Bstat 1

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Chapter 1 - Data and Statistics
Statistics
Applications in Business and Economics
Data
Data Sources
Descriptive Statistics
Statistical Inference
Analytics
Big Data and Data Mining
Computers and Statistical Analysis
Ethical Guidelines for Statistical Practice
What is Statistics?
The term statistics can refer to numerical facts such as averages, medians, percentages, and maximums that help us understand a variety of business and economic situations.
Statistics can also refer to the art and science of collecting, analyzing, presenting, and interpreting data.
Applications in Business and Economics
Accounting
Public accounting firms use statistical sampling procedures when conducting audits for their clients.

Economics
Economists use statistical information in making forecasts about the future of the economy or some aspect of it.

Finance
Financial advisors use price-earnings ratios and dividend yields to guide their investment advice.
Applications in Business and Economics
Marketing
Electronic point-of-sale scanners at retail checkout counters are used to collect data for a variety of marketing research applications.

Production
A variety of statistical quality control charts are used to monitor the output of a production process.

Information Systems
A variety of statistical information helps administrators assess the performance of computer networks.
Data and Data Sets
Data are the facts and figures collected, analyzed, and summarized for presentation and interpretation.
All the data collected in a particular study are referred to as the data set for the study.

Elements, Variables, and Observations
Elements are the entities on which data are collected.
A variable is a characteristic of interest for the elements.
The set of measurements obtained for a particular element is called an observation.
A data set with n elements contains n observations.
The total number of data values in a complete data set is the number of elements multiplied by the number of variables.
Data, Data Sets, Elements, Variables, and Observations
Statistics for 
Business and Economics (14e)
and Essentials of Statistics for Business and Economics (9e)
Anderson, Sweeney, Williams, Camm, Cochran, Fry, Ohlmann
© 2020 Cengage Learning

© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.

Chapter 1 - Data and Statistics
1.1 - Applications in Business and Economics
1.2 - Data
1.3 - Data Sources
1.4 - Descriptive Statistics
1.5 - Statistical Inference
1.6 - Analytics
1.7 - Big Data and Data Mining
1.8 - Computers and Statistical Analysis
1.9 - Ethical Guidelines for Statistical Practice
What Is Statistics?
The term statistics can refer to numerical facts such as averages, medians, percentages, and maximums that help us understand a variety of business and economic situations.
Statistics can also refer to the art and science of collecting, analyzing, presenting, and interpreting data.
Applications in Business and Economics (1 of 2)
Accounting
Public accounting firms use statistical sampling procedures when conducting audits for their clients.
Economics
Economists use statistical information in making forecasts about the future of the economy or some aspect of it.
Finance
Financial advisors use price-earnings ratios and dividend yields to guide their investment advice.
Applications in Business and Economics (2 of 2)
Marketing
Electronic point-of-sale scanners at retail checkout counters are used to collect data for a variety of marketing research applications.
Production
A variety of statistical quality control charts are used to monitor the output of a production process.
Information Systems
A variety of statistical information helps administrators assess the performance of computer networks.
Data and Data Sets
Data are the facts and figures collected, analyzed, and summarized for presentation and interpretation.
All the data collected in a particular study are referred to as the data set for the study.
Elements, Variables, and Observations
Elements are the entities on which data are collected.
A variable is a characteristic of interest for the elements.
The set of measurements obtained for a particular element is called an observation.
A data set with n elements contains n observations.
The total number of data values in a complete data set is the number of elements multiplied by the number of variables.
Data, Data Sets, Elements, Variables, and Observations
How would you describe yourself?
Vacationer
Volunteer – Love 
 Stats
Volunteer – Always 
 Curious
Victim – Scared
Victim - Bored
If there are 480 students in the first year class at GH and 65 in this class, we can multiply the numbers in the first slide by 375/65 (7.385)
to determine how many 
students in the first year 
class (overall) fall into 
these groups.
True
False
If there are 480 students in the first year class at GH and 65 in this class, we can say that because the value of Victim/Scared is 4 and the value for Volunteer/ Love Stats is 2, the people who answer Victim/Scared are twice as important as the ones who say Volunteer/Love Stats.
True
False
Scales of Measurement (1 of 6)
Scales of measurement include
Nominal
Ordinal
Interval
Ratio
The scale determines the amount of information contained in the data.
The scale indicates the data summarization and statistical analyses that are most appropriate.
Scales of Measurement (2 of 6)
Nominal scale
Data are labels or names used to identify an attribute of the element.
A nonnumeric label or numeric code may be used.
Example
Students of a university are classified by the school in which they are enrolled using a nonnumeric label such as Business, Humanities, Education, and so on.
Alternatively, a numeric code could be used for the school variable (e.g. 1 denotes Business, 2 denotes Humanities, 3 denotes Education, and so on).
Scales of Measurement (3 of 6)
Ordinal scale
The data have the properties of nominal data and the order or rank of the data is meaningful.
A nonnumeric label or numeric code may be used.
Example
Students of a university are classified by their class standing using a nonnumeric label such as Freshman, Sophomore, Junior, or Senior.
Alternatively, a numeric code could be used for the class standing variable (e.g. 1 denotes Freshman, 2 denotes Sophomore, and so on).
Scales of Measurement (4 of 6)
Interval scale
The data have the properties of ordinal data, and the interval between observations is expressed in terms of a fixed unit of measure.
Interval data are always numeric.
Example
Melissa has an SAT score of 1985, while Kevin has an SAT score of 1880. Melissa scored 105 points more than Kevin.
Scales of Measurement (5 of 6)
Ratio scale
Data have all the properties of interval data and the ratio of two values is meaningful.
Ratio data are always numerical.
Zero value is included in the scale.
Example:
Price of a book at a retail store is $ 200, while the price of the same book sold online is $100. The ratio property shows that retail stores charge twice the online price.
Categorical and Quantitative Data
Data can be further classified as being categorical or quantitative.
The statistical analysis that is appropriate depends on whether the data for the variable are categorical or quantitative.
In general, there are more alternatives for statistical analysis when the data are quantitative.
Categorical Data
Labels or names are used to identify an attribute of each element
Often referred to as qualitative data
Use either the nominal or ordinal scale of measurement
Can be either numeric or nonnumeric
Appropriate statistical analyses are rather limited
Quantitative Data
Quantitative data indicate how many or how much.
Quantitative data are always numeric.
Ordinary arithmetic operations are meaningful for quantitative data.
Scales of Measurement (6 of 6)
Cross-Sectional Data
Cross-sectional data are collected at the same or approximately the same point in time.
Example
Data detailing the number of building permits issued in November 2013 in each of the counties of Ohio.
Time Series Data (1 of 2)
Time series data are collected over several time periods.
Example
Data detailing the number of building permits issued in Lucas County, Ohio in each of the last 36 months.
Graphs of time series data help analysts understand
what happened in the past
identify any trends over time, and
project future levels for the time series
Time Series Data (2 of 2)
Graph of Time Series Data
Data Sources (1 of 5)
Existing Sources
Internal company records – almost any department
Business database services – Dow Jones & Co.
Government agencies - U.S. Department of Labor
Industry associations – Travel Industry Association of America
Special-interest organizations – Graduate Management Admission Council (GMAT)
Internet – more and more firms
Data Sources (2 of 5)
Data Available From Internal Company Records
Data Sources (3 of 5)
Data Available From Selected Government Agencies
Data Sources (4 of 5)
Statistical Studies – Observational
In observational (nonexperimental) studies no attempt is made to control or influence the variables of interest.
Example - Survey
Studies of smokers and nonsmokers are observational studies because researchers do not determine or control who will smoke and who will not smoke.
Data Sources (5 of 5)
Statistical Studies – Experimental
In experimental studies the variable of interest is first identified. Then one or more other variables are identified and controlled so that data can be obtained about how they influence the variable of interest.
The largest experimental study ever conducted is believed to be the 1954 Public Health Service experiment for the Salk polio vaccine. Nearly two million U.S. children (grades 1- 3) were selected.
Canadian Government Data
Virtually all Canadian data can be found at Statistics Canada.
The website is 

http://www.statcan.gc.ca/start-debut-eng.html


Data Acquisition Considerations
Time Requirement
Searching for information can be time consuming.
Information may no longer be useful by the time it is available.
Cost of Acquisition
Organizations often charge for information even when it is not their primary business activity.
Data Errors
Using any data that happen to be available or were acquired with little care can lead to misleading information.
Descriptive Statistics
Most of the statistical information in newspapers, magazines, company reports, and other publications consists of data that are summarized and presented in a form that is easy to understand.
Such summaries of data, which may be tabular, graphical, or numerical, are referred to as descriptive statistics.
Example
The manager of Hudson Auto would like to have a better understanding of the cost of parts used in the engine tune-ups performed in her shop. She examines 50 customer invoices for tune-ups. The costs of parts, rounded to the nearest dollar, are listed on the next slide.
Example: Hudson Auto Repair
Sample of Parts Cost ($) for 50 Tune-ups
91, 78, 93, 57, 75, 52, 99, 80, 97, 62
71, 69, 72, 89, 66, 75, 79, 75, 72, 76
104, 74, 62, 68, 97, 105, 77, 65, 80, 109
85, 97, 88, 68, 83, 68, 71, 69, 67, 74
62, 82, 98, 101, 79, 105, 79, 69, 62, 73
Tabular Summary: Frequency and Percent Frequency
Graphical Summary: Histogram
Example: Hudson Auto
Numerical Descriptive Statistics
The most common numerical descriptive statistic is the mean (or average).
The mean demonstrates a measure of the central tendency, or central location of the data for a variable.
Hudson’s mean cost of parts, based on the 50 tune-ups studied is $79 (found by summing up the 50 cost values and then dividing by 50).
Statistical Inference
Population: The set of all elements of interest in a particular study.
Sample: A subset of the population.
Statistical inference: The process of using data obtained from a sample to make estimates and test hypotheses about the characteristics of a population.
Census: Collecting data for the entire population.
Sample survey: Collecting data for a sample.
Process of Statistical Inference
Example: Hudson Auto
Analytics
Analytics is the scientific process of transforming data into insight for making better decisions.
Techniques:
Descriptive analytics: This describes what has happened in the past.
Predictive analytics: Use models constructed from past data to predict the future or to assess the impact of one variable on another.
Prescriptive analytics: The set of analytical techniques that yield a best course of action.
Big Data and Data Mining:
Big data: Large and complex data set.
Three V’s of Big data:
Volume : Amount of available data
Velocity: Speed at which data is collected and processed
Variety: Different data types
Data Warehousing
Data warehousing is the process of capturing, storing, and maintaining the data.
Organizations obtain large amounts of data on a daily basis by means of magnetic card readers, bar code scanners, point of sale terminals, and touch screen monitors.
Wal-Mart captures data on 20-30 million transactions per day.
Visa processes 6,800 payment transactions per second.
Data Mining
Methods for developing useful decision-making information from large databases.
Using a combination of procedures from statistics, mathematics, and computer science, analysts “mine the data” to convert it into useful information.
The most effective data mining systems use automated procedures to discover relationships in the data and predict future outcomes prompted by general and even vague queries by the user.
Data Mining Applications
The major applications of data mining have been made by companies with a strong consumer focus such as retail, financial, and communication firms.
Data mining is used to identify related products that customers who have already purchased a specific product are also likely to purchase (and then pop-ups are used to draw attention to those related products).
Data mining is also used to identify customers who should receive special discount offers based on their past purchasing volumes.
Data Mining Requirements
Statistical methodology such as multiple regression, logistic regression, and correlation are heavily used.
Also needed are computer science technologies involving artificial intelligence and machine learning.
A significant investment in time and money is required as well.
Data Mining Model Reliability
Finding a statistical model that works well for a particular sample of data does not necessarily mean that it can be reliably applied to other data.
With the enormous amount of data available, the data set can be partitioned into a training set (for model development) and a test set (for validating the model).
There is, however, a danger of overfitting the model to the point that misleading associations and conclusions appear to exist.
Careful interpretation of results and extensive testing is important.
Ethical Guidelines for Statistical Practice (1 of 2)
In a statistical study, unethical behavior can take a variety of forms including:
Improper sampling
Inappropriate analysis of the data
Development of misleading graphs
Use of inappropriate summary statistics
Biased interpretation of the statistical results
One should strive to be fair, thorough, objective, and neutral as you collect, analyze, and present data.
As a consumer of statistics, one should also be aware of the possibility of unethical behavior by others.
Ethical Guidelines for Statistical Practice (2 of 2)
The American Statistical Association developed the report “Ethical Guidelines for Statistical Practice”.
It contains 67 guidelines organized into 8 topic areas:
Professionalism
Responsibilities to Funders, Clients, Employers
Responsibilities in Publications and Testimony
Responsibilities to Research Subjects
Responsibilities to Research Team Colleagues
Responsibilities to Other Statisticians/Practitioners
Responsibilities Regarding Allegations of Misconduct
Responsibilities of Employers Including Organizations, Individuals, Attorneys, or Other Clients
End of Chapter 1

This is a satirical website. Don't take it Seriously. It's a joke.