## Topic outline

- General
- Course Introduction
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.

- 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.

**Completing this unit should take you approximately 16 hours.** - 1.1: Why Do We Need to Study Statistical Analysis as Part of a Business Program?
- Read this brief essay for an overview of the different ways that statistics are used in business and why it is essential that decision-makers have the tools to analyze data as part of their skill set.

- 1.2: Measuring Data
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 the author's instructions in*SticiGui:*"How to Use These Materials”.Watch this video from 1:03:00 to the end. This video is a companion lecture to Chapter 3, with the author of the text working through the materials.

Watch this video from the beginning until 0:54:00. This video is a companion lecture to Chapter 3, with the author of the text working through the materials.

- 1.3: Measures of Spread and Data
Read this chapter, which will show you all the necessary formulas for computing descriptive statistics for analyzing data from a sample such as the mean, median, mode, variance, range, and standard deviation. 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.Watch this video from 0:54:00 to the end. This video is a companion lecture to Chapter 4, with the author of the text working through the materials.

Watch this video from the beginning until 0:50:00. This video is a companion lecture to Chapter 4, with the author of the text working through the materials.

- This video shows the difference between the variance of a population and that of a sample.
- This video explains how to estimate the variance of a population using the variance of a sample.
This video provides a review for what you have learned so far as well as an introduction to standard deviation.

- 1.4: Spreadsheet Exercises for Unit 1
- 1.4.1: Measures of Middle and Spread
Read this section.

Read this section.

These spreadsheet files (in both Excel and Open Office formats) include a tab titled "Starter File,” which contains everything you need to get started on the activity. Once you have worked through the activity, you can click on the "Solution File” tab to see how your finished spreadsheet should look.

- 1.4.2: Histograms and Frequency Tables
Read this section.

Read this section.

Read this section. Note that the instructions given in the text are for version 2.x of OpenOffice. If you have 3.x, some steps are slightly different; you may need to consult the help documents.

These spreadsheet files (in both Excel and Open Office formats) include a tab titled "Starter File,” which contains everything you need to get started on the activity. Once you have worked through the activity, you can click on the "Solution File” tab to see how your finished spreadsheet should look.

- Unit 1 Problem Set and Assessment
For this assessment, please do the following problems: 3, 5, 17, 24-30. Note that not all problems have solutions attached, so please limit your choices to those that have solutions. To see a problem solution, click the "Show Solution” link below the problem.

- 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.

**Completing this unit should take you approximately 42 hours.** - 2.1: Counting
Read Chapter 12 to study several of the most common formulas in probability. 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 onSticiGui Exercises and Java Applets” in the "Course Information” section above.Watch this lecture, which is the companion to Chapter 12 with the author of the text working through the materials.

- 2.2: Theories of Probability
Read Chapter 13 for an explanation of the most common theories and formulas in probability. 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.Watch this lecture, which is the companion to Chapter 13 with the author of the text working through the materials.

Read this section to learn about the intuition behind ways to compute probabilities.

- 2.3: Set Theory
Watch this video, which provides demonstrations on how to use Venn Diagrams to understand probability.

Watch this video, which discusses Venn diagrams and the addition rule for probability.

Read Chapter 14 to study the symbols used in set theory such as for denoting the union of two sets A and B to be A∩B. 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.Watch this lecture, which is the companion to Chapter 14 with the author of the text working through the materials.

Watch this lecture, which is the companion to Chapter 14 with the author of the text working through the materials.

Chapter 15 is an optional reading as it shows you advanced rules when dealing with sets. Read through this chapter if you would like to experience the entire course as it was presented at University of California, Berkeley. 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.Chapter 16 is an optional reading as it shows you advanced rules when dealing with sets. Read through this chapter if you would like to experience the entire course as it was presented at University of California, Berkeley. 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.

- 2.4: Probability Fundamentals
Read Chapter 17 to learn the basic rules in probability such as how to find the probability of two dependent events. 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.Watch this lecture, which is the companion to Chapter 17 with the author of the text working through the materials.

Watch this lecture, which is the companion to Chapter 17 with the author of the text working through the materials.

Watch this lecture, which is the companion to Chapter 17 with the author of the text working through the materials.

Chapter 18 is an optional reading as it gives you an example of applying probability theory. Read through this chapter if you would like to experience the entire course as it was presented at University of California, Berkeley. 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 onSticiGui Exercises and Java Applets” in the "Course Information” section above

- 2.5: Probability Distributions and the Binomial Distribution
Read Chapter 19, which unites probability with samples and explains the nature of a probability distribution. 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.Watch this lecture, which is the companion to Chapter 19 with the author of the text working through the materials.

Watch this video, which will introduce you to random variables and probability distribution functions.

Watch this video, which will introduce you to probability distributions.

This is an optional reading. Chapter 20 will provide information on random variables and discrete distributions. Read through this chapter if you would like to experience the entire course as it was presented at University of California, Berkeley. 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.Watch this lecture, which is the companion to Chapter 20 with the author of the text working through the materials.

- 2.6: The Long Run and Expected Value
Read Chapter 21, which will give you several theorems when dealing with sample data such as the Law of Large Numbers. 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.Watch this lecture, which is the companion to Chapter 21 with the author of the text working through the materials.

This is an optional reading. Read Chapter 22 to learn about standard error. Read through this chapter if you would like to experience the entire course as it was presented at University of California, Berkeley. 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.Watch this lecture, which is the companion to Chapter 22 with the author of the text working through the materials.

- Unit 2 Problem Set and Assessment
For this assessment, please select ten of the problems from this set and solve them. Note that not all problems have solutions attached, so please limit your choices to those that have solutions. To see a problem solution, click the "Show Solution” link below the problem.

For this assessment, please do problems 1, 3, 5, 7, 11, 15, 17, and 23. To see a problem solution, click on the "Show Solution” link below the problem.

For this assessment, please do all the problems. To see a problem solution, click on the "Show Solution” link below the problem.

- 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.

**Completing this unit should take you approximately 14 hours.** - 3.1: The Normal Distribution
Watch this video, which gives an intuitive explanation of variables in real life that follow a normal distribution.

Read Chapter 23 for an explanation of one of the most important probability distributions used in statistics: The Normal Distribution. 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.Watch this video up to 1:19:00. This video is the companion to Chapter 23, with the author of the text working through the materials.

Read this article to learn how the normal distribution is graphed and how probabilities under the curve are computed from location X to Y on the curve using the table with probabilities for the normal distribution.

Read this section, as it shows how the normal distribution is graphed and how probabilities under the curve are computed using Excel.

Watch this video, which will walk you through the process of finding the z-scores, an important part of understanding this topic.

Watch this video, will walk you through the process of solving problems using the standard normal distribution, an important part of understanding this topic.

- 3.2: Spreadsheet Activity for Unit 3
For this activity, work through the use of a spreadsheet to solve normal distribution probability problems. Be sure you understand how to use the "normdist” and "norminv” functions. The supporting spreadsheet files (links above to both Excel and Open Office versions) include a tab entitled "Starter File,” which contains everything you need to get started on the activity. Once you have worked through the activity, you can click on the "Solution File” tab to see how your finished spreadsheet should look.

- Unit 3 Problem Set and Assessment
Complete all five exercises. You can check your work by clicking "Show Solution”.

- 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.

**Completing this unit should take you approximately 8 hours.** - 4.1: Sampling and Sampling Distributions
Read Chapter 24 for an explanation of what sampling means in statistics and its possible biases when collecting data for a sample. 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.Watch this lecture from 1:19:00 to the end, which is the companion to Chapter 24 with the author of the text working through the materials.

Watch this lecture, which is the companion to Chapter 24 with the author of the text working through the materials.

Watch this video, which explains the nature of sampling distribution and how it changes as sample size changes.

Watch this video, which explains the nature of sampling distribution and how it changes as sample size changes.

- Unit 4 Problem Set and Assessment
For this assessment, please work on problems 1, 3, 5, and 19-29. To see a problem solution, click on the "Show Solution” link below the problem.

- 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 counter-intuitive, 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.

**Completing this unit should take you approximately 33 hours.** - 5.1: Estimation
Read Chapter 25 to learn how to generalize for an entire population given sample data only and its statistics. 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.Watch this video up to 1:05:00. This video is the companion to Chapter 25, with the author of the text working through the materials.

- 5.2: Confidence Intervals
Read Chapter 26 to learn how to compute confidence intervals for the real population mean with the use of the "t-table” with probabilities. 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.Watch this video from 1:05:00 to the end. This video is the companion to Chapter 26, with the author of the text working through the materials.

Watch this video up to 1:12:00. This video is the companion to Chapter 26, with the author of the text working through the materials.

Read Chapter 30, which will show you the difference in hypothesis testing between the z and t tests. 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.

Read this section to learn how to compute confidence intervals for finding a range for the real population parameter using statistics from the sample data.

- 5.3: Hypothesis Testing: t-Tests
Read Chapter 27, which it introduces you to the concepts in hypothesis testing to test values of a true population parameter given sample statistics or to test and compare the statistics from two or more similar samples. Pay special attention to the following terms: null hypothesis, alternative hypothesis, significance level, power of a test, and p-values. These terms are widely used whenever a sample with data is analyzed. 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.Watch this video from 1:12:00 to the end. This video is the companion to Chapter 27, with the author of the text working through the materials.

Watch this video. This video is the companion to Chapter 27, with the author of the text working through the materials.

- Read this section to learn how to conduct hypothesis testing for a population mean and a population proportion using one sample.
Watch this video, which explains how to test a hypothesis.

Read this section to learn how to conduct hypothesis testing for a population mean and a population proportion using two samples.

- 5.4: Testing Equality of Two Percentages
Read Chapter 29 to learn how to compare data as a percent from two similar groups when you are analyzing categorical data. For example, suppose that you are analyzing the percent of low income women versus the percent of high income women that belong to one sample. Then, you want to compare those percentages to a different but similar sample of women. 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.Watch this lecture, which explains how to compare the proportion of two different samples.

Watch this lecture, which explains how to compare the proportion of two different samples.

- 5.5: The Multinomial Distribution and the Chi-Squared Test for Goodness of Fit
Read Chapter 31 to learn how to compare data as a percent from three or more similar groups when you are analyzing categorical data. 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.Watch this video, which explains how to conduct Chi-Square tests.

Watch this video, which explains how to conduct Chi-Square tests.

Watch this video, which explains how to conduct Chi-Square tests.

- Unit 5 Problem Set and Assessment
Select ten of the problems from this exercise to complete. Note that not all problems have solutions attached, so please limit your choices to those that have solutions. To see a problem solution, click on the "Show Solution” link below the problem.

Select ten of the problems from this exercise to complete. Note that not all problems have solutions attached, so please limit your choices to those that have solutions. To see a problem solution, click on the "Show Solution” link below the problem.

- 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.

**Completing this unit should take you approximately 20 hours.** - 6.1 Working with More Than One Variable
Read this chapter 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.Watch this video from 0:50:00 to the end. This video complements Chapter 5, with the author of the text working through the materials.

Watch this video until 0:38:00. This video complements Chapter 5, with the author of the text working through the materials.

- 6.2: Correlation and Association
Read this chapter for a discussion on the difference between correlation and association between two variables. When two variables are said to be highly correlated, it does not mean that one is causing the other. Pay special attention to the formula for computing the correlation value. 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.Watch this lecture from 0:41:00 to the end. This video is the companion to Chapter 7, with the author of the text working through the materials.

Read this section, which describes the formula for computing the correlation coefficient. It may be useful to save this resource for future reference to this formula. Note that the formula uses the Greek letter sigma, \( \Sigma \), as the summation symbol. For instance, \( \sum x_i=x_1+x_2+x_3 \) when \( i=1,2,3 \).

- 6.3: Regression
Read this chapter 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.Watch this lecture from 0:03:50 to the 0:57:50. This video is the companion to Chapter 9, with the author of the text working through the materials.

This is an optional reading. Read Chapter 11 on errors in regression. Read through this chapter if you would like to experience the entire course as it was presented at University of California, Berkeley. 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 video is optional. Watch it from 0:57:50 to the end. This video lecture complements Chapter 11, with the author of the text working through the materials.

This video is optional. Watch it until 0:47:00. This video lecture complements Chapter 11, with the author of the text working through the materials.

- 6.4: Spreadsheet Activity for Unit 6
Read this chapter, which discusses linear regressions and best fit lines.

For this activity, you will review how a spreadsheet can be used to plot data, determine the slope and intercept of regression line, and draw the regression line. The instructions for creating the scatter graph and regression line are in Section 4.25. However, for this activity, we are solving for the problem presented in Section 4.4. The supporting spreadsheet files (links above to both Excel and Open Office versions) include a tab titled "Starter File,” which contains everything you need to get started on the activity. Once you have worked through the activity, you can click on the "Solution File” tab to see how your finished spreadsheet should look.

- Unit 6 Problem Set and Assessment
For this assessment, please work on the following problems: 1, 3, 5, 11, 15, 17, and 22. To see a problem solution, click on the "Show Solution” link below the problem.