to compare one month to another. #or e"ample, we see that sales of product $ dropped in #ebruary,

specifically by %.&' (computed as *+*). *eyond suchcalculations, however, it is difficult to draw big picture conclusions.

Example 3.1: Tabular vs. Visual Data

Analysis


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$ visual chart provides themeans to easily compare overall sales

of different products (roduct

/ sells the least, for e"ample)0 identify trends (sales of

roduct D are increasing),other patterns (sales ofroduct / is relatively stablewhile sales of roduct *fluctuates more over time),and e"ceptions (roduct 12ssales fell considerably in3eptember).

Example 3.1: Tabular vs. Visual Data

Analysis


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$ dashboard is a visual representation of a set of keybusiness measures. t is derived from the analogy of anautomobile2s control panel, which displays speed,gasoline level, temperature, and so on. Dashboards provide important summaries of key business

information to help manage a business process or function.

Dashboards


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3elect the Inserttab. 4ighlight the data. /lick on chart type, then subtype.

5se Chart Toolsto customize.

Creating Charts in i!roso"t Ex!el


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1"cel distinguishes between vertical and horizontal barcharts, calling the former column charts and the latterbar charts. $ clustered column chart compares values across categories

using vertical rectangles0

a stacked column chart displays the contribution of each value tothe total by stacking the rectangles0

a 66' stacked column chart compares the percentage that eachvalue contributes to a total.

/olumn and bar charts are useful for comparingcategorical or ordinal data, for illustrating differencesbetween sets of values, and for showing proportions orpercentages of a whole.

Column and #ar Charts


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Example 3.$: Creating a Column Chart

4ighlighted /ells

4ighlight the range /+78%, which includes the headings anddata for each category. /lick on the Column Chart buttonand then on the first chart type in the list (a clusteredcolumn chart).


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Example 3.$: Creating a Column Chart

!o add a title, click on the first icon in the Chart Layouts group. /lick on 9/hart!itle: in the chart and change it to 911; 1mployment en: and 9?omen: in a similar fashion.


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@ine charts provide a useful means for displaying dataover time. Aou may plot multiple data series in line charts0 however, they can

be difficult to interpret if the magnitude of the data values differsgreatly. n that case, it would be advisable to create separate

charts for each data series.

%ine Charts

1"ample +.+7 $ @ine/hart for /hina 1"port

Data


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&ie Charts

$ pie chart displays this by partitioning a circle into pie-shaped areas showing the relative proportion.

1"ample +.B7 $ ie

/hart for /ensus Data


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&ie Charts

Data visualization professionals donCt recommend using pie charts.n a pie chart, it is difficult to compare the relative sizes of areas0however, the bars in the column chart can easily be compared todetermine relative ratios of the data.

f you do use pie charts, restrict them to small numbers of categories,always ensure that the numbers add to 66', and use labels to display

the group names and actual percentages. $void three-dimensional (+-D)pie charts=especially those that are rotated=and keep them simple.


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$n area chart combines the features of a pie chart withthose of line charts. $rea charts present more information than pie or line charts alone

but may clutter the observer2s mind with too many details if toomany data series are used0 thus, they should be used with care.

Area Charts

1"ample +.7 $n $rea/hart for 1nergy

/onsumption


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3catter charts show the relationship between twovariables. !o construct a scatter chart, we needobservations that consist ofpairsof variables.

'!atter Charts

1"ample +.%7 $3catter /hart for


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$ bubble chart is a type of scatter chart in which the sizeof the data marker corresponds to the value of a thirdvariable0 consequently, it is a way to plot three variablesin two dimensions.

#ubble Charts

1"ample +.&7$ *ubble/hart for3tock/omparisons


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3tock chart3urface chartDoughnut chart


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>any applications of business analytics involve geographic data.Eisualizing geographic data can highlight key data relationships,identify trends, and uncover business opportunities. n addition, itcan often help to spot data errors and help end users understandsolutions, thus increasing the likelihood of acceptance of decision

models. /ompanies like Fike use geographic data and information systems

for visualizing where products are being distributed and how thatrelates to demographic and sales information. !his information isvital to marketing strategies.

Geographic mapping capabilities were introduced in 1"cel 666 butwere not available in 1"cel 66 and later versions. !hesecapabilities are now available through >icrosoft >apoint 66,which must be purchased separately.

(eographi! Data


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Data bars /olor scales con sets

3parklines /amera tool

)ther Ex!el Data Visualization Tools


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Data bars display colored bars that are scaled to themagnitude of the data values (similar to a bar chart) butplaced directly within the cells of a range. 4ighlight the data in each column, click the Conditional

Formattingbutton in the Stylesgroup within the Hometab, selectData Bars, and choose the fill option and color.

Example 3.*: Data Visualization through

Conditional +ormatting


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Color s!ales shade cells based on their numerical valueusing a color palette. /olor-coding of quantitative data is commonly called a heatmap.




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,!on sets provide similar information using varioussymbols such as arrows or stoplight colors.




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'par-linesare graphics that summarize a rowor column of data in a single cell.

1"cel has three types of sparklines7 line,

column, and winloss. @ine sparklines are clearly useful for time-series data

/olumn sparklines are more appropriate forcategorical data.

?in-loss sparklines are useful for data that move up ordown over time.

'par-lines


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Generally you need to e"pand the row or column widths to display

them effectively. Fotice, however, that the lengths of the bars arenot scaled properly to the data0 for e"ample, in the first one,products D and 1 are roughly one-third the value of roduct 1 yetthe bars are not scaled correctly. 3o be careful when using them.

Example 3. Examples o" 'par-lines


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!his tool allows you to create live pictures of variousranges from different worksheets that you can place on asingle page, size them, and arrange them easily.

!hey are simply linked pictures of the original ranges,

and the advantage is that as any data are changed orupdated, the camera shots are also. !o use the camera too, first add it to the Quick ccess Toolbar(the set of

buttons above the ribbon). #rom the Filemenu, choose !ptionsand thenQuick ccess Toolbar. /hoose Commands, and then Commands "ot inthe #ibbon. 3elect Cameraand add it.

Ex!el Camera Tool


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>anagers often need to sort and filter data. +ilteringmeans e"tracting a set of records having

certain characteristics.

1"cel provides a convenient way of formattingdatabases to facilitate analysis using sorting andfiltering, called Tables.

Data /ueries: Tables0 'orting0 and

+iltering


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#irst, select the range of the data, including headers (a useful shortcut is to

select the first cell in the upper left corner, then click Ctrl$Shi%t$do&n arro&,and then Ctrl$Shi%t$right arro&).

Fe"t, click Tablefrom the Tablesgroup on the Inserttab and make sure thatthe bo" for 'y Table Has Headersis checked. (Aou may also Hust select acell within the table and then click on Tablefrom the Insertmenu.)

!he table range will now be formatted and will continue automatically whennew data are entered.

f you click within a table, the Table Tools Design tab will appear in theribbon, allowing you to do a variety of things, such as change the colorscheme, remove duplicates, change the formatting, and so on.

Example 3.1: Creating an Ex!el Table


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3uppose that in the Credit #isk Datatable, we wish to calculate thetotal amount of savings in column /. ?e could, of course, simplyuse the function I35>(/B7/BJ). 4owever, with a table, we coulduse the formula I35>(Table()Sa*ings+). !he table name,Table(,can be found (and changed) in the ,ropertiesgroup of the Table

Tools Design tab. Fote that Sa*ingsis the name of the header incolumn /. ;ne of the advantages of doing this is that if we add newrecords to the table, the calculation will be updated automatically,

Example 3.11: Table2#ased

Cal!ulations


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'orting Data in Ex!el

!he sort buttons in 1"cel can be found under the Datatab in the Sort - Filter group. 3elect a single cell in thecolumn you want to sort on and click the 9$K downarrow: button to sort from smallest to largest or the 9$K

up arrow: button to sort from largest to smallest. Aoumay also click the Sortbutton to specify criteria for moreadvanced sorting capabilities.


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3uppose we wish to sort the data by supplier. /lick onany cell in column $ of the data (but not the header cell

$+) and then the 9$K down: button in the Data tab. 1"celwill select the entire range of the data and sort by name

of supplier in column $.

Example 3.1$ 'orting Data in the

&ur!hase )rders Database


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$n talian economist, Eilfredo areto, observed in L6%that a large proportion of the wealth in taly was ownedby a small proportion of the people.

3imilarly, businesses often find that a large proportion ofsales come from a small percentage of customers, a

large percentage of quality defects stems from Hust acouple of sources, or a large percentage of inventoryvalue corresponds to a small percentage of items

$ &areto analysis involves sorting data and calculating

cumulative proportions.

&areto Analysis


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Example 3.13: Applying the &areto

&rin!iple

&' of the bicycle inventory value comes from B6' (LB) of items.

3ort by


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#or large data files, finding a particular subset ofrecords that meet certain characteristics by sortingcan be tedious.

1"cel provides two filtering tools7 $uto#ilter for simple criteria, and

$dvanced #ilter for more comple" criteria.

+iltering Data


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3elect any cell in thedatabase

Data M Sort - Filter .Filter

/lick on the dropdownarrow in cell D+.

3elect *olt-nut package to

filter out all other items.

Example 3.1: +iltering 4e!ords by ,tem

Des!ription

n the ,urchase !rders database, suppose we are interested ine"tracting all records corresponding to the item *olt-nut package.


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Example 3.1: +ilter 4esults

!he filter tool does not e"tract the records0 it simply hides therecords that don2t match the criteria. 4owever, you can copy andpaste the data to another 1"cel worksheet, >icrosoft ?orddocument, or a ower-oint presentation.

!o restore the original data file, click on the drop-down arrow againand then click /lear filter from 9tem Description.:


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3uppose we wish to identify all records in the ,urchase !rdersdatabase whose item cost is at least N66. #irst, click on the drop-down arrow in the tem /ost column and position the cursor over"umbers Filter. !his displays a list of options. 3elect /reater Than!r E0ual To . . . from the list.


Cost


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!he Custom utoFilterdialog allows you to specify up to twospecific criteria using 9and: and 9or: logic. 1nter 66 in the bo" asshown0 the tool will display all records having an item cost of N66or more.


Cost


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utoFiltercreates filtering criteria based on the type ofdata being filtered. f you choose to filter on ;rder Dateor $rrival Date, theutoFiltertools will display a differentDate Filters menu list for filtering that includes9tomorrow,: 9ne"t week,: 9year to date,: and so on.

utoFilter can be used sequentially to 9drill down: intothe data. #or e"ample, after filtering the results by *olt-nut package, we

could then filter by order date and select all orders processed in

3eptember.

About theAutoFilter


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'tatisti!sis both the science o% uncertainty andthe technology o% e1tracting in%ormation %rom data.

$ statisti!is a summary measure of data. Des!riptive statisti!s are methods that describe

and summarize data. >icrosoft 1"cel supports statistical analysis in two

ways7

. 3tatistical functions .nalysis Toolpak add-in

'tatisti!al ethods "or 'ummarizing Data


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$ "re6uen!y distribution is a table that showsthe number of observations in each of severalnonoverlapping groups. /ategorical variables naturally define the groups in a

frequency distribution. !o construct a frequency distribution, we need

only count the number of observations thatappear in each category. !his can be done using the 1"cel /;5F!# function.

+re6uen!y Distributions "or Categori!al

Data


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Example 3.17: Constru!ting a +re6uen!y Distribution

"or ,tems in the Purchase Orders Database

@ist the item names in a column on the spreadsheet. 5se the function I/;5F!#(NDNB7NDNL&,cell2re%erence),

where cell2re%erenceis the cell containing the item name


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Example 3.17: Constru!ting a +re6uen!y Distribution

"or ,tems in the Purchase Orders Database

/onstruct a column chart to visualize the frequencies.


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Example 3.18: Constru!ting a 4elative +re6uen!y

Distribution "or ,tems in the Purchase Orders Database

#irst, sum the frequencies to find the total number (notethat the sum of the frequencies must be the same as thetotal number of observations, n).

!hen divide the frequency of each category by thisvalue.


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#or numerical data that consist of a small numberof discrete values, we may construct a frequencydistribution similar to the way we did for

categorical data0 that is, we simply use /;5F!#to count the frequencies of each discrete value.

+re6uen!y Distributions "or 9umeri!al

Data


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n the ,urchase !rders data, the $ terms are allwhole numbers , , +6, and B.

Example 3.1*: +re6uen!y and 4elative

+re6uen!y Distribution "or A& Terms


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$ graphical depiction of a frequency distributionfor numerical data in the form of a column chart iscalled a histogram.

#requency distributions and histograms can becreated using thenalysis Toolpak in 1"cel. /lick the Data nalysis tools button in thenalysisgroup

under the Datatab in the 1"cel menu bar and selectHistogramfrom the list.

Ex!el HistogramTool


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3pecify the Input #ange corresponding to the data. f you includethe column header, then also check the Labelsbo" so 1"cel knowsthat the range contains a label. !he Bin #ange defines the groups(1"cel calls these 9bins:) used for the frequency distribution.

;istogram Dialog


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f you do not specify a Bin #ange, 1"cel willautomatically determine bin values for the frequencydistribution and histogram, which often results in a ratherpoor choice.

f you have discrete values, set up a column of these

values in your spreadsheet for the bin range and specifythis range in the Bin #ange field.


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?e will create a frequency distribution and histogram forthe $ !erms variable in the ,urchase !rdersdatabase.

?e defined the bin range below the data in cells

4LL746+ as follows7>onth

+6 B

Example 3.1:


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Histogramtool results7

Example 3.1:


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#or numerical data that have many different discrete values withlittle repetition or are continuous, a frequency distribution requiresthat we define by specifying

. the number of groups,

. the width of each group, and

+. the upper and lower limits of each group.. /hoose between to groups, and the range of each should be

equal.. /hoose the lower limit of the first group (@@) as a whole number

smaller than the minimum data value and the upper limit of the last

group (5@) as a whole number larger than the ma"imum data value.

;istograms "or 9umeri!al Data


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!he data range from a minimum of N%J.& to a ma"imum ofN&,660 set the lower limit of the first group to N6 and the upperlimit of the last group to N+6,666.

f we select groups, using equation (+.) the width of each groupis (N+6,666 - 6) I N%,666

Example 3.$: Constru!ting a +re6uen!y

Distribution and ;istogram "or Cost per )rder


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!en-group histogram

Example 3.$: Constru!ting a +re6uen!y

Distribution and ;istogram "or Cost per )rder


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3et the cumulative relative frequency of the first group equal to itsrelative frequency. !hen add the relative frequency of the ne"t groupto the cumulative relative frequency.

#or, e"ample, the cumulative relative frequency in cell D+ iscomputed as IDO/+ I 6.666 O 6.BB& I 6.BB&.

Example 3.$1 Computing Cumulative

4elative +re6uen!ies


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!he kthpercentile is a value at or below which at least kpercent of the observations lie. !he most common way tocompute the kthpercentile is to order the data values fromsmallest to largest and calculate the rank of the k thpercentileusing the formula7

3tatistical software use different methods that often involveinterpolating between ranks instead of rounding, thusproducing different results. !he 1"cel function 1


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/ompute the L6thpercentile for Cost per order inthe ,urchase !rders data.


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Data .Data nalysis .

#ank and ,ercentile

L6.+rd

percentile I N&B,+&

(same result asmanually computing

the L6

th

percentile)

Example 3.$ Ex!el Rank and Percentile

Tool

!he 1"cel value of the L6thpercentile that was computed in1"ample +.+ as N&B,+& is the L6.+rdpercentile value.


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/uartilesbreak the data into four parts. !he th percentile is called the first quartile,P0

the 6th percentile is called the second quartile, P0

the &th percentile is called the third quartile, P+0 and

the 66th percentile is the fourth quartile, PB.

;ne-fourth of the data fall below the first quartile, one-half are below the second quartile, and three-fourths arebelow the third quartile.

1"cel function P5$

/uartiles

E l 3 $5 C ti / til i


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/ompute the Puartiles of the Cost per !rder data #irst quartile7 IP5$

!hird quartile7 IP5$

#ourth quartile7 IP5$

Example 3.$5 Computing /uartiles in

Ex!el


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$ !ross2tabulation is a tabular method that displays thenumber of observations in a data set for differentsubcategories of two categorical variables. $ cross-tabulation table is often called a !ontingen!y table.

!he subcategories of the variables must be mutuallye"clusive and e"haustive, meaning that eachobservation can be classified into only one subcategory,and, taken together over all subcategories, they mustconstitute the complete data set.

Cross2Tabulations

E l 3 $7 C t ti C


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Sales Transactionsdatabase

/ount the number (and compute the percentage) ofbooks and DEDs ordered by region.

Example 3.$7: Constru!ting a Cross2

Tabulation

C T b l ti Vi li ti Ch t "


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Cross2Tabulation Visualization: Chart o"

4egional 'ales by &rodu!t


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1"cel provides a powerful tool for distilling acomple" data set into meaningful information7&ivotTables.

ivot!ables allows you to create custom

summaries and charts of key information in thedata.

ivot!ables can be used to quickly create cross-tabulations and to drill down into a large set ofdata in numerous ways.

Exploring Data


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/lick inside yourdatabaseInsert .

Tables .

,i*otTable

!he wizard creates ablank ivot!able as

shown.

Constru!ting &ivotTables


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3elect and drag thefields to one of theivot!able areas7 #eport Filter Column Labels #o& Labels 7alues

&ivotTable +ield %ist


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nitial ivot!ablefor


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cti*e Field . naly8e .Field Settings /hange summarization

method in 7alue Field

Settingsdialog bo" 3elect Count

Changing Value +ield 'ettings


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+inal &ivot Table


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5ncheck the bo"es inthe ,i*otTable FieldList or drag the fieldnames to differentareas.

Aou may easily addmultiple variables inthe fields to createdifferent views of the

data. 1"ample7 drag the

Sourcefield into the #o&Labelsarea

odi"ying &ivotTables

Example 3 $*:


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Dragging a field into the


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&ivotChartsvisualize data in ivot!ables. !hey can be created in a simple one-click fashion.

3elect the ivot!able

#rom the analyze tab, click ,i*otChart.

1"cel will display an Insert Chart dialog that allows you tochoose the type of chart you wish to display.

&ivotCharts

Example 3 $: A &ivotChart "or 'ales


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Example 3.$: A &ivotChart "or 'ales

Data

*y clicking on the drop-down buttons, you can easily change thedata that are displayed. by filtering the data. $lso, by clicking on the

chart and selecting the ,i*otChart Tools Design tab, you can switchthe rows and columns to display an alternate view of the chart orchange the chart type entirely.


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1"cel 66 introduced sli!ers= tools for drillingdown to 9slice: a ivot!able and display a subsetof data.

!o create a slicer for any of the columns in the

database, click on the ivot!able and chooseInsert Slicerfrom thenaly8etab in the ,i*otTableToolsribbon.

'li!ers


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Example 3.3


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!he camera tool is useful for creating ivot!able-based dashboards.

f you create several different ivot!ables andcharts, you can easily use the camera tool to take

pictures of them and consolidate them onto oneworksheet.

n this fashion, you can still make changes to theivot!ables and they will automatically bereflected in the camera shots.

&ivotTable Dashboards


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Camera2#ased Dashboard Example

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Evans Analytics2e Ppt 03