This document discusses exploring and visualizing data in Microsoft Excel. It covers topics such as creating charts, sorting and filtering data, statistical analysis methods for summarizing data, and using PivotTables and PivotCharts. Examples demonstrate how to construct frequency distributions, calculate percentiles and quartiles, filter records, and create cross-tabulations and charts from a structured data set.
Statistics is both the science of uncertainty and the technology.docxrafaelaj1
Statistics is both the science of uncertainty and the technology of extracting information from data.
A statistic is a summary measure of data.
Descriptive statistics are methods that describe and summarize data.
Microsoft Excel supports statistical analysis in two ways:
1. Statistical functions
2. Analysis Toolpak add-in
Statistical Methods for Summarizing Data
A frequency distribution is a table that shows the number of observations in each of several nonoverlapping groups.
Categorical variables naturally define the groups in a frequency distribution.
To construct a frequency distribution, we need only count the number of observations that appear in each category.
This can be done using the Excel COUNTIF function.
Frequency Distributions for Categorical Data
Example 3.16: Constructing a Frequency Distribution for Items in the Purchase Orders Database
List the item names in a column on the spreadsheet.
Use the function =COUNTIF($D$4:$D$97,cell_reference), where cell_reference is the cell containing the item name
Example 3.16: Constructing a Frequency Distribution for Items in the Purchase Orders Database
Construct a column chart to visualize the frequencies.
Relative frequency is the fraction, or proportion, of the total.
If a data set has n observations, the relative frequency of category i is:
We often multiply the relative frequencies by 100 to express them as percentages.
A relative frequency distribution is a tabular summary of the relative frequencies of all categories.
Relative Frequency Distributions
Example 3.17: Constructing a Relative Frequency Distribution for Items in the Purchase Orders Database
First, sum the frequencies to find the total number (note that the sum of the frequencies must be the same as the total number of observations, n).
Then divide the frequency of each category by this value.
For numerical data that consist of a small number of discrete values, we may construct a frequency distribution similar to the way we did for categorical data; that is, we simply use COUNTIF to count the frequencies of each discrete value.
Frequency Distributions for Numerical Data
In the Purchase Orders data, the A/P terms are all whole numbers 15, 25, 30, and 45.
Example 3.18: Frequency and Relative Frequency Distribution for A/P Terms
A graphical depiction of a frequency distribution for numerical data in the form of a column chart is called a histogram.
Frequency distributions and histograms can be created using the Analysis Toolpak in Excel.
Click the Data Analysis tools button in the Analysis group under the Data tab in the Excel menu bar and select Histogram from the list.
Excel Histogram Tool
Specify the Input Range corresponding to the data. If you include the column header, then also check the Labels box so Excel knows that the range contains a label. The Bin Range defines the groups (Excel calls these “bins”) used for the frequency distribution.
Histogra.
A matrix diagram is a visual representation that shows the connection between various groups of data. It is a planning tool that essentially displays the existence and strength of relationships between pairs of items from two or more data sets. It aims to help in problem-solving, decision making, and process improvement efforts.
The document discusses various techniques for handling data in Excel, including entering data manually or importing it, sorting and filtering data, using subtotals and pivot tables to summarize data, and formatting options. Key techniques covered include importing tab-delimited files, sorting data by clicking Data > Sort, filtering data using Data > Autofilter, creating pivot tables by selecting the data source and dragging field buttons, and formatting cells using conditional formats.
This document provides an overview of data visualization techniques. It discusses the uses of data visualization such as identifying errors and highlighting relationships in data. It also covers different types of charts (e.g. line charts, bar charts, pie charts) and tables as well as principles for effective data visualization like maximizing the data-ink ratio. Advanced techniques like parallel coordinate plots, treemaps and geographic information systems (GIS) charts are also introduced. Finally, the document discusses data dashboards and principles for effective dashboard design.
This document provides an overview of sorting algorithms. It begins by defining sorting and discussing characteristics like indirect sorting, distribution sorting, and stable sorting. It then explains several common sorting algorithms like selection sort, insertion sort, quicksort, bucket sort, radix sort, and merge sort. For each algorithm, it provides examples to illustrate how the algorithm works step-by-step to sort a list of numbers. The document aims to help readers understand different approaches to sorting and how to select the appropriate algorithm for a given sorting problem.
1. (TCO 1) Which of the following sets of SQL clauses represent the minimum combination of clauses to make a working SQL statement? (Points : 5)
SELECT, WHERE
FROM, WHERE
SELECT, FROM
FROM, ORDER BY
Introduction to Business analytics unit3jayarellirs
This document discusses various methods for visualizing and summarizing data. It describes different types of charts like column charts, line charts, pie charts, and scatter plots that can be used to visualize quantitative data. It also discusses tools in Excel for filtering, sorting, and summarizing data in tables and how techniques like Pareto analysis can help identify key factors.
Have you ever wonder how Excel sets the upper limit and the lower limit on th...Jennifer ChiaYu Lin
#Data Visualization #algorithm #Infographic
Have you ever wonder how Excel sets the upper limit and the lower limit on the vertical axis of a chart? And how this may lead to a misleading chart?
In my own case, I have not, until one day I found an obvious mistake on Excel’s dual vertical axes chart.
The mistake is resulted from that Excel does not have an algorithm that can address the most important and inevitable question for dual vertical axes charts: “How to set the upper limits and the lower limits on the TWO vertical axes?”. In fact, Excel simply adopts the same algorithm used for its single vertical axis chart on each vertical axis separately. And thus the elongations of the two axes are not coordinated to be the same, which leads to its misleading dual vertical axes charts.
To solve this critical mistake, Graphician invented a patented algorithm that can create 100% correct dual vertical axes chart. And we have also created a trial Excel Add-in which can adjust any dual vertical axes chart created by Excel 2007 or an advanced version with one single click.
You can now download the Add-in at http://www.graphician.com/patent-01.html. We hope you find the Add-in interesting and useful, and we would love to hear your comment about it if any. You may contact us at graphician1122@gmail.com or visit our website: "www.graphician.com" to find more information.
Statistics is both the science of uncertainty and the technology.docxrafaelaj1
Statistics is both the science of uncertainty and the technology of extracting information from data.
A statistic is a summary measure of data.
Descriptive statistics are methods that describe and summarize data.
Microsoft Excel supports statistical analysis in two ways:
1. Statistical functions
2. Analysis Toolpak add-in
Statistical Methods for Summarizing Data
A frequency distribution is a table that shows the number of observations in each of several nonoverlapping groups.
Categorical variables naturally define the groups in a frequency distribution.
To construct a frequency distribution, we need only count the number of observations that appear in each category.
This can be done using the Excel COUNTIF function.
Frequency Distributions for Categorical Data
Example 3.16: Constructing a Frequency Distribution for Items in the Purchase Orders Database
List the item names in a column on the spreadsheet.
Use the function =COUNTIF($D$4:$D$97,cell_reference), where cell_reference is the cell containing the item name
Example 3.16: Constructing a Frequency Distribution for Items in the Purchase Orders Database
Construct a column chart to visualize the frequencies.
Relative frequency is the fraction, or proportion, of the total.
If a data set has n observations, the relative frequency of category i is:
We often multiply the relative frequencies by 100 to express them as percentages.
A relative frequency distribution is a tabular summary of the relative frequencies of all categories.
Relative Frequency Distributions
Example 3.17: Constructing a Relative Frequency Distribution for Items in the Purchase Orders Database
First, sum the frequencies to find the total number (note that the sum of the frequencies must be the same as the total number of observations, n).
Then divide the frequency of each category by this value.
For numerical data that consist of a small number of discrete values, we may construct a frequency distribution similar to the way we did for categorical data; that is, we simply use COUNTIF to count the frequencies of each discrete value.
Frequency Distributions for Numerical Data
In the Purchase Orders data, the A/P terms are all whole numbers 15, 25, 30, and 45.
Example 3.18: Frequency and Relative Frequency Distribution for A/P Terms
A graphical depiction of a frequency distribution for numerical data in the form of a column chart is called a histogram.
Frequency distributions and histograms can be created using the Analysis Toolpak in Excel.
Click the Data Analysis tools button in the Analysis group under the Data tab in the Excel menu bar and select Histogram from the list.
Excel Histogram Tool
Specify the Input Range corresponding to the data. If you include the column header, then also check the Labels box so Excel knows that the range contains a label. The Bin Range defines the groups (Excel calls these “bins”) used for the frequency distribution.
Histogra.
A matrix diagram is a visual representation that shows the connection between various groups of data. It is a planning tool that essentially displays the existence and strength of relationships between pairs of items from two or more data sets. It aims to help in problem-solving, decision making, and process improvement efforts.
The document discusses various techniques for handling data in Excel, including entering data manually or importing it, sorting and filtering data, using subtotals and pivot tables to summarize data, and formatting options. Key techniques covered include importing tab-delimited files, sorting data by clicking Data > Sort, filtering data using Data > Autofilter, creating pivot tables by selecting the data source and dragging field buttons, and formatting cells using conditional formats.
This document provides an overview of data visualization techniques. It discusses the uses of data visualization such as identifying errors and highlighting relationships in data. It also covers different types of charts (e.g. line charts, bar charts, pie charts) and tables as well as principles for effective data visualization like maximizing the data-ink ratio. Advanced techniques like parallel coordinate plots, treemaps and geographic information systems (GIS) charts are also introduced. Finally, the document discusses data dashboards and principles for effective dashboard design.
This document provides an overview of sorting algorithms. It begins by defining sorting and discussing characteristics like indirect sorting, distribution sorting, and stable sorting. It then explains several common sorting algorithms like selection sort, insertion sort, quicksort, bucket sort, radix sort, and merge sort. For each algorithm, it provides examples to illustrate how the algorithm works step-by-step to sort a list of numbers. The document aims to help readers understand different approaches to sorting and how to select the appropriate algorithm for a given sorting problem.
1. (TCO 1) Which of the following sets of SQL clauses represent the minimum combination of clauses to make a working SQL statement? (Points : 5)
SELECT, WHERE
FROM, WHERE
SELECT, FROM
FROM, ORDER BY
Introduction to Business analytics unit3jayarellirs
This document discusses various methods for visualizing and summarizing data. It describes different types of charts like column charts, line charts, pie charts, and scatter plots that can be used to visualize quantitative data. It also discusses tools in Excel for filtering, sorting, and summarizing data in tables and how techniques like Pareto analysis can help identify key factors.
Have you ever wonder how Excel sets the upper limit and the lower limit on th...Jennifer ChiaYu Lin
#Data Visualization #algorithm #Infographic
Have you ever wonder how Excel sets the upper limit and the lower limit on the vertical axis of a chart? And how this may lead to a misleading chart?
In my own case, I have not, until one day I found an obvious mistake on Excel’s dual vertical axes chart.
The mistake is resulted from that Excel does not have an algorithm that can address the most important and inevitable question for dual vertical axes charts: “How to set the upper limits and the lower limits on the TWO vertical axes?”. In fact, Excel simply adopts the same algorithm used for its single vertical axis chart on each vertical axis separately. And thus the elongations of the two axes are not coordinated to be the same, which leads to its misleading dual vertical axes charts.
To solve this critical mistake, Graphician invented a patented algorithm that can create 100% correct dual vertical axes chart. And we have also created a trial Excel Add-in which can adjust any dual vertical axes chart created by Excel 2007 or an advanced version with one single click.
You can now download the Add-in at http://www.graphician.com/patent-01.html. We hope you find the Add-in interesting and useful, and we would love to hear your comment about it if any. You may contact us at graphician1122@gmail.com or visit our website: "www.graphician.com" to find more information.
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.
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.
This document outlines a training overview for a Microsoft Excel extended introduction course. The course consists of 6 classes covering topics like terminology, navigation, formatting, functions, macros, importing data, and charts. Each class is scheduled for a different date and includes the topics that will be covered, such as formatting, sorting, filtering, and different types of functions like date, logical, and statistical functions.
Queries allow users to extract specific information from one or more database tables. There are different ways to create queries, including using design view, a wizard, or SQL view. Queries can include calculations, formatting, parameters, and summaries to provide flexible reporting of essential data.
MS Access allows users to create and manage databases. A database contains tables which store records with fields of different data types. Queries can extract specific information from tables. Forms and reports present data to users. Relational databases store related tables and allow combining data through common fields. Users can create tables, queries, forms and reports in Access to enter, organize and present data.
DATA VISUALIZATION FOR MANAGERS MODULE 4| Creating Calculations to Enhance Data| BUSINESS ANALYTICS PAPER 1 |MBA SEM 3| RTMNU NAGPUR UNIVERSITY| BY JAYANTI R PANDE
MBA Notes by Jayanti Pande
#JayantiPande
#MBA
#MBAnotes
#BusinessAnalyticsNotes
1. You are given only three quarterly seasonal indices and qua.docxKiyokoSlagleis
1. You are given only three quarterly seasonal indices and quarterly seasonally adjusted data for the entire year. What is the raw data value for Q4? Raw data is not adjusted for seasonality.
Quarter Seasonal Index Seasonally Adjusted Data
Q1 .80 295
Q2 .85 299
Q3 1.15 270
Q4 --- 271
(Points : 3)
[removed]
325
[removed]
225
[removed]
252
[removed]
271
Question 2.
2.
One model of exponential smoothing will provide almost the same forecast as a liner trend method. What are linear trend intercept and slope counterparts for exponential smoothing?
[removed]
Alpha and Delta
[removed]
Delta and Gamma
[removed]
Alpha and Gamma
[removed]
Std Dev and Mean
Question 3.
3.
Why is the residual mean value important to a forecaster?
(Points : 3)
[removed]
Large mean values indicate nonautoregressiveness.
[removed]
Small mean values indicate the total amount of error is small
.
[removed]
Large absolute mean values indicate estimate bias
.
[removed]
Large mean values indicate the standard error of the model is small.
Question 4.
4.
When performing correlation analysis what is the null hypothesis? What measure in Minitab is used to test it and to be 95% confident in the significance of correlation coefficient.
(Points : 3)
[removed]
Ho: r = .05 p < .5
[removed]
Ho: r = 1 p =.05
[removed]
Ho: r ≠ 0 p≤.05
[removed]
Ho: r = 0 p≤.05
Question 5.
5.
In decomposition what does the cycle factor (CF) of .80 represent for a monthly forecast estimate of a Y variable?
(Points : 3)
[removed]
The estimated value is 80% of the average monthly seasonal estimate
.
[removed]
The estimate is .80 of the forecasted Y trend value
.
[removed]
The estimated value is .80 of the historical average CMA values.
[removed]
The estimated value has 20% more variation than the average historical Y data values
.
Question 6.
6.
A Burger King franchise owner notes that the sales per store has fallen below the stated national Burger King outlet average of $1,258,000. He asserts a change has occurred that reduced the fast food eating habits of Americans. What is his hypothesis (H1) and what type of test for significance must be applied?
(Points : 3)
[removed]
H1: u ≥ $1.258,000 A one-tailed t-test to the left
.
[removed]
H1: u = $1.258,000 A two-tailed t-test.
[removed]
H1: u < $1.258,000 A one-tailed t-test to the left.
[removed]
H1: p < $1.258,000 A one-tailed test to the right
.
Question 7.
7.
The CEO of Home Depot wants to see if city size has any relationship to the current profit margins of the company stores. What data type will he likely use to determine this?
(Points : 3)
[removed]
Time series data of profits by store.
[removed]
Recent 10 year sample of profits by store.
Firebird: cost-based optimization and statistics, by Dmitry Yemanov (in English)Alexey Kovyazin
This document discusses cost-based optimization and statistics in Firebird. It covers:
1) Rule-based optimization uses heuristics while cost-based optimization uses statistical data to estimate the cost of different access paths and choose the most efficient.
2) Statistics like selectivity, cardinality, and histograms help estimate costs by providing information on data distribution and amounts.
3) The optimizer aggregates costs from the bottom up and chooses the access path with the lowest total cost based on the statistical information.
Download Complete Material - https://www.instamojo.com/prashanth_ns/
This Advanced Excel - Office 2010 contains 12 Units and each unit contains 40 to 60 slides in it.
Contents…
• Manage cell and range names
• Calculate data across worksheets
• Use specialized functions
• Analyze data with logical and lookup functions
• Create and modify tables and Format tables
• Sort or filter worksheet or table data
• Calculate data in a table or worksheet
• Create, Modify and Format chart
• Create a PivotTable report
• Analyze data using PivotCharts
• Insert and modify pictures and ClipArt
• Draw and modify shapes
• Illustrate workflow using SmartArt graphics
• Layer and group graphic objects
• Customize the Excel environment
• Customize workbooks
• Manage themes and Create and use templates
• Apply conditional formatting
• Add data validation criteria
• Update a workbook’s properties
• Modify Excel’s default settings
• Share a workbook
• Set revision tracking and Review tracked revisions
• Merge workbooks
• Administer digital signatures
• Restrict document access
• Trace precedents and dependents of a cell
• Troubleshoot errors in formulas and invalid data and formulas
• Watch and evaluate formulas
• Create a data list outline, a trend line and scenarios
• Perform what-if analysis and statistical analysis
• Create a workspace and Consolidate data
• Link cells in different workbooks
• Edit links and Export Excel data
• Import a delimited text file
• Publish a worksheet to the web
• Import data from the web
• Create a web query
• Develop XML maps and Import and export XML data
Question 1. 1.You are given only three quarterly seasonal indi.docxteofilapeerless
Question 1.
1.
You are given only three quarterly seasonal indices and quarterly seasonally adjusted data for the entire year. What is the raw data value for Q4? Raw data is not adjusted for seasonality.
Quarter Seasonal Index Seasonally Adjusted Data
Q1 .80 295
Q2 .85 299
Q3 1.15 270
Q4 --- 271
(Points : 3)
325
225
252
271
Question 2.
2.
One model of exponential smoothing will provide almost the same forecast as a liner trend method. What are linear trend intercept and slope counterparts for exponential smoothing? (Points : 3)
Alpha and Delta
Delta and Gamma
Alpha and Gamma
Std Dev and Mean
Question 3.
3.
Why is the residual mean value important to a forecaster? (Points : 3)
Large mean values indicate nonautoregressiveness.
Small mean values indicate the total amount of error is small.
Large absolute mean values indicate estimate bias.
Large mean values indicate the standard error of the model is small.
Question 4.
4.
When performing correlation analysis what is the null hypothesis? What measure in Minitab is used to test it and to be 95% confident in the significance of correlation coefficient. (Points : 3)
Ho: r = .05 p < .5
Ho: r = 1 p =.05
Ho: r ≠ 0 p≤.05
Ho: r = 0 p≤.05
Question 5.
5.
In decomposition what does the cycle factor (CF) of .80 represent for a monthly forecast estimate of a Y variable? (Points : 3)
The estimated value is 80% of the average monthly seasonal estimate.
The estimate is .80 of the forecasted Y trend value.
The estimated value is .80 of the historical average CMA values.
The estimated value has 20% more variation than the average historical Y data values.
Question 6.
6.
A Burger King franchise owner notes that the sales per store has fallen below the stated national Burger King outlet average of $1,258,000. He asserts a change has occurred that reduced the fast food eating habits of Americans. What is his hypothesis (H1) and what type of test for significance must be applied? (Points : 3)
H1: u ≥ $1.258,000 A one-tailed t-test to the left.
H1: u = $1.258,000 A two-tailed t-test.
H1: u < $1.258,000 A one-tailed t-test to the left.
H1: p < $1.258,000 A one-tailed test to the right.
Question 7.
7.
The CEO of Home Depot wants to see if city size has any relationship to the current profit margins of the company stores. What data type will he likely use to determine this?
(Points : 3)
Time series data of profits by store.
Recent 10 year sample of profits by stores.
Recent cross section of store profits by city.
Trend of a random sample of store profits over time.
Question 8.
8.
Sometimes forecasters get lazy or forgetful and do not.
William Schaffrans Bus Intelligence Portfoliowschaffr
This document provides an overview and examples of the author's work with Microsoft's Business Intelligence Suite, including SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), SQL Server Reporting Services (SSR), Performance Point Server 2007 (PPS), and Microsoft Office SharePoint Server (MOSS). It showcases various packages, data flows, cubes, dimensions, measures, reports, scorecards, and dashboards created by the author using these tools to analyze and report on business data.
This chapter discusses various methods for organizing and presenting data visually, including tables, graphs, and charts. It covers techniques for numerical data such as frequency distributions, histograms, polygons, and scatter diagrams. For categorical data, it discusses summary tables and charts such as bar charts and pie charts. The goal is to condense raw data into more useful forms that facilitate interpretation and decision making.
This document discusses methods for organizing and presenting qualitative and quantitative data using frequency tables, charts, and graphs. It covers:
1. Creating frequency tables to organize qualitative and quantitative data, and presenting qualitative data as bar charts or pie charts.
2. Constructing frequency distributions to organize quantitative data into class intervals and determining class frequencies, and presenting quantitative data using histograms, frequency polygons, and cumulative frequency polygons.
3. An example of creating a frequency table and histogram based on sales price data from 80 vehicles to compare typical selling prices on dealer lots.
Introduction to Datamining Concept and TechniquesSơn Còm Nhom
This document provides an introduction to data mining techniques. It discusses data mining concepts like data preprocessing, analysis, and visualization. For data preprocessing, it describes techniques like similarity measures, down sampling, and dimension reduction. For data analysis, it explains clustering, classification, and regression methods. Specifically, it gives examples of k-means clustering and support vector machine classification. The goal of data mining is to retrieve hidden knowledge and rules from data.
This document discusses various methods for organizing and presenting categorical and numerical data using tables, charts, and graphs. It covers summarizing categorical data using summary tables, bar charts, pie charts, and Pareto diagrams. For numerical data, it discusses organizing data using ordered arrays, stem-and-leaf displays, frequency distributions, histograms, frequency polygons, ogives, contingency tables, side-by-side bar charts, and scatter plots. The goal is to effectively communicate patterns and relationships in the data.
Graphs, charts, and tables ppt @ bec domsBabasab Patil
This document discusses various methods for organizing and presenting quantitative data, including frequency distributions, histograms, stem-and-leaf diagrams, pie charts, bar charts, line charts, scatter plots, and strategies for grouping continuous data into classes. Key topics covered include constructing frequency distributions, interpreting relative frequencies, guidelines for determining class widths and intervals, and using graphs and charts to visualize categorical and multivariate data.
1. You are given only three quarterly seasonal indices and quarter.docxjackiewalcutt
1. You are given only three quarterly seasonal indices and quarterly seasonally adjusted data for the entire year. What is the raw data value for Q4? Raw data is not adjusted for seasonality.
Quarter Seasonal Index Seasonally Adjusted Data
Q1 .80 295
Q2 .85 299
Q3 1.15 270
Q4 --- 271
(Points : 3)
325
225
252
271
Question 2. 2. One model of exponential smoothing will provide almost the same forecast as a liner trend method. What are linear trend intercept and slope counterparts for exponential smoothing? (Points : 3)
Alpha and Delta
Delta and Gamma
Alpha and Gamma
Std Dev and Mean
Question 3. 3. Why is the residual mean value important to a forecaster? (Points : 3)
Large mean values indicate nonautoregressiveness.
Small mean values indicate the total amount of error is small.
Large absolute mean values indicate estimate bias. Large mean values indicate the standard error of the model is small.
Question 4. 4. When performing correlation analysis what is the null hypothesis? What measure in Minitab is used to test it and to be 95% confident in the significance of correlation coefficient. (Points : 3)
Ho: r = .05 p < .5
Ho: r = 1 p =.05
Ho: r ≠ 0 p≤.05
Ho: r = 0 p≤.05
Question 5. 5. In decomposition what does the cycle factor (CF) of .80 represent for a monthly forecast estimate of a Y variable? (Points : 3)
The estimated value is 80% of the average monthly seasonal estimate.
The estimate is .80 of the forecasted Y trend value.
The estimated value is .80 of the historical average CMA values.
The estimated value has 20% more variation than the average historical Y data values.
Question 6. 6. A Burger King franchise owner notes that the sales per store has fallen below the stated national Burger King outlet average of $1,258,000. He asserts a change has occurred that reduced the fast food eating habits of Americans. What is his hypothesis (H1) and what type of test for significance must be applied? (Points : 3)
H1: u ≥ $1.258,000 A one-tailed t-test to the left.
H1: u = $1.258,000 A two-tailed t-test.
H1: u < $1.258,000 A one-tailed t-test to the left.
H1: p < $1.258,000 A one-tailed test to the right.
Question 7. 7.
The CEO of Home Depot wants to see if city size has any relationship to the current profit margins of the company stores. What data type will he likely use to determine this?
(Points : 3)
Time series data of profits by store.
Recent 10 year sample of profits by stores.
Recent cross section of store profits by city.
Trend of a random sample of store profits over time.
Question 8. 8. Sometimes forecasters get lazy or forgetful and do not check the significance of XY data correlations ...
This document provides an overview and instructions for creating queries in Microsoft Access. It covers using the Simple Query Wizard to create basic queries, sorting and filtering data, creating queries in Design view, establishing relationships between tables, building multitable queries, and using operators and calculations in queries. The objectives, vocabulary, and step-by-step instructions aim to teach students how to extract and work with specific data from an Access database.
Grouping and Displaying Data to Convey Meaning: Tables & Graphs chapter_2 _fr...Prashant Borkar
This presentation is about Grouping and Displaying Data to Convey Meaning: Tables and Graphs
Contents were taken from Statistics for Management by Levin & Rubin.
Presentation includes,
How can we Arrange Data?
Raw Data
Arranging Data using Data Array & Frequency Distribution
Constructing a Frequency Distribution
Graphing Frequency Distributions
It also covers some solved examples of it.
This chapter introduces descriptive statistics. It aims to study basic statistical concepts including variables, measures of central tendency, and measures of dispersion. For measures of central tendency, it discusses how to calculate the mean, median, and mode for both ungrouped and grouped data. It also introduces how to calculate variance and standard deviation as measures of dispersion. Examples are provided to demonstrate calculating these descriptive statistics for raw data sets.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
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.
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.
This document outlines a training overview for a Microsoft Excel extended introduction course. The course consists of 6 classes covering topics like terminology, navigation, formatting, functions, macros, importing data, and charts. Each class is scheduled for a different date and includes the topics that will be covered, such as formatting, sorting, filtering, and different types of functions like date, logical, and statistical functions.
Queries allow users to extract specific information from one or more database tables. There are different ways to create queries, including using design view, a wizard, or SQL view. Queries can include calculations, formatting, parameters, and summaries to provide flexible reporting of essential data.
MS Access allows users to create and manage databases. A database contains tables which store records with fields of different data types. Queries can extract specific information from tables. Forms and reports present data to users. Relational databases store related tables and allow combining data through common fields. Users can create tables, queries, forms and reports in Access to enter, organize and present data.
DATA VISUALIZATION FOR MANAGERS MODULE 4| Creating Calculations to Enhance Data| BUSINESS ANALYTICS PAPER 1 |MBA SEM 3| RTMNU NAGPUR UNIVERSITY| BY JAYANTI R PANDE
MBA Notes by Jayanti Pande
#JayantiPande
#MBA
#MBAnotes
#BusinessAnalyticsNotes
1. You are given only three quarterly seasonal indices and qua.docxKiyokoSlagleis
1. You are given only three quarterly seasonal indices and quarterly seasonally adjusted data for the entire year. What is the raw data value for Q4? Raw data is not adjusted for seasonality.
Quarter Seasonal Index Seasonally Adjusted Data
Q1 .80 295
Q2 .85 299
Q3 1.15 270
Q4 --- 271
(Points : 3)
[removed]
325
[removed]
225
[removed]
252
[removed]
271
Question 2.
2.
One model of exponential smoothing will provide almost the same forecast as a liner trend method. What are linear trend intercept and slope counterparts for exponential smoothing?
[removed]
Alpha and Delta
[removed]
Delta and Gamma
[removed]
Alpha and Gamma
[removed]
Std Dev and Mean
Question 3.
3.
Why is the residual mean value important to a forecaster?
(Points : 3)
[removed]
Large mean values indicate nonautoregressiveness.
[removed]
Small mean values indicate the total amount of error is small
.
[removed]
Large absolute mean values indicate estimate bias
.
[removed]
Large mean values indicate the standard error of the model is small.
Question 4.
4.
When performing correlation analysis what is the null hypothesis? What measure in Minitab is used to test it and to be 95% confident in the significance of correlation coefficient.
(Points : 3)
[removed]
Ho: r = .05 p < .5
[removed]
Ho: r = 1 p =.05
[removed]
Ho: r ≠ 0 p≤.05
[removed]
Ho: r = 0 p≤.05
Question 5.
5.
In decomposition what does the cycle factor (CF) of .80 represent for a monthly forecast estimate of a Y variable?
(Points : 3)
[removed]
The estimated value is 80% of the average monthly seasonal estimate
.
[removed]
The estimate is .80 of the forecasted Y trend value
.
[removed]
The estimated value is .80 of the historical average CMA values.
[removed]
The estimated value has 20% more variation than the average historical Y data values
.
Question 6.
6.
A Burger King franchise owner notes that the sales per store has fallen below the stated national Burger King outlet average of $1,258,000. He asserts a change has occurred that reduced the fast food eating habits of Americans. What is his hypothesis (H1) and what type of test for significance must be applied?
(Points : 3)
[removed]
H1: u ≥ $1.258,000 A one-tailed t-test to the left
.
[removed]
H1: u = $1.258,000 A two-tailed t-test.
[removed]
H1: u < $1.258,000 A one-tailed t-test to the left.
[removed]
H1: p < $1.258,000 A one-tailed test to the right
.
Question 7.
7.
The CEO of Home Depot wants to see if city size has any relationship to the current profit margins of the company stores. What data type will he likely use to determine this?
(Points : 3)
[removed]
Time series data of profits by store.
[removed]
Recent 10 year sample of profits by store.
Firebird: cost-based optimization and statistics, by Dmitry Yemanov (in English)Alexey Kovyazin
This document discusses cost-based optimization and statistics in Firebird. It covers:
1) Rule-based optimization uses heuristics while cost-based optimization uses statistical data to estimate the cost of different access paths and choose the most efficient.
2) Statistics like selectivity, cardinality, and histograms help estimate costs by providing information on data distribution and amounts.
3) The optimizer aggregates costs from the bottom up and chooses the access path with the lowest total cost based on the statistical information.
Download Complete Material - https://www.instamojo.com/prashanth_ns/
This Advanced Excel - Office 2010 contains 12 Units and each unit contains 40 to 60 slides in it.
Contents…
• Manage cell and range names
• Calculate data across worksheets
• Use specialized functions
• Analyze data with logical and lookup functions
• Create and modify tables and Format tables
• Sort or filter worksheet or table data
• Calculate data in a table or worksheet
• Create, Modify and Format chart
• Create a PivotTable report
• Analyze data using PivotCharts
• Insert and modify pictures and ClipArt
• Draw and modify shapes
• Illustrate workflow using SmartArt graphics
• Layer and group graphic objects
• Customize the Excel environment
• Customize workbooks
• Manage themes and Create and use templates
• Apply conditional formatting
• Add data validation criteria
• Update a workbook’s properties
• Modify Excel’s default settings
• Share a workbook
• Set revision tracking and Review tracked revisions
• Merge workbooks
• Administer digital signatures
• Restrict document access
• Trace precedents and dependents of a cell
• Troubleshoot errors in formulas and invalid data and formulas
• Watch and evaluate formulas
• Create a data list outline, a trend line and scenarios
• Perform what-if analysis and statistical analysis
• Create a workspace and Consolidate data
• Link cells in different workbooks
• Edit links and Export Excel data
• Import a delimited text file
• Publish a worksheet to the web
• Import data from the web
• Create a web query
• Develop XML maps and Import and export XML data
Question 1. 1.You are given only three quarterly seasonal indi.docxteofilapeerless
Question 1.
1.
You are given only three quarterly seasonal indices and quarterly seasonally adjusted data for the entire year. What is the raw data value for Q4? Raw data is not adjusted for seasonality.
Quarter Seasonal Index Seasonally Adjusted Data
Q1 .80 295
Q2 .85 299
Q3 1.15 270
Q4 --- 271
(Points : 3)
325
225
252
271
Question 2.
2.
One model of exponential smoothing will provide almost the same forecast as a liner trend method. What are linear trend intercept and slope counterparts for exponential smoothing? (Points : 3)
Alpha and Delta
Delta and Gamma
Alpha and Gamma
Std Dev and Mean
Question 3.
3.
Why is the residual mean value important to a forecaster? (Points : 3)
Large mean values indicate nonautoregressiveness.
Small mean values indicate the total amount of error is small.
Large absolute mean values indicate estimate bias.
Large mean values indicate the standard error of the model is small.
Question 4.
4.
When performing correlation analysis what is the null hypothesis? What measure in Minitab is used to test it and to be 95% confident in the significance of correlation coefficient. (Points : 3)
Ho: r = .05 p < .5
Ho: r = 1 p =.05
Ho: r ≠ 0 p≤.05
Ho: r = 0 p≤.05
Question 5.
5.
In decomposition what does the cycle factor (CF) of .80 represent for a monthly forecast estimate of a Y variable? (Points : 3)
The estimated value is 80% of the average monthly seasonal estimate.
The estimate is .80 of the forecasted Y trend value.
The estimated value is .80 of the historical average CMA values.
The estimated value has 20% more variation than the average historical Y data values.
Question 6.
6.
A Burger King franchise owner notes that the sales per store has fallen below the stated national Burger King outlet average of $1,258,000. He asserts a change has occurred that reduced the fast food eating habits of Americans. What is his hypothesis (H1) and what type of test for significance must be applied? (Points : 3)
H1: u ≥ $1.258,000 A one-tailed t-test to the left.
H1: u = $1.258,000 A two-tailed t-test.
H1: u < $1.258,000 A one-tailed t-test to the left.
H1: p < $1.258,000 A one-tailed test to the right.
Question 7.
7.
The CEO of Home Depot wants to see if city size has any relationship to the current profit margins of the company stores. What data type will he likely use to determine this?
(Points : 3)
Time series data of profits by store.
Recent 10 year sample of profits by stores.
Recent cross section of store profits by city.
Trend of a random sample of store profits over time.
Question 8.
8.
Sometimes forecasters get lazy or forgetful and do not.
William Schaffrans Bus Intelligence Portfoliowschaffr
This document provides an overview and examples of the author's work with Microsoft's Business Intelligence Suite, including SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), SQL Server Reporting Services (SSR), Performance Point Server 2007 (PPS), and Microsoft Office SharePoint Server (MOSS). It showcases various packages, data flows, cubes, dimensions, measures, reports, scorecards, and dashboards created by the author using these tools to analyze and report on business data.
This chapter discusses various methods for organizing and presenting data visually, including tables, graphs, and charts. It covers techniques for numerical data such as frequency distributions, histograms, polygons, and scatter diagrams. For categorical data, it discusses summary tables and charts such as bar charts and pie charts. The goal is to condense raw data into more useful forms that facilitate interpretation and decision making.
This document discusses methods for organizing and presenting qualitative and quantitative data using frequency tables, charts, and graphs. It covers:
1. Creating frequency tables to organize qualitative and quantitative data, and presenting qualitative data as bar charts or pie charts.
2. Constructing frequency distributions to organize quantitative data into class intervals and determining class frequencies, and presenting quantitative data using histograms, frequency polygons, and cumulative frequency polygons.
3. An example of creating a frequency table and histogram based on sales price data from 80 vehicles to compare typical selling prices on dealer lots.
Introduction to Datamining Concept and TechniquesSơn Còm Nhom
This document provides an introduction to data mining techniques. It discusses data mining concepts like data preprocessing, analysis, and visualization. For data preprocessing, it describes techniques like similarity measures, down sampling, and dimension reduction. For data analysis, it explains clustering, classification, and regression methods. Specifically, it gives examples of k-means clustering and support vector machine classification. The goal of data mining is to retrieve hidden knowledge and rules from data.
This document discusses various methods for organizing and presenting categorical and numerical data using tables, charts, and graphs. It covers summarizing categorical data using summary tables, bar charts, pie charts, and Pareto diagrams. For numerical data, it discusses organizing data using ordered arrays, stem-and-leaf displays, frequency distributions, histograms, frequency polygons, ogives, contingency tables, side-by-side bar charts, and scatter plots. The goal is to effectively communicate patterns and relationships in the data.
Graphs, charts, and tables ppt @ bec domsBabasab Patil
This document discusses various methods for organizing and presenting quantitative data, including frequency distributions, histograms, stem-and-leaf diagrams, pie charts, bar charts, line charts, scatter plots, and strategies for grouping continuous data into classes. Key topics covered include constructing frequency distributions, interpreting relative frequencies, guidelines for determining class widths and intervals, and using graphs and charts to visualize categorical and multivariate data.
1. You are given only three quarterly seasonal indices and quarter.docxjackiewalcutt
1. You are given only three quarterly seasonal indices and quarterly seasonally adjusted data for the entire year. What is the raw data value for Q4? Raw data is not adjusted for seasonality.
Quarter Seasonal Index Seasonally Adjusted Data
Q1 .80 295
Q2 .85 299
Q3 1.15 270
Q4 --- 271
(Points : 3)
325
225
252
271
Question 2. 2. One model of exponential smoothing will provide almost the same forecast as a liner trend method. What are linear trend intercept and slope counterparts for exponential smoothing? (Points : 3)
Alpha and Delta
Delta and Gamma
Alpha and Gamma
Std Dev and Mean
Question 3. 3. Why is the residual mean value important to a forecaster? (Points : 3)
Large mean values indicate nonautoregressiveness.
Small mean values indicate the total amount of error is small.
Large absolute mean values indicate estimate bias. Large mean values indicate the standard error of the model is small.
Question 4. 4. When performing correlation analysis what is the null hypothesis? What measure in Minitab is used to test it and to be 95% confident in the significance of correlation coefficient. (Points : 3)
Ho: r = .05 p < .5
Ho: r = 1 p =.05
Ho: r ≠ 0 p≤.05
Ho: r = 0 p≤.05
Question 5. 5. In decomposition what does the cycle factor (CF) of .80 represent for a monthly forecast estimate of a Y variable? (Points : 3)
The estimated value is 80% of the average monthly seasonal estimate.
The estimate is .80 of the forecasted Y trend value.
The estimated value is .80 of the historical average CMA values.
The estimated value has 20% more variation than the average historical Y data values.
Question 6. 6. A Burger King franchise owner notes that the sales per store has fallen below the stated national Burger King outlet average of $1,258,000. He asserts a change has occurred that reduced the fast food eating habits of Americans. What is his hypothesis (H1) and what type of test for significance must be applied? (Points : 3)
H1: u ≥ $1.258,000 A one-tailed t-test to the left.
H1: u = $1.258,000 A two-tailed t-test.
H1: u < $1.258,000 A one-tailed t-test to the left.
H1: p < $1.258,000 A one-tailed test to the right.
Question 7. 7.
The CEO of Home Depot wants to see if city size has any relationship to the current profit margins of the company stores. What data type will he likely use to determine this?
(Points : 3)
Time series data of profits by store.
Recent 10 year sample of profits by stores.
Recent cross section of store profits by city.
Trend of a random sample of store profits over time.
Question 8. 8. Sometimes forecasters get lazy or forgetful and do not check the significance of XY data correlations ...
This document provides an overview and instructions for creating queries in Microsoft Access. It covers using the Simple Query Wizard to create basic queries, sorting and filtering data, creating queries in Design view, establishing relationships between tables, building multitable queries, and using operators and calculations in queries. The objectives, vocabulary, and step-by-step instructions aim to teach students how to extract and work with specific data from an Access database.
Grouping and Displaying Data to Convey Meaning: Tables & Graphs chapter_2 _fr...Prashant Borkar
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Contents were taken from Statistics for Management by Levin & Rubin.
Presentation includes,
How can we Arrange Data?
Raw Data
Arranging Data using Data Array & Frequency Distribution
Constructing a Frequency Distribution
Graphing Frequency Distributions
It also covers some solved examples of it.
This chapter introduces descriptive statistics. It aims to study basic statistical concepts including variables, measures of central tendency, and measures of dispersion. For measures of central tendency, it discusses how to calculate the mean, median, and mode for both ungrouped and grouped data. It also introduces how to calculate variance and standard deviation as measures of dispersion. Examples are provided to demonstrate calculating these descriptive statistics for raw data sets.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
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State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
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https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
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Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Intelligence supported media monitoring in veterinary medicine
data-exp-Viz-00-2.pdf
1. Exploring Data and Visualizing
Demeke A. Ayele
demekeayele@gmail.com
School of Computer Science
EIC
00-1
2. Data Visualization
Data Queries: Using Sorting and Filtering
Statistical Methods for Summarizing Data
Exploring Data Using PivotTables
Topics
3-2
3. Creating Charts in Microsoft Excel
Select the insert tab.
Highlight the data.
Click on chart type, then subtype.
Use chart tools to customize.
Data Visualization
3-3
Figure 3.1
Figure 3.2
4. Example 3.1 Creating a Column Chart
Data Visualization
3-4
Figure 3.3
Highlighted Cells
5. Example 3.1 (continued) Creating a Column Chart
Choose column chart (clustered or stacked).
Add chart title (Alabama Employment).
Rename Series1, Series2, and Series3
(ALL EMPLOYEES, Men, Women).
Data Visualization
3-5
Figure 3.4
6. Example 3.1 (continued) Creating a Column Chart
Data Visualization
3-6
Figure 3.5
Clustered
Column
Chart
7. Example 3.1 (continued) Creating a Column Chart
Data Visualization
3-7
Figure 3.6
Stacked
Column
Chart
8. Example 3.2 Line Chart for U.S. Exports to China
Data Visualization
3-8
Figure 3.7
9. Example 3.3 Pie Chart for Census Data
Data Visualization
3-9
Figure 3.8
Figure 3.9
10. Example 3.4 Area Chart for Energy Consumption
Data Visualization
3-10
Figure 3.10
11. Example 3.5 Scatter Chart for Real Estate Data
Data Visualization
3-11
Figure 3.11
12. Example 3.6
Bubble Chart for Comparing Stock Characteristics
Data Visualization
3-12
Figure 3.12
14. Example 3.7
Sorting Data in the Purchase Orders Database
3-14
Figure 3.13
Figure 3.14
Sort by Supplier
Data Queries: Using Sorting and Filtering
15. Pareto Analysis
An Italian economist, Vilfredo Pareto, observed in
1906 that a large proportion of the wealth in Italy
was owned by a small proportion of the people.
Similarly, businesses often find that a large
proportion of sales come from a small proportion
of customers.
A Pareto analysis involves sorting data and
calculating cumulative proportions.
3-15
Data Queries: Using Sorting and Filtering
16. Example 3.8 Applying the Pareto Principle
3-16
Data Queries: Using Sorting and Filtering
Figure 3.15
75% of the bicycle inventory value comes from 40% (9/24) of items.
Sort by
17. Example 3.9 Filtering Records by Item Description
Highlight A3:J97
Data tab
Sort & Filter group
Filter
Click on the D3
dropdown arrow.
Select Bolt-nut
package to filter out
all other items.
3-17
Figure 3.16
Data Queries: Using Sorting and Filtering
18. Example 3.9 (continued)
Filtering Records by Item Description
Filter results for the bolt-nut package
3-18
Figure 3.17
Data Queries: Using Sorting and Filtering
19. Example 3.10 Filtering Records by Item Cost
To identify items that
cost at least $200
Click on dropdown
arrow for item cost
Number Filters
Greater Than Or
Equal To…
3-19
Figure 3.18
Data Queries: Using Sorting and Filtering
20. Example 3.10 (continued) Filtering by Item Cost
Custom AutoFilter dialog box
Click OK
Only items
costing at least
$200 is then
displayed.
3-20
Figure 3.19
Data Queries: Using Sorting and Filtering
21. AutoFilter criteria is based on the data type.
Number Filters includes numerical criteria.
Date Filters include tomorrow, next week, etc.
AutoFilter can be used sequentially.
First filter by one variable.
Then filter those data by another variable.
3-21
Data Queries: Using Sorting and Filtering
22. Analytics in Practice: Discovering Value
of Data Analysis at Alders International
Duty free operations at airports, seaports, etc.
Maintain a data warehouse to track point-of-sale
information and inventory levels.
Pareto analysis revealed that 80% of profits were
generated from 20% of their product lines.
Allows selective elimination of less profitable items.
3-22
Data Queries:
Using Sorting and Filtering
23. A statistic is a summary measure of data.
Descriptive statistics are methods that describe
and summarize data.
Microsoft Excel supports statistical analysis in two
ways:
1. Statistical functions
2. Analysis Toolpak add-in for PCs
(for Macs, StatPlus is similar)
3-23
Statistical Methods for Summarizing Data
Statistical methods are essential to Business Analytics
24. Example 3.11 Constructing a Frequency Distribution
for Items in the Purchase Order Database
3-24
Statistical Methods for Summarizing Data
Figure 3.20
Copy Column D (Item Description) to Column A in a new worksheet
25. Example 3.11 (continued) Constructing a Frequency
Distribution for Items in the Purchase Order Database
3-25
Statistical Methods for Summarizing Data
Figure 3.22
Figure 3.21
26. Example 3.11 (continued) Constructing a Frequency
Distribution for Items in the Purchase Order Database
3-26
Statistical Methods for Summarizing Data
Figure 3.23
27. Example 3.12 Constructing a Relative Frequency
Distribution for Items Purchased
3-27
Statistical Methods for Summarizing Data
Figure 3.24
Compute relative
frequencies by
dividing each
frequency by 94.
28. Example 3.13 Frequency and Relative Frequency
Distribution for A/P Terms
3-28
Statistical Methods for Summarizing Data
Figure 3.26
Figure 3.25
29. Excel’s Histogram Tool
Using the Analysis Toolpak
Data
Data Analysis
Histogram
Fill in the Input Range and Bin Range (optional).
Choose Labels if columns have headers rows.
Choose Chart Output.
3-29
Statistical Methods for Summarizing Data
Figure 3.27
30. Example 3.14
Using the Histogram Tool for A/P Terms
A/P data in H3:H97
Bins below in H99:H103
Month
15
25
30
45
3-30
Statistical Methods for Summarizing Data
Figure 3.28
31. Example 3.14 (continued)
Using the Histogram Tool for A/P Terms
3-31
Statistical Methods for Summarizing Data
Figure 3.29
Table above
is not linked
to chart.
32. Example 3.15 Constructing a Frequency
Distribution and Histogram for Cost Per Order
3-32
Statistical Methods for Summarizing Data
5 groups with a
$26,000 group width
Figure 3.30
33. Example 3.15 (continued) Constructing a Frequency
Distribution and Histogram for Cost Per Order
3-33
Statistical Methods for Summarizing Data
Figure 3.31
10 groups with a
$13,000 group width
34. Example 3.16 Computing Cumulative Relative
Frequencies for the Cost Per Order Data
3-34
Statistical Methods for Summarizing Data
Ogive Figure 3.33
Figure 3.32
35. Example 3.17 Computing Percentiles
Compute the 90th
percentile for cost per order in the
Purchase Orders Data.
Rank of kth
percentile =
n = 94 observations
k = 90
Rank of 90th
percentile = 94(90)/100+0.5
= 85.1 (round to 85)
Value of the 85th
observation = $74,375
3-35
Statistical Methods for Summarizing Data
36. Example 3.18 Computing Percentiles in Excel
Compute the 90th
percentile for cost per order.
Excel function for the kth
percentile:
=PERCENTILE.INC(array, k)
=PERCENTILE.INC(G4:G97, 0.90)
= $73,737.50
Excel does not use the formula on previous slide.
3-36
Statistical Methods for Summarizing Data
37. Example 3.19 Excel’s Rank and Percentile Tool
Data Data Analysis
Rank and Percentile
90.3rd
percentile
= $74,375
(same result as
manually computing
the 90th
percentile)
3-37
Statistical Methods for Summarizing Data
Figure 3.34
38. Example 3.20 Computing Quartiles in Excel
Compute the Quartiles of the Cost per Order data
Excel function for quartiles:
=QUARTILE.INC(array, quart)
=QUARTILE.INC(G4:G97, 1) = $6,757.81
=QUARTILE.INC(G4:G97, 2) = $15,656.25
=QUARTILE.INC(G4:G97, 3) = $27,593.75
=QUARTILE.INC(G4:G97, 4) = $127,500.00
3-38
Statistical Methods for Summarizing Data
39. Example 3.21 Constructing a Cross-Tabulation
Sales Transactions database
Identify the number (and percentage) of books
and DVDs ordered by region.
3-39
Statistical Methods for Summarizing Data
Figure 3.35
40. Example 3.21 (continued) Constructing a Cross-
Tabulation
3-40
Statistical Methods for Summarizing Data
Table 3.1
Table 3.2
41. Example 3.21 (continued) Constructing a Cross-
Tabulation
Excel’s PivotTable (covered next) makes this easy.
3-41
Statistical Methods for Summarizing Data
Figure 3.36
Table 3.1
43. PivotTable Field List
Select the fields for:
Report Filter
Column Labels
Row Labels
Σ Values
Or, before choosing
PivotTable, you can select
a cell in the data and let
Excel prepare a default
PivotTable.
3-43
Exploring Data Using PivotTables
Figure 3.37
45. Example 3.22 (continued) Creating a PivotTable
Pivot Table Tools
Options
Active Field
Field Settings
Change summarization
method in Value Field
Settings dialog box
Select Count
3-45
Exploring Data Using PivotTables
Figure 3.39
46. Example 3.22 (continued) Creating a PivotTable
3-46
Exploring Data Using PivotTables
Figure 3.40
Table 3.1
PivotTable for Count
of Regional Sales
by Product
PivotTable results
match those shown
earlier in Table 3.1.
47. Drag Source into the
Row Labels box.
PivotTable for Sales
by Region, Product,
and Order Source
3-47
Exploring Data Using PivotTables
Figure 3.41
Example 3.22 (continued)
Creating a PivotTable
48. Example 3.23
Using the Pivot
Table Report Filter
Drag Payment into
Report Filter box.
PivotTable Filtered
by Payment Type.
3-48
Exploring Data Using PivotTables
Figure 3.42
49. Example 3.23 (continued)
Using the PivotTable Report Filter
Click on the drop-down arrow in row 1.
3-49
Exploring Data Using PivotTables
Figure 3.43
Choose Credit-Card.
Obtain this cross-tabulation
PivotTable for credit card
transactions.
50. Example 3.24 A PivotChart for Sales Data
Create a chart using the PivotTable for
Sales by Region, Product, and Order Source.
Insert
Column Chart
To display only Book
data, click on the
Product button and
deselect DVD.
3-50
Exploring Data Using PivotTables
Figure 3.44
51. Assignment I (20%)
(use Ms Excel)
- Search and get required data
- Do the exploratory analysis:
- statistical analysis
- visualization
52. 3-52
Key Terms
Area chart
Bar chart
Bubble chart
Column chart
Contingency table
Cross-tabulation
Cumulative relative
frequency
Cumulative relative
frequency distribution
Data profile (fractile)
Descriptive statistics
Doughnut chart
Frequency distribution
Histogram
kth
percentile
Line chart
Ogive
Pareto analysis
Pie chart
54. Recall that PLE produces lawnmowers and a
medium size diesel power lawn tractor.
Create charts of the satisfaction data, sales data,
delivery time data, and other variables of interest.
Compare shipping costs for existing and proposed
plant locations.
Examine customer attributes by region and write a
formal report summarizing your results.
Case Study
Performance Lawn Equipment (3)
3-54
56. Objectives
• Sort data and filter data
• Summarize an Excel table
• Insert subtotals into a range of data
• Outline buttons to show or hide details
• Create and modify a PivotTable and PivotChart
57. Planning a Structured Range of Data
• A collection of similar data can be
structured in a range of columns and
rows, representing fields and records,
respectively
• A structured range of data is
commonly referred to as a list or
table
58. Creating an Effective Structured Range
of Data
• Enter field names in top row of range
• Use short, descriptive field names
• Format field names to distinguish
header row from data
• Enter same kind of data for a field in
each record
• Separate data (including header row)
from other information in the
worksheet by at least one blank row
and one blank column
59. Planning a Structured Range of Data
• Freezing a row or column keeps
headings visible as you work with
data in a large worksheet
60. Save Time with Excel Table Features
• Format quickly using a table style
• Add new rows and columns that
automatically expand the range
• Add a Total row to calculate a
summary function (SUM, AVERAGE,
COUNT, MIN, MAX)
• Enter a formula in a cell that is
copied to all other cells in the column
• Create formulas that reference cells
in a table by using table and column
62. Sorting Data
• Sort data in ascending or
descending order
• Use the Sort A to Z button or the
Sort Z to A button to sort data
quickly with one sort field
63. Sorting Data
• Use sort dialog box to sort multiple
columns
• Primary and secondary sort fields
• Up to 64 sort fields possible
64. Sorting Using a Custom
List
• A custom list indicates sequence to order
data
– Four predefined custom sort lists
• Two days-of-the-week custom lists
• Two months-of-the-year custom lists
– Can also create a custom list to sort
records in a sequence you define
65. Filtering Data
• Filtering data temporarily hides any
records that do not meet specified
criteria
• After data is filtered, it can be sorted,
copied, formatted, charted, and
printed
66. Using the Total Row to Calculate
Summary Statistics
• You can calculate sum, average,
count, maximum, and minimum on all
columns in a table or on a filtered
table in a Total row
67. Creating Subtotals
• Subtotals can be created on columnar
data
– The data must be sorted for subtotals to
be created
– Column headers must also appear in the
data
• Subtotal command
– Offers many kinds of summary
information (counts, sums, averages,
minimums, maximums)
– Inserts a subtotal row into range for each
group of data; adds grand total row
below last row of data
68. Inserting Subtotals
• Sort data so that records with the same
value in a specified field are grouped
together before using Subtotal command
– It cannot be used in an Excel table
– First convert the Excel table to a range
• Click SubTotal on the Data ribbon
70. Using the Subtotal Outline View
• Control the level of detail with
buttons
–Level 3: Most detail
–Level 2: Subtotals and grand total,
but not individual records
–Level 1: Only the grand total
71. Pivot Tables
• Interactive table used to group and
summarize either a range of data or an
Excel table into a concise, tabular
format for easier reporting and analysis
• Dynamic organization; can be
“pivoted” to examine data from various
perspectives by rearranging its
structure
• Best used to analyze data that can be
summarized in multiple ways
• Pivot tables can be created from lists or
external data sources
72. Analyzing Data with PivotTables
• Provide ability to “pivot” the table
(rearrange, hide, and display
different category fields to provide
alternative views of the data)
73. Analyzing Data with PivotTables
• Summarize data into categories
using functions (COUNT, SUM,
AVERAGE, MAX, MIN)
• Values fields contain summary data
• Category fields group the values
74. Creating a PivotTable
• Use PivotTable dialog box to select
data to analyze and location of the
PivotTable report
75. Creating a PivotTable
• PivotTable Field List has two sections
– Upper field list section displays names of
each field; use check boxes to add fields
to PivotTable
– Lower layout section includes boxes for
four areas in which you can place fields
77. Creating a PivotTable
• Apply PivotTable styles by using a
preset style or modifying its
appearance
• Formatting PivotTable values fields
–Applying PivotTable styles does not
change the numeric formatting
78. Refreshing a PivotTable
• You cannot change data directly in
the PivotTable
• Instead, you must edit the Excel
table, and then refresh, or update,
the PivotTable to reflect the updated
data
79. Grouping PivotTable
Items
• Grouping items combines dates or
numeric items into larger groups so
that the PivotTable can include the
desired level of summarization
80. Filtering and Slicing a PivotTable
• Filters can be applied to a PivotTable
• PivotTable filters can be based on:
– Field values
– Row and column label groupings
• PivotTable filters can be removed
81. Filtering and Slicing a PivotTable
• Slicer—small window that contains a
button for each item in a field
• Slicer—helpful when filtering a
PivotTable based on multiple tables
• Slicers can be customized
83. Creating a Calculated
Field
• Custom calculation options:
– % of Grand Total
– % of Column Total
– % of Row Total
– % of Parent Row Total
– Running Total
– Rank Smallest to Largest
– Rank Largest to Smallest
84. Creating a PivotChart
• PivotChart—interactive graphical
representation of PivotTable data
• Changing the position of a field in the
PivotTable or the PivotChart changes
the corresponding object as well
• Create a PivotChart:
– Click in the PivotTable
– Click PivotChart in the Tools group on the
ANALYZE tab
88. 88
Functions
SUMIFS
Adds the cells in a
range that meet
multiple criteria
COUNTIFS
Applies criteria to
cells across multiple
ranges and counts
the number of times
all criteria are met
The key difference between these and Countif/Sumif is that these
allow the use of multiple criteria. Countif/Sumif do not
89. 89
DATA TABLES
A data table is a range of cells that shows how
changing one or two variables in your formulas
will affect the results of those formulas
To create a Data Table select
data and click Insert tab, Table
(in table group)
Convert a table to a range of data Click
anywhere in the table, click on Design tab then
click Convert to
Range in Tools group.
90. 90
DATA TABLES
Can be used to Calculate Options
In example sheet in cell J2 type =G3 then
select I2:J15
Click Data tab, What-if-analysis,
then Data Table
In Data Table, Column input
cell, click D4, and click OK
91. 91
Protecting Worksheets
Two step process, first unlock cells you
want user to change
Select cells you want unlocked
Home tab, Font group, click on Dialogue Box
expander, click on Protection tab, and remove
check mark from “Locked” choice
92. 92
PROTECT SHEETS
REVIEW tab > CHANGES group >
PROTECT
SHEET button
select the options you
want to be protected
> OK
94. 94
CONDITIONAL FORMATTING WITH A RULE cont.
Select a RULE TYPE:
Set your parameters:
Select the formatting you want by clicking on
the button at the bottom
95. 95
SORT BY MULTIPLE FIELDS
HOME tab > EDITING group > SORT
& FILTER Button > CUSTOM SORT
For each category you want
to sort by, click on the
ADD LEVEL button
96. 96
AUTOFILTER
Select a range of cells containing data.
HOME tab > EDITING group >
SORT & FILTER button > FILTER
Drop-down arrows will now
Appear beside each
Column heading
Select the drop-down arrow and:
De-select: SELECT ALL
Then select the checkbox beside
the option you wish to sort by
97. 97
SUBTOTALS
DATA tab >
Note that data should be sorted to get best results
You can automatically calculate subtotals and grand totals
in a list for a column by using the Subtotal command in the
Outline group on the Data tab.
98. 98
PIVOT TABLE
Are used to summarize, analyze, explore, and
present summary data
Select the range
INSERT > click on
PivotTable
My table has headers is selected > OK
99. 99
Modify A PivotTable So That A Column Displays The
MAXIMUM Value, Instead Of The SUM
Select the cell which has the desired
COLUMN HEADING
OPTIONS tab > ACTIVE FIELD group >
FIELD SETTINGS button
In the list, select the
Desired function > OK
102. 102
PIVOT CHART BASED ON A PIVOT TABLE
PIVOT TABLE TOOLS > OPTIONS > TOOLS group >
PivotChart button
in the PivotChart Filter Pane which pops up
when you create the PivotChart
Click on the drop-down arrow beside
the 1st
category name
De-select: SELECT ALL
Then select the categories you want to be
Able to view in your PivotChart > OK
103. 103
GOAL SEEK
Automatically vary the contents of one cell
so that the value of the contents of another cell
equals a certain amount
Click DATA tab > DATA TOOLS group >
"WHAT-IF ANALYSIS" icon >
GOAL SEEK
In the SET CELL textbox, key in the cell
you want the ANSWER to appear in
In the BY CHANGING CELL textbox,
key in the cell reference you want
changed in order to get the desired answer > OK