Business statistics (Introduction)

Apr 11, 2020Download as ppt, pdf0 likes34 views

This document introduces the concepts of data analysis. It defines key terms like individuals, variables, categorical vs. quantitative variables, and distribution. It explains that the goal of data analysis is to organize, display, summarize and ask questions about data in order to make inferences about populations based on samples. The next sections will cover analyzing categorical data with graphs and tables, as well as describing quantitative data with numerical summaries.

CHAPTER 1
EXPLORING DATA
 Introduction: Data Analysis: Making Sense of Data
 1.1 Analyzing Categorical Data
 1.2 Displaying Quantitative Data with Graphs
 1.3 Describing Quantitative Data with Numbers

INTRODUCTION
DATA ANALYSIS: MAKING SENSE OF
DATA
Learning Objectives
After this section, you should be able to…
DEFINE “Individuals” and “Variables”
DISTINGUISH between “Categorical” and “Quantitative”
variables
DEFINE “Distribution”
DESCRIBE the idea behind “Inference”

DATAANALYSIS
Statistics is the science of data.
 Data Analysis is the process of organizing,
displaying, summarizing, and asking questions
about data.
Definitions:
Individuals – objects (people, animals, things)
described by a set of data
Variable - any characteristic of an individual
Categorical Variable
– places an individual into
one of several groups or
categories.
Quantitative Variable
– takes numerical values for
which it makes sense to find
an average.

DATAANALYSIS
 A variable generally takes on many different
values. In data analysis, we are interested in
how often a variable takes on each value.
Definition:
Distribution – tells us what values a variable
takes and how often it takes those values
2009 Fuel EconomyGuide
MODEL MPG
1
2
3
4
5
6
7
8
9
Acura RL 22
Audi A6 Quattro 23
BentleyArnage 14
BMW5281 28
Buick Lacrosse 28
Cadillac CTS 25
Chevrolet Malibu 33
Chrysler Sebring 30
Dodge Avenger 30
2009 Fuel EconomyGuide
MODEL MPG <new>
9
10
11
12
13
14
15
16
17
Dodge Avenger 30
Hyundai Elantra 33
Jaguar XF 25
Kia Optima 32
Lexus GS 350 26
Lincolon MKZ 28
Mazda 6 29
Mercedes-BenzE350 24
MercuryMilan 29
2009 Fuel EconomyGuide
MODEL MPG <new>
16
17
18
19
20
21
22
23
24
Mercedes-BenzE350 24
MercuryMilan 29
Mitsubishi Galant 27
Nissan Maxima 26
Rolls Royce Phantom 18
Saturn Aura 33
Toyota Camry 31
Volkswagen Passat 29
Volvo S80 25
Variable of Interest:
MPG
Dotplot of MPG
Distribution
Example

2009 Fuel EconomyGuide
MODEL MPG <new>
9
10
11
12
13
14
15
16
17
Dodge Avenger 30
Hyundai Elantra 33
Jaguar XF 25
Kia Optima 32
Lexus GS 350 26
Lincolon MKZ 28
Mazda 6 29
Mercedes-BenzE350 24
MercuryMilan 29
2009 Fuel EconomyGuide
MODEL MPG <new>
16
17
18
19
20
21
22
23
24
Mercedes-BenzE350 24
MercuryMilan 29
Mitsubishi Galant 27
Nissan Maxima 26
Rolls Royce Phantom 18
Saturn Aura 33
Toyota Camry 31
Volkswagen Passat 29
Volvo S80 25
2009 Fuel EconomyGuide
MODEL MPG
1
2
3
4
5
6
7
8
9
Acura RL 22
Audi A6 Quattro 23
BentleyArnage 14
BMW5281 28
Buick Lacrosse 28
Cadillac CTS 25
Chevrolet Malibu 33
Chrysler Sebring 30
Dodge Avenger 30
Add numerical
summaries
DATAANALYSIS
Examine each variable
by itself.
Then study
relationships among
the variables.
Start with a graph or
graphs
How to Explore Data

DATAANALYSIS
From Data Analysis to Inference
Population
Sample
Collect data from a
representative Sample...
Perform Data
Analysis, keeping
probability in mind…
Make an Inference
about the Population.

ACTIVITY: HIRING
DISCRIMINATION Follow the directions on Page 5
 Perform 5 repetitions of your simulation.
 Turn in your results to your teacher.
 Teacher: Right-click (control-click) on the graph to edit the counts.
DataAnalysis

INTRODUCTION
DATA ANALYSIS: MAKING SENSE OF
DATA
Summary
In this section, we learned that…
A dataset contains information on individuals.
For each individual, data give values for one or more variables.
Variables can be categorical or quantitative.
The distribution of a variable describes what values it takes and
how often it takes them.
Inference is the process of making a conclusion about a
population based on a sample set of data.

LOOKING AHEAD…
We’ll learn how to analyze categorical data.
Bar Graphs
Pie Charts
Two-Way Tables
Conditional Distributions
We’ll also learn how to organize a statistical problem.
In the next Section…

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Process Mining at Rabobank - Organizational challengesProcess mining Evangelist

Frank van Geffen is a Business Analyst at the Rabobank in the Netherlands. The first time Frank encountered Process Mining was in 2002, when he graduated on a method called communication diagnosis. He stumbled upon the topic again in 2008 and was amazed by the possibilities. Frank shares his experiences after applying process mining in various projects at the bank. He thinks that process mining is most interesting for the Process manager / Process owner (accountable for all aspects of the complete end to end process), the Process Analyst (responsible for performing the process mining analysis), the Process Auditor (responsible for auditing processes), and the IT department (responsible for development/aquisition, delivery and maintanance of the process mining software).

hersh's midterm project.pdf music retail and distributionhershtara1

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Process Mining and Official Statistics - CBSProcess mining Evangelist

Johan Lammers from Statistics Netherlands has been a business analyst and statistical researcher for almost 30 years. In their business, processes have two faces: You can produce statistics about processes and processes are needed to produce statistics. As a government-funded office, the efficiency and the effectiveness of their processes is important to spend that public money well. Johan takes us on a journey of how official statistics are made. One way to study dynamics in statistics is to take snapshots of data over time. A special way is the panel survey, where a group of cases is followed over time. He shows how process mining could test certain hypotheses much faster compared to statistical tools like SPSS.

文凭证书美国SDSU文凭圣地亚哥州立大学学生证学历认证查询Taqyea

保密服务圣地亚哥州立大学英文毕业证书影本美国成绩单圣地亚哥州立大学文凭【q微1954292140】办理圣地亚哥州立大学学位证(SDSU毕业证书)毕业证书购买【q微1954292140】帮您解决在美国圣地亚哥州立大学未毕业难题（San Diego State University）文凭购买、毕业证购买、大学文凭购买、大学毕业证购买、买文凭、日韩文凭、英国大学文凭、美国大学文凭、澳洲大学文凭、加拿大大学文凭（q微1954292140）新加坡大学文凭、新西兰大学文凭、爱尔兰文凭、西班牙文凭、德国文凭、教育部认证，买毕业证，毕业证购买，买大学文凭，购买日韩毕业证、英国大学毕业证、美国大学毕业证、澳洲大学毕业证、加拿大大学毕业证（q微1954292140）新加坡大学毕业证、新西兰大学毕业证、爱尔兰毕业证、西班牙毕业证、德国毕业证，回国证明，留信网认证，留信认证办理，学历认证。从而完成就业。圣地亚哥州立大学毕业证办理，圣地亚哥州立大学文凭办理，圣地亚哥州立大学成绩单办理和真实留信认证、留服认证、圣地亚哥州立大学学历认证。学院文凭定制，圣地亚哥州立大学原版文凭补办，扫描件文凭定做，100%文凭复刻。特殊原因导致无法毕业，也可以联系我们帮您办理相关材料：１：在圣地亚哥州立大学挂科了，不想读了，成绩不理想怎么办？？？ 2：打算回国了，找工作的时候，需要提供认证《SDSU成绩单购买办理圣地亚哥州立大学毕业证书范本》【Q/WeChat：1954292140】Buy San Diego State University Diploma《正式成绩单论文没过》有文凭却得不到认证。又该怎么办？？？美国毕业证购买，美国文凭购买，【q微1954292140】美国文凭购买，美国文凭定制，美国文凭补办。专业在线定制美国大学文凭，定做美国本科文凭，【q微1954292140】复制美国San Diego State University completion letter。在线快速补办美国本科毕业证、硕士文凭证书，购买美国学位证、圣地亚哥州立大学Offer，美国大学文凭在线购买。美国文凭圣地亚哥州立大学成绩单，SDSU毕业证【q微1954292140】办理美国圣地亚哥州立大学毕业证(SDSU毕业证书)【q微1954292140】录取通知书offer在线制作圣地亚哥州立大学offer/学位证毕业证书样本、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作。帮你解决圣地亚哥州立大学学历学位认证难题。主营项目： 1、真实教育部国外学历学位认证《美国毕业文凭证书快速办理圣地亚哥州立大学办留服认证》【q微1954292140】《论文没过圣地亚哥州立大学正式成绩单》，教育部存档，教育部留服网站100%可查. 2、办理SDSU毕业证，改成绩单《SDSU毕业证明办理圣地亚哥州立大学成绩单购买》【Q/WeChat：1954292140】Buy San Diego State University Certificates《正式成绩单论文没过》，圣地亚哥州立大学Offer、在读证明、学生卡、信封、证明信等全套材料，从防伪到印刷，从水印到钢印烫金，高精仿度跟学校原版100%相同. 3、真实使馆认证（即留学人员回国证明），使馆存档可通过大使馆查询确认. 4、留信网认证，国家专业人才认证中心颁发入库证书，留信网存档可查. 《圣地亚哥州立大学学位证书的英文美国毕业证书办理SDSU办理学历认证书》【q微1954292140】学位证1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。高仿真还原美国文凭证书和外壳，定制美国圣地亚哥州立大学成绩单和信封。毕业证网上可查学历信息SDSU毕业证【q微1954292140】办理美国圣地亚哥州立大学毕业证(SDSU毕业证书)【q微1954292140】学历认证生成授权声明圣地亚哥州立大学offer/学位证文凭购买、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作。帮你解决圣地亚哥州立大学学历学位认证难题。圣地亚哥州立大学offer/学位证、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作【q微1954292140】Buy San Diego State University Diploma购买美国毕业证，购买英国毕业证，购买澳洲毕业证，购买加拿大毕业证，以及德国毕业证，购买法国毕业证（q微1954292140）购买荷兰毕业证、购买瑞士毕业证、购买日本毕业证、购买韩国毕业证、购买新西兰毕业证、购买新加坡毕业证、购买西班牙毕业证、购买马来西亚毕业证等。包括了本科毕业证，硕士毕业证。

Decision Trees in Artificial-Intelligence.pdfSaikat Basu

Customer Segmentation using K-Means clusteringIngrid Nyakerario

This project demonstrates the application of machine learning—specifically K-Means Clustering—to segment customers based on behavioral and demographic data. The objective is to identify distinct customer groups to enable targeted marketing strategies and personalized customer engagement. The presentation walks through: Data preprocessing and exploratory data analysis (EDA) Feature scaling and dimensionality reduction K-Means clustering and silhouette analysis Insights and business recommendations from each customer segment This work showcases practical data science skills applied to a real-world business problem, using Python and visualization tools to generate actionable insights for decision-makers.

AWS Certified Machine Learning Slides.pdfphilsparkshome

Adopting Process Mining at the Rabobank - use caseProcess mining Evangelist

Frank van Geffen is a Process Innovator at the Rabobank. He realized that it took a lot of different disciplines and skills working together to achieve what they have achieved. It's not only about knowing what process mining is and how to operate the process mining tool. Instead, a lot of emphasis needs to be placed on the management of stakeholders and on presenting insights in a meaningful way for them. The results speak for themselves: In their IT service desk improvement project, they could already save 50,000 steps by reducing rework and preventing incidents from being raised. In another project, business expense claim turnaround time has been reduced from 11 days to 1.2 days. They could also analyze their cross-channel mortgage customer journey process.

50_questions_full.pptxddddddddddddddddddemir73065

Process Mining at Dimension Data - Jan vermeulenProcess mining Evangelist

Dimension Data has over 30,000 employees in nine operating regions spread over all continents. They provide services from infrastructure sales to IT outsourcing for multinationals. As the Global Process Owner at Dimension Data, Jan Vermeulen is responsible for the standardization of the global IT services processes. Jan shares his journey of establishing process mining as a methodology to improve process performance and compliance, to grow their business, and to increase the value in their operations. These three pillars form the foundation of Dimension Data's business case for process mining. Jan shows examples from each of the three pillars and shares what he learned on the way. The growth pillar is particularly new and interesting, because Dimension Data was able to compete in a RfP process for a new customer by providing a customized offer after analyzing the customer's data with process mining.

How to regulate and control your it-outsourcing provider with process miningProcess mining Evangelist

Oliver Wildenstein is an IT process manager at MLP. As in many other IT departments, he works together with external companies who perform supporting IT processes for his organization. With process mining he found a way to monitor these outsourcing providers. Rather than having to believe the self-reports from the provider, process mining gives him a controlling mechanism for the outsourced process. Because such analyses are usually not foreseen in the initial outsourcing contract, companies often have to pay extra to get access to the data for their own process.

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Microsoft Excel: A Comprehensive OverviewGinaTomarongRegencia

定制学历(美国Purdue毕业证)普渡大学电子版毕业证Taqyea

Process Mining at Rabobank - Organizational challengesProcess mining Evangelist

hersh's midterm project.pdf music retail and distributionhershtara1

Modern_Distribution_Presentation.pptx AaMuhammadAwaisKamboh

Just-In-Timeasdfffffffghhhhhhhhhhj Systems.pptssuser5f8f49

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problem solving.presentation slideshow bsc nursingvishnudathas123

chapter3 Central Tendency statistics.pptjustinebandajbn

Process Mining and Official Statistics - CBSProcess mining Evangelist

文凭证书美国SDSU文凭圣地亚哥州立大学学生证学历认证查询Taqyea

Decision Trees in Artificial-Intelligence.pdfSaikat Basu

Customer Segmentation using K-Means clusteringIngrid Nyakerario

AWS Certified Machine Learning Slides.pdfphilsparkshome

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50_questions_full.pptxddddddddddddddddddemir73065

Process Mining at Dimension Data - Jan vermeulenProcess mining Evangelist

How to regulate and control your it-outsourcing provider with process miningProcess mining Evangelist

Business statistics (Introduction)

2. CHAPTER 1 EXPLORING DATA  Introduction: Data Analysis: Making Sense of Data  1.1 Analyzing Categorical Data  1.2 Displaying Quantitative Data with Graphs  1.3 Describing Quantitative Data with Numbers

3. INTRODUCTION DATA ANALYSIS: MAKING SENSE OF DATA Learning Objectives After this section, you should be able to… DEFINE “Individuals” and “Variables” DISTINGUISH between “Categorical” and “Quantitative” variables DEFINE “Distribution” DESCRIBE the idea behind “Inference”

4. DATAANALYSIS Statistics is the science of data.  Data Analysis is the process of organizing, displaying, summarizing, and asking questions about data. Definitions: Individuals – objects (people, animals, things) described by a set of data Variable - any characteristic of an individual Categorical Variable – places an individual into one of several groups or categories. Quantitative Variable – takes numerical values for which it makes sense to find an average.

5. DATAANALYSIS  A variable generally takes on many different values. In data analysis, we are interested in how often a variable takes on each value. Definition: Distribution – tells us what values a variable takes and how often it takes those values 2009 Fuel EconomyGuide MODEL MPG 1 2 3 4 5 6 7 8 9 Acura RL 22 Audi A6 Quattro 23 BentleyArnage 14 BMW5281 28 Buick Lacrosse 28 Cadillac CTS 25 Chevrolet Malibu 33 Chrysler Sebring 30 Dodge Avenger 30 2009 Fuel EconomyGuide MODEL MPG <new> 9 10 11 12 13 14 15 16 17 Dodge Avenger 30 Hyundai Elantra 33 Jaguar XF 25 Kia Optima 32 Lexus GS 350 26 Lincolon MKZ 28 Mazda 6 29 Mercedes-BenzE350 24 MercuryMilan 29 2009 Fuel EconomyGuide MODEL MPG <new> 16 17 18 19 20 21 22 23 24 Mercedes-BenzE350 24 MercuryMilan 29 Mitsubishi Galant 27 Nissan Maxima 26 Rolls Royce Phantom 18 Saturn Aura 33 Toyota Camry 31 Volkswagen Passat 29 Volvo S80 25 Variable of Interest: MPG Dotplot of MPG Distribution Example

6. 2009 Fuel EconomyGuide MODEL MPG <new> 9 10 11 12 13 14 15 16 17 Dodge Avenger 30 Hyundai Elantra 33 Jaguar XF 25 Kia Optima 32 Lexus GS 350 26 Lincolon MKZ 28 Mazda 6 29 Mercedes-BenzE350 24 MercuryMilan 29 2009 Fuel EconomyGuide MODEL MPG <new> 16 17 18 19 20 21 22 23 24 Mercedes-BenzE350 24 MercuryMilan 29 Mitsubishi Galant 27 Nissan Maxima 26 Rolls Royce Phantom 18 Saturn Aura 33 Toyota Camry 31 Volkswagen Passat 29 Volvo S80 25 2009 Fuel EconomyGuide MODEL MPG 1 2 3 4 5 6 7 8 9 Acura RL 22 Audi A6 Quattro 23 BentleyArnage 14 BMW5281 28 Buick Lacrosse 28 Cadillac CTS 25 Chevrolet Malibu 33 Chrysler Sebring 30 Dodge Avenger 30 Add numerical summaries DATAANALYSIS Examine each variable by itself. Then study relationships among the variables. Start with a graph or graphs How to Explore Data

7. DATAANALYSIS From Data Analysis to Inference Population Sample Collect data from a representative Sample... Perform Data Analysis, keeping probability in mind… Make an Inference about the Population.

8. ACTIVITY: HIRING DISCRIMINATION Follow the directions on Page 5  Perform 5 repetitions of your simulation.  Turn in your results to your teacher.  Teacher: Right-click (control-click) on the graph to edit the counts. DataAnalysis

9. INTRODUCTION DATA ANALYSIS: MAKING SENSE OF DATA Summary In this section, we learned that… A dataset contains information on individuals. For each individual, data give values for one or more variables. Variables can be categorical or quantitative. The distribution of a variable describes what values it takes and how often it takes them. Inference is the process of making a conclusion about a population based on a sample set of data.

10. LOOKING AHEAD… We’ll learn how to analyze categorical data. Bar Graphs Pie Charts Two-Way Tables Conditional Distributions We’ll also learn how to organize a statistical problem. In the next Section…

Business statistics (Introduction)

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