Harnessing Technology to make education free

Blog / ePortfolio / Forums / 289 courses / FAQ / digedcon

This course has recently been updated.  To view the archived version of the course, please go here.

Business Statistics

Purpose of Course  showclose

This course will introduce you to business statistics, or the application of statistics in the workplace. Statistics is a course in the methods for gathering, analyzing, and interpreting data. If you have taken a statistics course in the past, you may find some of the topics in this course familiar. You can apply statistics to any number of fields – from anthropology to hedge fund management – because many of us best interpret data when it is presented in an organized fashion (as it is with statistics). You can analyze data in any number of forms. Summary statistics, for example, provide an overview of a data set, such as the average score on an exam. However, the average does not always tell the entire story; for example, if the average score is 80, it may be because half of the students received 100s and the other half received 60s. This would present a much different story than if everyone in the class had received an 80, which demonstrates consistency. Statistics provides more than simple averages. In this course, you will learn how to apply statistical tools to analyze data, draw conclusions, and make predictions of the future. The course will begin with data distributions, followed by probability analysis, sampling, hypothesis testing, inferential statistics, and, finally, regression. This course is mathematically intensive, and much of what you learn here will deal with things you encounter every day. This course also makes use of spreadsheets, an important tool for working with and making sense of numerical data.

Course Information  showclose

Welcome to BUS204. General information about this course and its requirements can be found below.

Course Designer: David T. Bourgeois, Ph.D.

Primary Resources: This course makes primary use of the following materials:
The relationship between the book chapters and the video lectures is listed below. Please note the different lecture numbers depending upon if you access them via YouTube or iTunes U.

YouTube iTunes U SticiGui Chapters Covered
Lecture 1 Lecture 1 Intro to class, Chapter 1 (0:00 to 57:28)
Lecture 2 Lecture 2 Chapter 3 (1:03:00 to 1:16:08)
Lecture 3 Lecture 3 Chapter 3 (0:00 to 54:00),
Chapter 4 (54:00 to 1:14:23)
Lecture 4 Lecture 4 Chapter 4 (0:00 to 50:00),
Chapter 5 (50:00 to 1:16:12)
Lecture 5 Lecture 5 Chapter 5 (0:00 to 38:00),
Chapter 7 (41:00 to 1:18:57)
Lecture 6 Lecture 6 Chapter 9 (3:50 to 57:50),
Chapter 11 (57:50 to 1:11:48)
Lecture 7 Lecture 7 Chapter 11 (0:00 to 47:00),
Lecture 8 Lecture 8 Chapter 12 (0:00 to 1:16:41)
Lecture 9 Lecture 10 Chapter 13 (0:00 to 1:15:02)
Lecture 11 Lecture 14 Chapter 14 (0:00 to 1:20:52)
Lecture 12 Lecture 15 Chapter 17 (0:00 to 1:20:59)
Lecture 13 Lecture 16 Chapter 17 (0:00 to 1:23:41)
Lecture 14 Lecture 17 Chapter 17 (0:00 to 56:00),
Chapter 18 (56:00 to 1:25:13)
Lecture 15 Lecture 18 Chapter 19 (0:00 to 1:21:08)
Lecture 16 Lecture 19 Chapter 20 (0:00 to 1:25:21)
Lecture 17 Lecture 20 Chapter 21 (0:00 to 1:24:52)
Lecture 18 Lecture 21 Chapter 22 (0:00 to 1:20:42)
Lecture 19 Lecture 22 Chapter 23 (0:00 to 1:19:00),
Chapter 24 (1:19:00 to 1:23:45)
Lecture 20 Lecture 23 Chapter 24 (0:00 to 1:19:31)
Lecture 21 Lecture 24 Chapter 25 (0:00 to 1:05:00),
Chapter 26 (1:05:00 to 1:18:42)
Lecture 22 Lecture 25 Chapter 26 (0:00 to 1:12:00),
Chapter 27 (1:12:00 to 1:20:03)
Lecture 23 Lecture 26 Chapter 27 (0:00 to 1:20:57)
Lecture 25 Lecture 29 Review of semester
Note: There are no lectures for chapters 15, 16, 29, and 30. 

Special Instructions on SticiGui exercises and Java applets: Every chapter in the online textbook has exercises to check your understanding; you should carefully work through each exercise. The author of the text has provided instructions on how to work with the exercises here: SticiGui: “How to Use These Materials.”*
 
Embedded throughout the text are the SticiGui Java applets. Think of these applets as little software programs that you can use to explore the different aspects of statistics you are learning. Each Java applet has instructions posted next to it in the text. You can find a listing of all the SticiGui Java applets here;* follow the links to each applet for further description and instructions on how to use each one. These applets should be compatible will all major browsers; Java needs to be enabled in order for them to work. 
 
The minimum system requirements for using these tools are detailed here.*

Requirements for Completion: In order to complete this course, you will need to work through each unit and all of its assigned materials. A key point to remember is that to get the most out of this course, you will need to complete each assignment and be sure you understand it! Many of the units build on each other, so you will want to complete the course in order.

Note that not all of the listed readings and lectures are required for this course. Those that are not required are listed as optional. If you would like to experience the full course as it was presented at University of California, Berkeley, then you will want to go through these optional sections as well. Note that anything considered optional will not be on the Final Exam.

Besides reviewing these materials, you will also need to complete: 
  • The interactive examples included in the online textbook (SticiGui Exercises and Java applets)
  • The assessments at the end of each unit
  • The spreadsheet activities at the end of Units 1, 3, and 6
  • The Final Exam
Note that you will only receive an official grade on your final exam. However, in order to adequately prepare for this exam, you will need to work through all required course materials and the assignments listed above.

In order to pass this course, you will need to earn a 70% or higher on the final exam. Your score on the exam will be tabulated as soon as you complete it. If you do not pass the exam, you may take it again.

Time Commitment: Completing this course should take you a total of 122.25 hours. Note that there are some resources that are indicated as optional. These resources total an additional 24.25 hours. Each unit includes a time advisory that lists the amount of time you are expected to spend on each subunit. These should help you plan your time accordingly. It may be useful to take a look at these time advisories, to determine how much time you have over the next few weeks to complete each unit, and then to set goals for yourself. For example, Unit 1 should take you 13.75 hours. Perhaps you can sit down with your calendar and decide to complete subunits 1.1 and subunit 1.2 (total of 4.75 hours) on Monday and Tuesday nights; subunit 1.3 (about 4.5 hours) on Wednesday and Thursday nights; etc.
 
Tips/Suggestions: This course uses a complete online textbook, developed by Dr. Philip Stark at the University of California, Berkeley. This textbook is used in Dr. Stark’s Business Statistics course. This textbook includes several interactive examples that you can use to enhance your learning. Do not skip these examples! Learn how they work and try to understand them. Many times, Dr. Stark will use these examples in the online video lectures, so be sure to watch for them and work the examples as he works them in class. You can read what the author of the materials has to say about the best way to use these materials by reviewing the material in the Preface.*
 
Make sure to also take notes as you navigate through the resources in this course. These notes will be useful to study from as you prepare to take your final exam.

*Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

Khan Academy  
This course features a number of Khan Academy™ videos. Khan Academy™ has a library of over 3,000 videos covering a range of topics (math, physics, chemistry, finance, history and more), plus over 300 practice exercises. All Khan Academy™ materials are available for free at www.khanacademy.org.

Learning Outcomes  showclose

Upon successful completion of this course, you will be able to:
  • explain the importance of statistics to business;
  • explain the differences between quantitative and qualitative data, and identify examples of each type of data;
  • define and apply the following terms: data sets, mean, median, mode, standard deviation, and variance;
  • summarize and interpret data in a tabular format using frequency distributions and visually with histograms;
  • define and apply the concept of a probability distribution, and explain the properties of different distributions;
  • differentiate between discrete and continuous probability distributions;
  • define and apply the concept of a random variable, and differentiate the population from a sample;
  • relate the central limit theorem to sample size and normal distribution;
  • describe and identify the different sampling methods, including systematic, stratified random, cluster, convenience, panel, and quota sampling, and identify examples of each;
  • use a point estimator from a sample to estimate the entire population;
  • estimate intervals over which the population parameter could exist using sample data;
  • apply hypothesis testing for testing population parameters using one or two samples;
  • identify the dependent and independent variables in the linear regression model;
  • plot a regression line, and explain how the regression coefficient shapes that line; and
  • work with statistical data in a spreadsheet environment.

Course Requirements  showclose

In order to take this course, you must:

√    have access to a computer;

√    have continuous broadband Internet access;

√    have the ability/permission to install plug-ins or software (e.g. Adobe Reader or Flash);

√    make sure your browser meets the minimum requirements laid out for the online textbook;*

√    have the ability to download and save files and documents to a computer;

√    be competent in the English language.

√    have access to a calculator that includes the ability to do square roots. (A statistical calculator is available as part of the online textbook we are using here.* You may also use the calculator that comes with your operating system, which should have square root capability if you set it to the proper mode.);

√    have read the Saylor Student Handbook; and

√    have completed either MA001, MA003, MA005, or equivalent.

It is suggested, but not mandatory, that you have completed all courses listed in the “The Core Program” section of the Business Administration discipline: BUS103, BUS105, BUS200/ECON101, BUS201/ECON102, BUS202, and BUS203.

*Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

Unit Outline show close


Expand All Resources Collapse All Resources
  • Unit 1: Introduction to Statistical Analysis  

    Statistics may appear to be a difficult, even scary, subject. You will find, however, that you are already familiar with the fundamentals of statistics from your life experience. For instance, from your experience, you know that the majority of adult males have the same shoe size, which is very close to the average size, and that there are a few adult males on both sides of the average (small and large size). In statistics, this phenomenon shown from the data pattern is said to be a variable that follows a normal distribution.

    This unit will provide an introduction to statistical analysis and how it relates to business. For example, you may be interested in learning about the average price of a 50-inch digital TV by gathering the price for it from 30 different stores. You take your 30 prices and compute the average price. Given the fact that there are thousands of stores that are selling that particular product, the next question in statistics is: Are you confident enough to say that your computed average is reflective of the real average that would be computing from all the existing prices for that TV sold at all stores?

    You are probably familiar with the average of a data set. In this course, we will refer to what most people call the average as the arithmetic mean. The average is actually any single value used to describe the middle of a data set. The most common averages used in statistics are the arithmetic mean, the median, and the mode. Each describes the middle of a dataset in different ways. For example, the median is the numeric value that separates the upper and lower half of a data set. The mean is the sum of all values divided by the number of values. The mode is the most common value within the dataset.

    In many instances, the median and the mean are similar, but this introductory unit will also identify many examples where it is not. The distinction between summary statistics is important in business statistics. This unit will define various terms that you may not be familiar with, such as variance and outliers. Understanding this vocabulary will be vital to the successful completion of this course.

    Unit 1 Time Advisory   show close
    Unit 1 Learning Outcomes   show close
  • 1.1 Why Do We Need to Study Statistical Analysis as Part of a Business Program?  
  • 1.2 Measuring Data  
    • Reading: University of California, Berkeley: Philip Stark’s SticiGui: “Chapter 3: Statistics”

      Link: University of California, Berkeley: Philip Stark’s SticiGui: “Chapter 3: Statistics” (PDF)
       
      Instructions: Read this chapter for an introduction on how to present a summary of data through graphs, tables, and numerical measures such as the average. This will be helpful in terms of analyzing business data in a simple way with the help of the widely-used methods in statistical analysis. There are several exercises and Java applets embedded in the text that are meant to further reinforce your learning. Do not skip these exercises! For instructions on how to navigate these exercises, see “Special Instructions on SticiGui Exercises and Java Applets” in the “Course Information” section, and check out the author’s instructions in SticiGui: “How to Use These Materials”. This resource covers the topics outlined in subunits 1.2.1 to 1.2.7.

      Reading this chapter should take approximately 3 hours.

      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

    • Lecture: YouTube: University of California, Berkeley: Philip Stark’s Statistics 21: “Lecture 2” and “Lecture 3”

      Link: YouTube: University of California, Berkeley: Philip Stark’s Statistics 21: “Lecture 2” (YouTube) and “Lecture 3” (YouTube)
       
      Also available in:
      iTunes U (Lectures 2 and 3)
       
      Instructions: Watch “Lecture 2” from 1:03:00 to the end, and then watch “Lecture 3” from the beginning until 54:00. These videos are companion lectures to Chapter 3, with the author of the text working through the materials. This resource covers the topics outlined in subunits 1.2.1 to 1.2.7.

      Watching these lectures and pausing to take notes should take approximately 1 hour and 30 minutes.
       
      Terms of Use: The videos above are licensed under a Creative Commons Attribution-Noncommercial-No Derivative License. They are attributed to Philip Stark and the University of California, Berkeley. The original versions can be found here and here.

      See a broken link? Please let us know!

  • 1.2.1 Types of Variables: Quantitative vs. Qualitative  

    Note: The material beneath subunit 1.2 covers this topic. For the reading material, focus on the text below the heading “Variable.” Carefully review the box labeled “Examples of Quantitative, Qualitative, and Categorical Variables.” Make sure that you understand the differences between quantitative data, which deals with numeric information, and qualitative data, which measures categories or characteristics.

  • 1.2.2 Sample Data Sets  

    Note: The material beneath subunit 1.2 covers this topic. Note that a sample is a portion of a population under interest. For example, suppose that you are interested in computing the average age of a manufacturing plant in the United States. Knowing that there are thousands of manufacturing plants, you are more likely to choose for your analysis just a sample, of say 200 manufacturing plants, from the entire population. In the text below the heading “Sample Data Sets,” pay special attention to the data set given in Table 3-3. This dataset will be used throughout the rest of that chapter.

  • 1.2.3 Frequency Tables  

    Note: The material beneath subunit 1.2 covers this topic. Review the dataset in Table 3-3 in the text and the frequency table that follows the table as these tables show you how to put numerical data into a frequency table with the use of class intervals from the data.

  • 1.2.4 Histograms  

    Note: The material beneath subunit 1.2 covers this topic. Be aware that a histogram is a graph that uses the information from a frequency table.

  • 1.2.5 Skewness and Modes  

    Note: The material beneath subunit 1.2 covers this topic. As explained in the text below the heading “Skewness and Modes,” a histogram of incomes of individuals or home prices tends to be skewed to the right. Skewness is a measure that shows whether the distribution of a variable is symmetric.

  • 1.2.6 Percentiles and Quartiles  

    Note: The material beneath subunit 1.2 covers this topic. Note that percentiles are about cutting the histogram into 100 equal pieces. Quartiles are about cutting the histogram into 4 equal parts.

  • 1.2.7 Estimating Percentiles from Histograms  

    Note: The material beneath subunit 1.2 covers this topic. You should first consider how the 50th percentile is created by just cutting the histogram into two equal parts. Be sure to do the online exercises as shown below the heading “Estimating Percentiles from Histograms.”

  • 1.3 Measures of Spread and Data  
  • 1.3.1 Mean  

    Note: The material beneath subunit 1.3 covers this topic. As explained in Chapter 4, the mean applies to numerical data and not to a categorical variable, which does not have numbers.

  • 1.3.2 Median  

    Note: The material beneath subunit 1.3 covers this topic. As explained in Chapter 4, the median applies to numerical data and not to a categorical variable, which does not have numbers.

  • 1.3.3 Mode  

    Note: The material beneath subunit 1.3 covers this topic. As explained in Chapter 4, the mode applies to both numerical and categorical variables as it corresponds to the most frequent value.

  • 1.3.4 Spread and Variability  

    Note: The material beneath subunit 1.3 covers this topic. Make sure to watch the videos from the Khan Academy to learn how to compute the variance and the standard deviation in detail.

  • 1.3.5 Affine Transformations  

    Note: The material beneath subunit 1.3 covers this topic. Of concern is the definition of an affine transformation: those variables that are created from other variables through a direct relationship. An example is converting the temperature from Centigrade to Fahrenheit degrees.

  • 1.3.6 Markov’s Inequality  

    Note: The material beneath subunit 1.3 covers this topic. The best way to learn about this topic is to follow Example 4-4 in order to see the practicality of this concept.

  • 1.3.7 Chebyshev’s Inequality for Lists  

    Note: The material beneath subunit 1.3 covers this topic. Be sure to read Examples 4-5 and 4-6 as they show you, through examples, how Markov’s inequality and Chebyshev’s inequality differ from each other.

  • 1.4 Spreadsheet Exercises for Unit 1  
  • 1.4.1 Measures of Middle and Spread  
  • 1.4.2 Histograms and Frequency Tables  
  • 1.5 Assessments for Unit 1  
  • Unit 2: Counting, Probability, and Probability Distributions  

    What is the likelihood that an event will occur?  What are the chances that a given student will receive a 60-69 score?  By studying distributions of data, you can determine the probability that a certain event will occur.  By looking at the distribution of grades in a class, you can identify the probability that a student will receive between a 60 and 69.  The applications of probability in business are infinite; from predicting profits to determining the chances that a business model will affect regulation, businesses use probability to make decisions frequently.
     
    Before you can focus on probability, you must first learn how to count. What's that you say? You already know how to count? Maybe – but in this unit you will learn techniques for counting the different ways that multiple events can occur together. These are called “Combinations” and “Permutations,” and they are a fundamental concept needed to fully understand probability.

    Unit 2 Time Advisory   show close
    Unit 2 Learning Outcomes   show close
  • 2.1 Counting  
  • 2.1.1 The Fundamental Rule of Counting  

    Note: The reading and lecture assigned below subunit 2.1 cover this topic. The tossing of a coin is one of the classical examples taught in a probability course.  Make sure you understand the example given in the text under the section titled “The Fundamental Rule of Counting.”

  • 2.1.2 Permutations  

    Note: The reading and lecture assigned below subunit 2.1 cover this topic. Make sure you read Example 12-2 as it explains how to use the factorial sign (!) such as 4! = 4x3x2x1 = 24.

  • 2.1.3 Combinations  

    Note: The reading and lecture assigned below subunit 2.1 cover this topic. Know the difference between a permutation, when the order of possible choices matters, and a combination, when the order does not matter.  Of importance is the formula for computing combinations, make sure you know how to use it as explained in the text under the section titled “Combinations (Unordered Choices).”

  • 2.1.4 Card Hands  

    Note: The reading and lecture assigned below subunit 2.1 cover this topic. Make sure to read Examples 12-4 through 12-6 and the table above Example 12-4 in order to understand the different combinations of cards from a set of 52 typical cards.

  • 2.2 Theories of Probability  
  • 2.2.1 Random Events  

    Note: The reading and lecture assigned below subunit 2.2 cover this topic. This concept will appear throughout the course; make sure you understand that a random event is something that happens by chance alone.

  • 2.2.2 Equally Likely Outcomes  

    Note: The reading and lecture assigned below subunit 2.2 cover this topic. Make sure you know how to apply the concept by using the formula 100%/n, or 1/n, to compute the expected probability of an outcome with n possible outcomes.

  • 2.2.3 Frequency Theory  

    Note: The reading and lecture assigned below subunit 2.2 cover this topic. Make sure to know the difference between frequency and subjective theory in probability.

  • 2.2.4 Subjective Theory  

    Note: The reading and lecture assigned below subunit 2.2 cover this topic. Make sure to know the difference between frequency and subjective theory in probability.

  • 2.3 Set Theory  
  • 2.4 Probability Fundamentals  
  • 2.4.1 The Axioms of Probability  

    Note: The reading and lecture assigned below subunit 2.4 cover this topic. In probability, the symbol ∩ is used to denote “and” as in Probability of A and B = P(A ∩ B).  The inverted symbol is explained in the text under the section labeled “The Axioms of Probability.”

  • 2.4.2 Conditioning  

    Note: The reading and lecture assigned below subunit 2.4 cover this topic. Make sure you know how to use the formula for computing the conditional probability of an outcome as explained in the text under the section labeled “Conditioning.”

  • 2.4.3 The Multiplication Rule  

    Note: The reading and lecture assigned below subunit 2.4 cover this topic. You can follow an example of the multiplication rule in Example 17-8.

  • 2.4.4 Bayes' Rule  

    Note: The reading and lecture assigned below subunit 2.4 cover this topic. You can follow an example of the Bayes’ Rule formula in Example 17-9.

  • 2.4.5 Independence  
  • 2.5 Probability Distributions and the Binomial Distribution  
  • 2.6 The Long Run and Expected Value  
  • 2.6.1 The Law of Large Numbers  

    Note: The reading and lecture assigned below subunit 2.6 cover this topic. The Law of Large Numbers implies repeated sampling with replacement, meaning that the events considered are assumed to be independent from each other.

  • 2.6.2 Expected Value of a Random Variable  

    Note: The reading and lecture assigned below subunit 2.6 cover this topic. The expected value is another name for the “mean” (or average) when outcomes have probabilities attached to them.  Make sure you know how to compute the expected value as shown for the data from Table 21-1.

  • 2.6.3 Expected Value of a Sample Sum  

    Note: The reading and lecture assigned below subunit 2.6 cover this topic. Make sure you know how to apply the formula as given in the text under the section titled “Expected Value of a Sample Sum.”

  • 2.6.4 Properties of the Expected Value  

    Note: The reading and lecture assigned below subunit 2.6 cover this topic. Make sure you know how to apply the formulas as given in the text under the section titled “Properties of the Expected Value.”

  • 2.6.5 Expected Value of the Sample Mean and Sample Percentage  

    Note: The reading and lecture assigned below subunit 2.6 cover this topic. Make sure you know how to apply the formulas as given in the text under the section titled “Expected Value of the Sample Mean and Sample Percentage.”

  • 2.6.6 Gambling and Fair Bets  

    Note: The reading and lecture assigned below subunit 2.6 cover this topic. Make sure to read Example 21-3.

  • 2.6.7 Expected Values of Some Common Distributions  

    Note: The reading and lecture assigned below subunit 2.6 cover this topic. Note that the p used in the formulas stands for the probability of a successful outcome. For example, suppose that you are analyzing whether or not customers will buy your product and have these two possible outcomes: yes = successful outcome = the customer will buy and no = not successful = the customer will not buy. If the probability that someone will buy is 0.45; therefore, p = 0.45, and (1-p) = 0.55, which is the probability that someone will not buy.

  • 2.7 Assessments for Unit 2  
  • Unit 3: The Normal Distribution  

    A distribution is a line graph representation of the probability that an event will occur.  It is similar to a histogram, but in a distribution, the user does not determine the grouping; instead, data is grouped according to the likelihood that it will occur within the dataset.  Distributions also allow for analysis of a specific event, whereas a histogram requires events be grouped.
     
    An important type of this distribution is the “normal” distribution. The normal distributionis used to approximate real-world occurrences. If you can make certain assumptions about the occurrence of an event, then you can use the normal distribution to find out the probabilities of that event occurring. Many of the events that are important to business can be approximated using the normal distribution.

    Unit 3 Time Advisory   show close
    Unit 3 Learning Outcomes   show close
  • 3.1 The Normal Distribution  
  • 3.1.1 The Normal Curve  

    Note: The reading and lectures assigned below subunit 3.1 cover this topic. Make sure you know the properties of the Normal curve and how to get the probabilities using the Normal probability table.

  • 3.1.2 The Central Limit Theorem  

    Note: The reading and lectures assigned below subunit 3.1 cover this topic. The Central Limit Theorem states that as nincreases this higher nleads any variable to follow a normal distribution.

  • 3.1.3 Markov's and Chebyshev's Inequalities for Random Variables  

    Note: The reading and lectures assigned below subunit 3.1 cover this topic. Make sure you know the difference between these two concepts.

  • 3.2 Spreadsheet Activity for Unit 3  
  • 3.3 Assessments for Unit 3  
  • Unit 4: Sampling and Sampling Distributions  

    While you may not become a professional data gatherer, it is likely that you will need to compile data on a regular basis.  When gathering data, you will not always have the luxury of collecting all available data.  For example, economists cannot measure the entire unemployment of the population, so they must take a random sample instead.  Likewise, in a manufacturing facility, quality control managers do not have the resources to test every product that comes off the line; it is simply not feasible.  Instead, they take samples at various points during the production process to test the quality of the products the firm produces.

    There are a number of methods employed in sampling data.  It is important that the sampling method fits the application.  For example, marketing managers may wish to test a product on various groups of people.  They may define these groups by age, race, geography, income, or any other factors.  They then divide the population into these groups and take samples from each group in a process known as cluster sampling.  If marketers do not properly divide the population, they may end up marketing to the wrong demographic and achieving poor sales.

    Unit 4 Time Advisory   show close
    Unit 4 Learning Outcomes   show close
  • 4.1 Sampling and Sampling Distributions  
  • 4.1.1 Parameters and Statistics  

    Note: The reading and lectures assigned below subunit 4.1 cover this topic. A parameter is a characteristic (such as the average) of a population under interest, whereas a statistic is a characteristic of a sample.

  • 4.1.2 Why Sample?  

    Note: The reading and lectures assigned below subunit 4.1 cover this topic. Taking a sample that is representative of a population under interest is what the field of statistics is all about.  It is almost impractical in real life to analyze all the observations belonging to a population.

  • 4.1.3 Sample Surveys  

    Note: The reading and lectures assigned below subunit 4.1 cover this topic. Be aware that the rule in statistics is to take a sample that is representative of the entire population, meaning that sampling bias (such as taking a survey of only our friends in order to understand consumer behavior) should be avoided.

  • 4.1.4 Bias in Surveys  

    Note: The reading and lectures assigned below subunit 4.1 cover this topic. Focus on how a researcher can introduce bias into a survey.

  • 4.1.5 Sampling Designs  

    Note: The reading and lectures assigned below subunit 4.1 cover this topic. Focus on the different ways to draw observations for a sample as shown in the text under the section titled “Ways to Draw Samples.”

  • 4.1.6 Sampling Distributions  

    Note: The reading and lectures assigned below subunit 4.1 cover this topic. Be sure to view the Khan Academy’s video for this section as it explains the meaning of distributions that apply to sample data.

  • 4.2 Assessments for Unit 4  
  • Unit 5: Estimation and Hypothesis Testing  

    Estimation is the process of making predictions based on the best available information.  Businesses employ estimation in order to help managers make decisions regarding the future.  For example, if the CFO estimates profits will be lower next year, the CEO will consider cost-cutting measures to make up for the loss.  Normally, companies do not want to pursue aggressive cost-cutting because it usually comes in the form of layoffs, which are bad for employee morale.

    In order to make accurate estimates, companies use hypothesis testing.  For example, assume the CFO thinks profits will be below 5% of revenue next year.  His null hypothesis is that profits will be 5% or greater next year.  The alternative hypothesis is that profits will not be 5% or greater next year.  This seems counterintuitive but statistics proposes that a hypothesis cannot be proven true; it can only be rejected, or shown to be not true.  Through the hypothesis testing process, the CFO will either reject or accept the null hypothesis.  Hypothesis tests are always framed in this manner because, with imperfect information, nothing can be proven. 
     
    Note:  The best non-business analogy to hypothesis testing comes from the courtroom. In the United States, a defendant is presumed innocent until proven guilty. The null hypothesis in this scenario is innocent or not guilty. The alternative hypothesis is guilty. In order to find the defendant guilty, the jury must be offered enough evidence that suggests the defendant is guilty beyond a reasonable doubt. If the members of the jury make that decision, then they reject the null hypothesis. If the jury members decide they do not have enough evidence to make that judgment, then they must find the defendant not guilty. Notice not guilty does not mean the jury claims the defendant is innocent. The decision simply means the members of the jury do not have enough information to find the person guilty, so they err on the side of caution and fail to reject the null hypothesis. As an aside, in this example, beyond a reasonable doubt is analogous to the level of significance, which you will learn is crucial to hypothesis testing.

    Unit 5 Time Advisory   show close
    Unit 5 Learning Outcomes   show close
  • 5.1 Estimation  
  • 5.1.1 Estimating Means and Percentages  

    Note: The reading and lecture assigned below subunit 5.1 cover this topic. This section is about knowing how to use the mean from the sample data to estimate a range for the real population mean.  In terms of estimating percentages, the percentage of a given characteristic from the sample data is used to compute a range for the real population percentage.

  • 5.1.2 The Sample Standard Deviation and Variance  

    Note: The reading and lecture assigned below subunit 5.1 cover this topic. Recall that the standard deviation and variance were introduced in subunit 1.3 “Measures of Spread and Data.”

  • 5.2 Confidence Intervals  
  • 5.3 Hypothesis Testing: t-Tests  
  • 5.3.1 Examples of Hypothesis Testing Problems  

    Note: The reading and lectures assigned below subunit 5.3 cover this topic. Before covering this section from Chapter 27 under the section titled “Examples of Hypothesis Testing Problem,” make sure to read the first section about hypothesis testing.  Hypothesis testing is the scientific procedure needed when testing data from a sample and when experiments are conducted to test their significance.

  • 5.3.2 Significance Level and Power  

    Note: The reading and lectures assigned below subunit 5.3 cover this topic. In statistics, the significance level refers to the probability of concluding that the sample data give valid approximations for the real population parameters when in fact that is not true.  The key is to choose a small significance level.  This level is chosen by the data analyst but the most common one is the 0.05 significance level, which translates to using a 95% confidence level.

  • 5.3.3 Test Statistics and P-values  

    Note: The reading and lectures assigned below subunit 5.3 cover this topic. Make sure to read Examples 27-2 and 27-3.  Also, watch the video from the Khan Academy “Hypothesis Testing and P-values” as it shows you how to test a hypothesis.

  • 5.3.4 Hypotheses about Parameters  

    Note: The reading and lectures assigned below subunit 5.3 cover this topic. In Chapter 27 under the section titled “Hypotheses about Parameters,” pay special attention to the concepts: rejection region, one-sided, and two-sided tests.  Also, make sure to read the case study titled “Case Study: Employment Discrimination Arbitration” in order to understand how hypothesis testing is done in practice.

  • 5.3.5 Population Parameters Testing Using One or Two Samples  

    Note: The reading and lectures assigned below subunit 5.3 cover this topic. In addition to reading Chapter 27 from the main text, read the sections titled “10 Hypothesis Testing” and “11 Testing the Two Sample Means with the t-Test” in the reading from the College of Micronesia-FSM: Dana Lee Ling's Introduction to Statistics Using OpenOffice.org. Those sources will reinforce your understanding of the difference in hypothesis testing between one and two samples.

  • 5.3.6 Caveats: The Meaning of Rejection, Statistical Significance and Practical Importance, and Interpreting P-values  

    Note: The reading and lectures assigned below subunit 5.3 cover this topic. In Chapter 27 from the main text, make sure to read the final section titled “Caveats,” which explains the most common problems one makes when conducting hypothesis testing.

  • 5.4 Testing Equality of Two Percentages  
  • 5.5 The Multinomial Distribution and the Chi-Squared Test for Goodness of Fit  
  • 5.6 Assessments for Unit 5  
  • Unit 6: Correlation and Regression  

    If two data points move in the same direction, does that mean that one causes the other? How are we to analyze their correlation?

    Regression is an analysis of the relationship of one variable to another. A regression might identify, for example, the relationship between car speed and the number of fatal accidents. In this example, speed and number of accidents are the two variables; the number of accidents is said to be the dependent variable, because the number of accidents depends on the speed. Speed is considered the independent variable. While regressions can be calculated manually, a statistically significant dataset could take a long time to regress.

    Regressions not only allow us to determine whether a relationship exists but also to identify how strong that relationship is. The measure of this relationship is known as the regression coefficient. If the regression coefficient is relatively low, then speed may not be the major factor in fatal accidents. Perhaps the major factor is the time of day, whether it rained or not, or if alcohol was involved. With multiple regression, a number of independent variables can be tested against the dependent variable at the same time. The regression coefficient would determine which variables have the strongest relationship with the dependent variable. In business, you will frequently use regression to predict future events. Though not an exact science, regression can be used to make reliable predictions if enough variables are identified. For example, first responders could use regression outputs to predict the number of fatal accidents in a given shift based on average travel speed, time of day, weather, and any other factors deemed significant. This unit will also stress the importance of determining the factors that most likely contribute to a dependent variable.
     
    Regression is often used in finance. Investors often want to know the relationship between a stock’s performance and the overall performance of the market. By regressing the period returns of a stock with the returns of the market, investors can see the regression coefficient. This coefficient is known as a stock’s beta and is covered extensively in BUS202: Principles of Finance.

    Unit 6 Time Advisory   show close
    Unit 6 Learning Outcomes   show close
  • 6.1 Working with More Than One Variable  
    • Reading: University of California, Berkeley: Philip Stark's SticiGui: “Chapter 5: Multivariate Data and Scatterplots”

      Link: University of California, Berkeley: Philip Stark's SticiGui: “Chapter 5: Multivariate Data and Scatterplots” (HTML and Java)
       
      Instructions: Read Chapter 5 to learn how to analyze the relationship between two variables. Two variables may be positively or negatively related when different pairs of data show the same pattern. For example, when incomes of individuals rise so does their consumption of goods and services; thus, income and consumption are considered to be positively related. As a person’s income rises, the number of bus rides this person takes falls; thus, income and bus riding are negatively related. There are several exercises and Java applets embedded in the text that are meant to further reinforce your learning. Do not skip these exercises! For instructions on how to navigate these exercises, see “Special Instructions on SticiGui Exercises and Java Applets” in the “Course Information” section. This resource covers the topics outlined in subunits 6.1.1 to 6.1.4. 

      Reading this chapter should take approximately 3 hours.
       
      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

    • Lecture: YouTube: University of California, Berkeley: Philip Stark's Statistics 21: “Lecture 4” and “Lecture 5”

      Links: YouTube: University of California, Berkeley: Philip Stark's Statistics 21: “Lecture 4” (YouTube) and “Lecture 5” (YouTube)
       
      Also available in:
      iTunes U (Lectures 4 and 5)
       
      Instructions: Watch "Lecture 4" from 50:00 to the end and then watch “Lecture 5” from the beginning until 38:00. These videos complement Chapter 5, with the author of the text working through the materials. This resource covers the topics outlined in subunits 6.1.1 to 6.1.4.  

      Watching these lectures and pausing to take notes should take approximately 1.5 hours.
       
      Terms of Use: The videos above are licensed under a Creative Commons Attribution-Noncommercial-No Derivative License. They are attributed to Philip Stark and the University of California. The original versions can be found here and here (YouTube). 

      See a broken link? Please let us know!

  • 6.1.1 Multivariate Data  

    Note: The reading and lectures assigned below subunit 6.1 cover this topic. Pay attention to bivariate data, which is about tracking two variables for each observation.

  • 6.1.2 Scatterplots  

    Note: The reading and lectures assigned below subunit 6.1 cover this topic. Note that a scatterplot usually shows the dependent variable on the Y-axis and the independent variable on the X-axis.

  • 6.1.3 Outliers  

    Note: The reading and lectures assigned below subunit 6.1 cover this topic. Under the heading labeled “Describing Scatterplots,” review why an outlier, which is an unusual value, creates problems for getting summary statistics such as the mean.

  • 6.1.4 Association  

    Note: The reading and lectures assigned below subunit 6.1 cover this topic. Be sure to revisit the section labeled “Scatterplots” and note how two variables are said to be linearly associated as their paired values form a straight line that either moves up or down.

  • 6.2 Correlation and Association  
  • 6.2.1 The Correlation Coefficient  

    Note: The readings and lecture assigned below subunit 6.2 cover this topic. The correlation coefficient is a numerical measure between -1 and +1. It measures the strength of linear association between two variables. If it is close to -1, it means that two variables are strongly but negatively related. If it is close to +1, it means that two variables are strongly but positively related. Negative coefficients mean that two variables move in opposite directions, while positive coefficients mean that they move in the same direction. A coefficient of 0 means that there is no linear association between two variables. Make sure you understand the difference between correlation and causation as explained in that section of the text with the heading labeled “Correlation and Association.”

  • 6.2.2 The Effect of Nonlinear Association  

    Note: The readings and lecture assigned below subunit 6.2 cover this topic. A nonlinear association is when the correlation coefficient is 0 (or very close to it from either side).

  • 6.2.3 Computing the Correlation Coefficient  

    Note: The readings and lecture assigned below subunit 6.2 cover this topic. Make sure to read Chapter 12 by Dean and Illowsky to review how to compute the correlation coefficient when you are given n data points with each having a pair of X and Y values. Note that the formula uses the Greek letter sigma, ∑, as the summation symbol. For instance, ∑ Xi = X1 + X2 + X3 when i = 1, 2, 3.

  • 6.3 Regression  
    • Reading: University of California, Berkeley: Philip Stark's SticiGui “Chapter 9: Regression”

      Link: University of California, Berkeley: Philip Stark's SticiGui: “Chapter 9: Regression” (HTML and Java)
       
      Instructions: Read Chapter 9 to learn how to analyze X and Y data, where the X variable is considered the independent variable and Y the dependent variable. Regression analysis is used to determine how the X values affect the Y values by assuming that there is a linear relationship between them. There are several exercises and Java applets embedded in the text that are meant to further reinforce your learning. Do not skip these exercises!  For instructions on how to navigate these exercises, see “Special Instructions on SticiGui Exercises and Java Applets” in the “Course Information” section above. This resource covers the topics outlined in subunits 6.3.1 to 6.3.5.  

      Reading this chapter should take approximately 3 hours.
       
      Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

      See a broken link? Please let us know!

    • Lecture: YouTube: University of California, Berkeley: Philip Stark's Statistics 21: “Lecture 6”

      Link: YouTube: University of California, Berkeley: Philip Stark's Statistics 21: “Lecture 2” (YouTube)
       
      Also available in:
      iTunes U (Lecture 6)
       
      Instructions: Watch “Lecture 6” from 3:50 to the 57:50. This video is the companion to Chapter 9, with the author of the text working through the materials. This resource covers the topics outlined in subunits 6.3.1 to 6.3.5.  

      Viewing this lecture and pausing to take notes should take approximately 2 hours.
       
      Terms of Use: This video is licensed under a Creative Commons Attribution-Noncommercial-No Derivative License. It is attributed to Philip Stark and the University of California. The original version can be found at here.

      See a broken link? Please let us know!

  • 6.3.1 SD Line  

    Note: The reading and lecture assigned below subunit 6.3 cover this topic. Make sure to look at Figure 9-1 and focus on how the Standard Deviation (SD) line fits in an X-Y scatterplot.

  • 6.3.2 Graph of Averages  

    Note: The reading and lecture assigned below subunit 6.3 cover this topic. Make sure to look at Figure 9-2 and focus on how average values of Y fit within class intervals of the X data.

  • 6.3.3 Regression Line  

    Note: The reading and lecture assigned below subunit 6.3 cover this topic. Make sure to look at Figure 9-4 and focus on how the regression line is formed when the average values of Y are connected.

  • 6.3.4 Estimating Using the Regression Line  

    Note: The reading and lecture assigned below subunit 6.3 cover this topic. The regression line is expressed this way: Y = aX + b, where a is the slope and b is the intercept of the line.

  • 6.3.5 The Equation of the Regression Line  

    Note: The reading and lecture assigned below subunit 6.3 cover this topic. Make sure you know how to compute a and b given the equations in the text under the section labeled “The Equation of the Regression Line.”

  • 6.4 Spreadsheet Activity for Unit 6  
  • 6.5 Assessments for Unit 6  
  • Final Exam  

« Previous Unit Next Unit » Back to Top