W7- OB Notes Part 2

Chapter 1: What is Statistics?

Overview

Statistics is a way to extract information from data. The text describes how, when, and why statistical procedures are conducted in management. The vastness of the book corresponds to the diversity of statistical methods and applications.

Appendix 1: Material to Download

Types of Statistics

Descriptive Statistics

• Organizing, summarizing, and presenting data conveniently.

• Covered in Chapters 2 and 3 with various graphical methods including bar charts, histograms, and box plots for better visual analysis of data distribution.

• Chapter 4 presents numerical measures that summarize data features, including:

  • Measures of location (mean, median, mode) examples include calculating average salaries in a company.

  • Measures of variability (range, variance, standard deviation) showing how much variation exists in test scores among students.

Inferential Statistics

• Uses sample data to make conclusions about a population.

• Applied in real-world scenarios, such as:

  • Pepsi's exclusivity at a university: A university offers Pepsi a contract for exclusive rights to sell products, analyzing potential gains can inform decision-making.

  • Example of exit polls in elections: Surveys taken to predict election outcomes can guide political strategies.

Case Examples

Case 12.2: Pepsi’s Exclusivity Agreement

Background

A university offers Pepsi a contract for exclusive rights to sell products. Pepsi provides data, such as current sales volume and pricing.

Analysis & Gains

• Analysis based on a 25% market share projection:

  • Total cans sold annually = 3,520,000.

  • Gross revenue (after university's cut) = $2,288,000.

  • Costs total $1,056,000; additional payment to the university is $200,000.

  • Resulting net profit = $1,032,000.

• Current profit = $616,000 leading to a potential gain of $416,000.

Survey Approach

A survey of 500 randomly selected students to gather data on soft drink consumption. This implies using inferential statistics to estimate consumption for the entire university population of 50,000.

Applications in Political Elections

Exit Polls

• Used to predict election outcomes by sampling voters exiting polling places.

• Data helps determine the support for candidates using statistical inference, providing real-time analytics during elections.

• Example: Bush vs. Gore, 2000 Election

  • Polls analyze 765 voters to infer the overall voting pattern in Florida:

    • Population: Approx. 5 million voters.

    • Sample: 765 voters from polling stations.

  • Investigate specific questions like the proportion voting for Bush.

  • Importance of recognizing limitations: conclusions based on samples can include margins of error.

Key Statistical Concepts

• Population: Group of all items of interest.

• Parameters are descriptive measures of a population.

• Sample: Subset of the population.

• Statistics describe measures of a sample.

• Statistical Inference: Estimating population characteristics based on sample data.

  • Example: If a sample of 1,000 voters shows 70% support for a policy, statistical inference allows us to estimate that potentially 70% of the whole voter population supports the policy, with a margin of error.

Confidence and Significance Levels

• Confidence Level: Expected accuracy of an estimate (e.g., 95%).

  • Indicates that if we conducted the same study 100 times, we expect 95 of those studies to produce results within the margin of error of the true population parameter.

• Significance Level: Probability of making a wrong conclusion over time (e.g., 5%).

  • This is used in hypothesis testing to determine whether the results can be considered statistically significant.

Statistical Applications in Business

Statistics plays a key role in various business domains:

• Finance: Market models, risk management, portfolio analysis, and predicting market trends based on historical data.

• Marketing: Market segmentation, targeting strategies, customer satisfaction surveys, and A/B testing for product changes.

• Human Resources: Pay equity analyses, employee satisfaction evaluations, and workforce planning.

• Operations Management: Reducing variation, optimizing production processes, and quality control measures.

Large Real Data Sets

• General Social Survey (GSS): Important source of American social data, conducted biennially to track social trends over time.

  • Enables analysis of social behavior and attitudes.

• Survey of Consumer Finances (SCF): Conducted triennially, details household finances which helps understand economic disparities in detail.

Statistics and the Computer

• Microsoft Excel usage:

  • General statistical computations such as calculating averages and creating charts.

  • Excel Analysis ToolPak for advanced methods such as regression analysis and hypothesis testing.

  • Step-by-step guides for statistical scenarios enable users to apply methods without extensive statistical backgrounds.

Resources for Students

• Access additional materials at Cengage’s website using the book’s ISBN.

• Available resources include:

  • Data sets, Excel workbooks, online appendices, and formula cards.