商务统计学最新英文版教学课件绪论

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商务统计学英文课件 (9)

商务统计学英文课件 (9)

e = the mathematical constant approximated by 2.71828 π = the mathematical constant approximated by 3.14159 Z = any value of the standardized normal distribution
n To use the normal probability plot to determine whether a set of data is approximately normally distributed
Continuous Probability Distributions
n A continuous random variable is a variable that can assume any value on a continuum (can assume an uncountable number of values)
X 8.0
8.6
Finding Normal Probabilities
(continued)
n Let X represent the time it takes to download an image file from the internet.
n Suppose X is normal with mean 8.0 and standard deviation 5.0. Find P(X < 8.6)
The random variable has an infinite theoretical range: + to
σ X
μ
Mean = Median = Mode

商务统计学最新英文版教学课件第4章

商务统计学最新英文版教学课件第4章

Venn Diagram For All Days In 2015
Sample Space (All Days In 2015)
Days That Are In January and Are Wednesdays
January Days
Wednesdays
Organizing & Visualizing Events
A = Weekday; B = Weekend; C = January; D = Spring;
Events A, B, C and D are collectively exhaustive (but not mutually exclusive – a weekday can be in January or in Spring)
total number of days in 2015
365
Jan.
Wed.
4
Not Wed. 27
Total
31
Not Jan. 48 286 334
Total 52 313 365
Marginal Probability Example
P(Wed.)
P(Jan.and Wed.) P(Not Jan.and Wed.) 4 48 52 365 365 365
(continued)
Contingency Tables -- For All Days in 2015
Jan. Not Jan. Total
Wed.
4
48
52
Not Wed. 27
286
313
Total
Decision Trees
Sample Space

商务统计学课件英文版BSFC7e-CH01

商务统计学课件英文版BSFC7e-CH01

Chapter 1, Slide 5
Collecting Data Correctly Is A Critical
Task
DCOVA
▪ Need to avoid data flawed by biases, ambiguities, or other types of errors.
▪ Results from flawed data will be suspect or in error.
Chapter 1, Slide 2
Classifying Variables By Type
DCOVA
▪ Categorical (qualitative) variables take categories as their values such as “yes”, “no”, or “blue”, “brown”, “green”.
Copyright © 2016, 2013, 2010 Pearson Education, Inc.
Chapter 1, Slide 7
Establishing A Business Objective
Focuses Data Collection
Examples Of Business Objectives:
People being surveyed to determine their satisfaction with a recent product or service experience.
Copyright © 2016, 2013, 2010 Pearson Education, Inc.
Chapter 1, Slide 12
Examples of Survey Data

Ch19 Decision Theory 商务统计学概论(英文第四版)教学课件 Introduction to Business Statistics

Ch19 Decision Theory 商务统计学概论(英文第四版)教学课件 Introduction to Business Statistics

© 2002 The Wadsworth Group
The Decision Tree
Decision Alternatives State of Nature Payoff
p1 State 1 Occurs
v11
Select Alternative 1
p2 State 2 Occurs
v12
p3 State 3 Occurs
Non-Bayesian Decision Theory: An Example
• Maximin Strategy:
– Decide to lease the snow-making machine because the minimum payoff for that alternative is $30,000, which beats the minimum payoff of $20,000 for the alternative to not lease the snow-making machine.
© 2002 The Wadsworth Group
The Decision Situation: An Example
• The decision alternatives are:
– The operator does not lease the snow-making machine.
– The operator does lease the snow-making machine.
© 2002 The Wadsworth Group
Bayesian Decision Theory: Strategies With Probabilities
• Expected Payoff (or Expected Monetary Value) Criterion: Select the alternative where the expected value for the payoff is the best.

商务统计学最新英文版教学课件第6章

商务统计学最新英文版教学课件第6章

Z X μ 18.6 18.0 0.12
σ
5.0
μ = 18 σ= 5
μ=0 σ= 1
18 18.6
X
P(X < 18.6)
0 0.12
Z
P(Z < 0.12)
Solution: Finding P(Z < 0.12)
Standardized Normal Probability Table (Portion)
The standardized normal distribution (Z) has a mean of 0 and a standard deviation of 1
Translation to the Standardized Normal Distribution
Translate from X to the standardized normal (the “Z” distribution) by subtracting the mean of X and dividing by its standard deviation:
f(Z) 1 e(1/2)Z2 2π
Where
e = the mathematical constant approximated by 2.71828 π = the mathematical constant approximated by 3.14159 Z = any value of the standardized normal distribution
Any normal distribution (with any mean and standard deviation combination) can be transformed into the standardized normal distribution (Z)

商务统计学最新英文版教学课件第2章

商务统计学最新英文版教学课件第2章
Chapter 2
Organizing and Visualizing Variables
Objectives
In this chapter you learn: Methods to organize variables. Methods to visualize variables. Methods to organize or visualize more than
Large Amount
Total
No Errors 89.47%
71.43%
92.86%
83.75%
Errors 10.53% 28.57%
7.14% 16.25%
Total 100.0% 100.0% 100.0% 100.0%
Contingency Table Based On
Percentage Of Column Totals
Small Amount
Medium Amount
Large Amount
Total
No Errors 50.75%
29.85%
19.40%
100.0%
Errors 30.77% 61.54%
7.69% 100.0%
Total 47.50% 35.00% 17.50% 100.0%
Tables Used For Organizing Numerical Data
Organizing Numerical Data:
Frequency Distribution Example
DCOVA
▪ Sort raw data in ascending order:
12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58

商务统计学最新英文版教学课件第12章

商务统计学最新英文版教学课件第12章

▪ Explain the impact of changes in an independent variable on the dependent variable
▪ Dependent variable: the variable we wish to predict or explain
▪ Independent variable: the variable used to predict or explain the dependent variable
b1 is the estimated change in the mean value of Y as a result of a one-unit
increase in X
Simple Linear Regression Example
DCOVA
A real estate agent wishes to examine the relationship between the selling price of a home and its size (measured in square feet)
Simple Linear Regression Model
DCOVA
Dependent Variable
Population Y intercept
Population Slope Coefficient
Independent Variable
Yi β0 β1Xi εi
Random Error term
House Price in $1000s (Y) 245 312 279 308 199 219 405 324 319 255
Square Feet (X) 1400 1600 1700 1875 1100 1550 2350 2450 1425 1700

商务统计学ppt sbfc1e_alq_chapter07

商务统计学ppt sbfc1e_alq_chapter07

Game Commercials Won't Watch
Total
Male 279 81 132
492
Female 200 156 160
516
Total 479 237 292
1008
What is the marginal probability that a viewer was female?
A. .159
B. .310
C. .643
D. .512
Game Commercials Won't Watch
Total
Male 279 81 132
492
Female 200 156 160
516
Total 479 237 292
1008
What is the marginal probability that a viewer was female?
A. True
B. False
Copyright © 2011 Pearson Education, Inc.
Slide 7- 6
For independent trials, the Law of Averages states that as the number of trials increases, the long run relative frequency of repeated events gets closer and closer to a single value.
A. less than 50%, since “tails” are due to come up. B. 50%. C. greater than 50%, since it appears that we are in a streak of “heads.” D. not able to be determined.

商务统计学最新英文版教学课件第8章

商务统计学最新英文版教学课件第8章

An interval estimate provides more information about a population characteristic than does a point estimate
Such interval estimates are called confidence intervals
DCOVA
Suppose confidence level = 95%
Also written (1 - ) = 0.95, (so = 0.05)
A relative frequency interpretation:
95% of all the confidence intervals that can be constructed will contain the unknown true parameter
A specific interval either will contain or will not contain the true parameter
No probability involved in a specific interval
Confidence Intervals
Confidence Intervals
Mean, μ
when Population Standard Deviation σ is Known when Population Standard Deviation σ is Unknown
Confidence Intervals for the Population Proportion, π
formed in this manner will contain µ Thus, based on the one sample, you actually

商务统计学英文课件 (10)

商务统计学英文课件 (10)

Region of Non-Rejection
Critical Values
Region of Rejection
“Too Far Away” From Mean of Sampling Distribution
Possible Errors in Hypothesis Test Decision Making
Example: The average number of TV sets in U.S.
Homes is equal to three (
H0 : μ) 3
n Is always about a population parameter,
not about a sample statistic
small, so you reject the null hypothesis .
n In other words, getting a sample mean of 20 is so unlikely if the population mean was 50, you conclude that the population mean must not be 50.
Business Statistics: A First Course
5th Edition
Fundamentals of Hypothesis Testing: One-Sample Tests
Learning Objectives
In this chapter, you learn:
n The basic principles of hypothesis testing n How to use hypothesis testing to test a mean or

商务统计学课件第1章(ISEC)

商务统计学课件第1章(ISEC)


Business Statistics, A First Course (4e) © 2006 Prentice-Hall, Inc.
Hale Waihona Puke Chap 1-4附注在商业世界中,统计学有4种重要的应用。
· 总结商业数据 · 根据数据得出结论 · 作出商业行动的可靠预测 · 改进运营过程
Business Statistics, A First Course (4e) © 2006 Prentice-Hall, Inc.
Chap 1-10
为什么要收集数据?


市场调研者需要了解一个新的旅游产品的效果; 消费者的消费行为方面的信息数据,如:消费者购买产 品的花费、选择的产品渠道、偏好产品的类型、产品使 用周期、购买产品的目的、消费者家庭背景、工作和生 活环境、个人消费观和价值观等。如果企业收集到了这 些数据,建立消费者大数据库,便可通过统计和分析来 掌握消费者的消费行为、兴趣偏好和产品的市场口碑现 状,再根据这些总结出来的行为、兴趣爱好和产品口碑 现状制定有针对性的营销方案和营销战略,投消费者所 好,那么其带来的营销效应是可想而知的。

Business Statistics, A First Course (4e) © 2006 Prentice-Hall, Inc.
Chap 1-16
Types of Data(数据类型)
Data
(属性) Categorical
Examples:

Numerical (数值)
gender belief (Defined categories) 定义属性类别
Probability Theory & Mathematical Statistics 概率论与数理统计

商务统计学最新英文版教学课件第5章

商务统计学最新英文版教学课件第5章
Network (xi) 0 1 2 3 4 5
Probability P(X = xi) 0.35 0.25 0.20 0.10 0.05 0.05
[xi – E(X)]2
[xi – E(X)]2P(X = xi)
(0 – 1.4)2 = 1.96 (1.96)(0.35) = 0.686
(1 – 1.4)2 = 0.16 (0.16)(0.25) = 0.040
Standard Deviation of a discrete variable
N
σ σ2 [xi E(X)]2 P(X xi ) i1
where:
E(X) = Expected value of the discrete variable X
xi
= the ith outcome of X
New job applicants either accept the offer or reject it
The Binomial Distribution Counting Techniques
Suppose the event of interest is obtaining heads on the toss of a fair coin. You are to toss the coin three times. In how many ways can you get two heads?
Each observation is categorized as to whether or not the “event of interest” occurred
e.g., head or tail in each toss of a coin; defective or not defective light bulb

商务统计学英文课件 (5)

商务统计学英文课件 (5)

Two variable model Y
(continued)
Yˆ b0 b1X1 b2X2
Slope for variable X1
X2
Slope for variable X2
X1
Example: 2 Independent Variables
n A distributor of frozen dessert pies wants to evaluate factors thought to influence demand
n Dependent variable: Pie sales (units per week) n Independent variables: Price (in $)
Advertising ($100’s)
n Data are collected for 15 weeks
Pie Sales Example
Total
14 56493
The Multiple Regression Equation
Sales 306.526 - 24.975(Price) 74.131(Advertising)
where Sales is in number of pies per week Price is in $ Advertising is in $100’s.
In this chapter we will use Excel or Minitab to obtain the regression slope coefficients and other regression summary measures.
Multiple Regression Equation
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Statistics Is Evolving So Businesses Can Use The Vast Amount Of Data Available
The emerging field of Business Analytics makes “extensive use of:
Data Statistical and quantitative analysis Explanatory & predictive models Fact based management
Attributes that distinguish “Big Data” from well structured large data sets are “volume” of data, “velocity” of the data collection, and “variety” of the data.
DCOVA
DATA The set of individual values associated with a variable.
STATISTICS
The methods that help transform data into useful information for decision makers.
students. How to prepare for using Microsoft Excel® or Minitab with
this book.
In Today’s Business World You Cannot Escape From Data
In today’s digital world ever increasing amounts of data are gathered, stored, reported on, and available for further study.
decisions. How applying the DCOVA framework for applying
statistics can help solve business problems. The significance of business analytics. The opportunity business analytics represent for business
A survey reported women were more likely than men to cite seeing photos or videos, sharing with man people at one, seeing entertaining or funny posts, learning about ways to help others, and receiving support from people in your network as reasons to use Facebook.
to drive decisions and actions.”
To Properly Apply Statistics You Should Follow A Framework To Minimize Possible Errors
In this book we will use DCOVA
Define the data you want to study in order to solve a problem or meet an objective
conclusions and present results
Using The DCOVA Framework Helps You To Apply Statistics To:
Summarize & visualize business data
Reach conclusions from those data
Use information systems’ methods to collect and process data sets of all sizes, including very large data sets that would otherwise be hard to examine efficiently.
Financial analysts determining why certain trends occur to predict future financial environments.
Marketers driving loyalty programs and customer marketing decisions to drive sales.
Business Analytics Has Already Been Applied In Many Business Decision-Making Contexts
Human resource managers (HR) understanding relationships between HR drivers, key business outcomes, employee skills, capabilities, and motivation.
Make reliable predictions about business activities
Improve business processes
Definition Of Some Terms
VARIABLE A characteristic of an item or individual.
Business Analytics: The Changing Face Of Statistics
Use statistical methods to analyze and explore data to uncover unforeseen relationships.
Use management science methods to develop optimization models that impact an organization’s strategy, planning, and operations.
Collect the data from appropriate sources Organize the data collected by developing
tables Visualize the data by developing charts Analyze the data collected to reach
Chapter GS
Getting Started
Objectives
In this chapter you learn: That the preponderance of data makes learning about
statistics critically important. Statistics is a way of thinking that can lead to better
Statistics: An Important Part of Your Business Education
You need analytical skills for the increasingly data-driven environment of business.
Studies show an increase in productivity, innovation, and competitiveness for organizations that embrace business analytics.
You hear the word data everywhere.
Data are facts about the world and are constantly reported as numbers by an ever increasing number of sources.
Each Business Person Faces A Choice Of How To Deal With This Explosion Of Data
To quote Hal Varian, the chief economist at Google Inc., “the sexy job in the next 10 years will be statisticians. And I’m not kidding.”
How To Use This Book
Supply chain managers planning and forecasting based on product distribution and optimizing sales distribution based on key inventory measures.
The Growth Of “Big Data” Spurs The Use Of Business Analytics
Without Statistics You Can’t
Determine if the numbers in these studies are useful information
Validate claims of predictability or causality
See patterns that large amounts of data sometimes reveal
“Big Data” is still a fuzzy concept.
Very large data sets are arising because of the automatic collection of high volumes of data at very fast rates.
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