Business Statistics: A First Course PDF

C h a p 1 - 1 Business Statistics: A First Course Fifth Edition Chapter 1 Introduction and Data Collection 1.1 - 1 C h a p 1 - 2 Learning Objectives In this chapter you learn: How St...

C h a p 1 - 1 Business Statistics: A First Course Fifth Edition Chapter 1 Introduction and Data Collection 1.1 - 1 C h a p 1 - 2 Learning Objectives In this chapter you learn: How Statistics is used in business The sources of data used in business The types of data used in business The basics of Microsoft Excel 1.1 - 2 C h a p 1 - Why Learn Statistics? 3 So you are able to make better sense of the ubiquitous use of numbers: –Business memos –Business research –Technical reports –Technical journals –Newspaper articles –Magazine articles An example: “Consumer payment with credit card increased from 18% in 2020 to 25%, while payment in cash decreased to 14% from 21%” 1.1 - 3 C h a p 1 - What is statistics? 4 A branch of mathematics taking and transforming numbers into useful information for decision makers Methods for processing & analyzing numbers Methods for helping reduce the uncertainty inherent in decision making 1.1 - 4 C h a p 1 - Why Study Statistics? 5 Decision Makers Use Statistics To: ▪ Present and describe business data and information properly (Figure or Table) ▪ Draw conclusions about large groups of individuals or items, using information collected from subsets of the individuals or items (e.g., weight of students in our class) ▪ Make reliable forecasts about a business activity (e.g., oil price) ▪ Improve business processes 1.1 - 5 Introduction to Statistics 1.1 - 6  Types of Statistics Statistics The branch of mathematics that transforms data into useful information for decision makers. Descriptive Statistics Inferential Statistics Collecting, summarizing, Drawing conclusions and/or and describing data (e.g., making decisions concerning a mean, median-table, chart) population based only on sample data (e.g., which investment leads best return) 1.1 - 7 Chap 1-7  Descriptive Statistics Collect data – e.g., Survey Present data – e.g., Tables and graphs Characterize data – e.g., Sample mean =  X i n 1.1 - 8 Chap 1-8  Inferential Statistics Estimation – e.g., Estimate the population mean weight using the sample mean weight Hypothesis testing – e.g., Test the claim that the population mean weight is 120 pounds Drawing conclusions about a large group of individuals based on a subset of the large group. 1.1 - 9 Chap 1-9  Data ❖ Data collections of observations (such as measurements, genders, survey responses) Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 10  Statistics ❖ Statistics is the science of planning studies and experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 11  Population ❖ Population the complete collection of all individuals (scores, people, measurements, and so on) to be studied; the collection is complete in the sense that it includes all of the individuals to be studied Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 12  Census versus Sample ❖ Census Collection of data from every member of a population ❖ Sample Subcollection of members selected from a population Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 13  ❖ Sample data must be collected in an appropriate way, such as through a process of random selection. ❖ If sample data are not collected in an appropriate way, the data may be so completely useless that no amount of statistical torturing can salvage them. Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 14  Types of Data Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 15  Parameter ❖ Parameter a numerical measurement describing some characteristic of a population. population parameter Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 16  Statistic ❖ Statistic a numerical measurement describing some characteristic of a sample. sample statistic Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 17  Types of Data Data Categorical Numerical Examples: ◼ Marital Status ◼ Political Party Discrete Continuous ◼ Eye Color (Defined categories) Examples: Examples: ◼ Number of Children ◼ Weight ◼ Defects per hour ◼ Voltage (Counted items) (Measured characteristics) Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. 1.1 Chap -1-18 18  Quantitative Data ❖ Quantitative (or numerical) data consists of numbers representing counts or measurements. Example: The weights of supermodels Example: The ages of respondents Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 19  Categorical Data ❖Categorical (or qualitative or attribute) data consists of names or labels (representing categories) Example: The genders (male/female) of professional athletes Example: Shirt sizes on professional athletes uniforms - substitutes for names. Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 20  Working with Quantitative Data Quantitative data can further be described by distinguishing between discrete and continuous types. Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 21  Discrete Data ❖ Discrete data result when the number of possible values is either a finite number or a ‘countable’ number (i.e. the number of possible values is 0, 1, 2, 3,...) Example: The number of eggs that a hen lays Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 22  Continuous Data ❖ Continuous (numerical) data result from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps, interruptions, or jumps Example: The amount of milk that a cow produces; e.g. 2.343115 gallons per day Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 23  Levels of Measurement Another way to classify data is to use levels of measurement. Four of these levels are discussed in the following slides. Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 24  Nominal Level ❖ Nominal level of measurement characterized by data that consist of names, labels, or categories only, and the data cannot be arranged in an ordering scheme (such as low to high) Example: Survey responses yes, no, undecided Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 25  Ordinal Level ❖ Ordinal level of measurement involves data that can be arranged in some order, but differences between data values either cannot be determined or are meaningless Example: Course grades A, B, C, D, or F Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 26  Interval Level ❖ Interval level of measurement like the ordinal level, with the additional property that the difference between any two data values is meaningful, however, there is no natural zero starting point (where none of the quantity is present) - Negative values are defined Example: Years 1000, 2000, 1776, and 1492 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 27  Ratio Level ❖ Ratio level of measurement the interval level with the additional property that there is also a natural zero starting point (where zero indicates that none of the quantity is present); for values at this level, differences and ratios are meaningful - true zero point (zero means nothing) - Example: Prices of college textbooks ($0 represents no cost, a $100 book costs twice as much as a $50 book) Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 28 Summary - Levels of Measurement ❖ Nominal - categories only ❖ Ordinal - categories with some order ❖ Interval - differences but no natural starting point ❖ Ratio - differences and a natural starting point Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 29  Collecting Sample Data Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 30  Key Concept ❖ If sample data are not collected in an appropriate way, the data may be so completely useless that no amount of statistical torturing can salvage them. ❖ Method used to collect sample data influences the quality of the statistical analysis. ❖ Of particular importance is simple random sample. Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 31  Basics of Collecting Data Statistical methods are driven by the data that we collect. We typically obtain data from two distinct sources: observational studies and experiment. Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 32  Observational Study ❖ Observational study observing and measuring specific characteristics without attempting to modify the subjects being studied Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 33  Experiment ❖ Experiment apply some treatment and then observe its effects on the subjects; (subjects in experiments are called experimental units) Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 34  Simple Random Sample ❖ Simple Random Sample of n subjects selected in such a way that every possible sample of the same size n has the same chance of being chosen Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 35  Random & Probability Samples ❖ Random Sample members from the population are selected in such a way that each individual member in the population has an equal chance of being selected ❖ Probability Sample selecting members from a population in such a way that each member of the population has a known (but not necessarily the same) chance of being selected Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 36  Random Sampling selection so that each individual member has an equal chance of being selected Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 37  Systematic Sampling Select some starting point and then select every kth element in the population Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 38  Convenience Sampling use results that are easy to get Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 39  Stratified Sampling subdivide the population into at least two different subgroups that share the same characteristics, then draw a sample from each subgroup (or stratum) Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 40  Cluster Sampling divide the population area into sections (or clusters); randomly select some of those clusters; choose all members from selected clusters Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 41  Multistage Sampling Collect data by using some combination of the basic sampling methods In a multistage sample design, pollsters select a sample in different stages, and each stage might use different methods of sampling Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 42 Methods of Sampling - Summary ❖ Random ❖ Systematic ❖ Convenience ❖ Stratified ❖ Cluster ❖ Multistage Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 1.1 - 43 C h a p 1 - Personal Computer Programs Used 4 4 For Statistics Minitab – A statistical package to perform statistical analysis – Designed to perform analysis as accurately as possible Microsoft Excel – A multi-functional data analysis tool – Can perform many functions but none as well as programs that are dedicated to a single function. Both Minitab and Excel use worksheets to store data Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. 1.1 - 44 C h a p 1 - Minitab & Microsoft Excel Terms 4 5 ▪ When you use Minitab or Microsoft Excel, you place the data you have collected in worksheets. ▪ The intersections of the columns and rows of worksheets form boxes called cells. ▪ If you want to refer to a group of cells that forms a contiguous rectangular area, you can use a cell range. ▪ Both worksheets and projects can contain both data, summaries, and charts. Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. 1.1 - 45 C h a p 1 - You are using programs properly if 4 6 you can Understand how to operate the program Understand the underlying statistical concepts Understand how to organize and present information Know how to review results for errors Make secure and clearly named backups of your work Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. 1.1 - 46 C h a p 1 - Chapter Summary 4 7 In this chapter, we have ▪ Reviewed why a manager needs to know statistics ▪ Introduced key definitions: ▪ Population vs. Sample ▪ Categorical vs. Numerical data ▪ Examined descriptive vs. inferential statistics ▪ Reviewed data types ▪ Discussed Minitab and Microsoft Excel terms Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. 1.1 - 47