Topic Name Description
Course Syllabus Course Syllabus
1.1: Why Do We Need to Study Statistical Analysis as Part of a Business Program? Why Do We Need to Study Statistical Analysis as Part of a Business Program?
Read this article about 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 Definitions of Statistics, Probability, and Key Terms

This article introduces how we 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.

Kinds of Data in Statistics

Watch the first video from 1:03:00 to the end, and the second video until 54:00. These videos explain the foundations of statistics, what data is, and the various types of data we'll be exploring.

1.3: Measures of Spread and Data Variance and Standard Deviation

These videos explain the difference between the variance of a population of a sample, how to estimate the variance of a population based on a sample, and how to find the standard deviation and why it is important.

Descriptive Statistics

Read this chapter, which discusses how to describe locations within a sample and how to analyze data from a sample. Be sure to attempt the practice problems and homework at the end of each section.

1.4: Spreadsheet Exercises: Measures of Central Tendency and Spread Measures of Central Tendency: Mode, Median, Mean, and Midrange

This section explores the measures of central tendency: mode, median, mean and midrange. It shows you the commands for computing these metrics using a spreadsheet program and it gives you the correct format for entering those commands into the spreadsheet program.

1.5: Spreadsheet Exercises: Graphs of Histograms and Frequency Tables Graphs and Charts

Shapes of Distributions

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.

Unit 1 Problem Set and Assessment Descriptive Statistics Homework

Attempt these practice problems and then check your answers. Note that not every part of these problems has an included solution.

2.1: Counting Terminology

Read this introduction to some of the most common formulas and terms in probability.

Counting and Probability

Watch this lecture, in which Professor Stark works through several examples of counting and probability.

2.2: Theories of Probability Independent and Mutually Exclusive Events

Read this section, which explains how to categorize the probability of an event based on what you know about the variables involved.

Venn Diagrams

This section explains some of the most common theories and formulas in probability, and demonstrates set theory by using Venn diagrams.

2.3: Set Theory Probability with Playing Cards and Venn Diagrams

Watch this video, which demonstrates how to use Venn Diagrams to understand probability.

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

Two Basic Rules of Probability

Read this section, which covers the symbols used in set theory. For example, the union of two sets A and B is denoted as A∩B. This section also discusses the basic rules of probability and set theory.

Set Theory

Watch these lectures, in which Professor Stark discusses set theory and works through a series of examples.

2.4: Probability Fundamentals Properties of Continuous Probability Density Functions

Read this section on the probability density function, which is the foundation for how we understand probability.

Probability Fundamentals

Watch these lectures, in which Professor Stark works through several examples of how to approach solving problems related to probability.

2.5: Probability Distributions and the Binomial Distribution Probability Density Functions and Random Variables

Watch these videos, which will introduce you to probability distributions and random variables.

Discrete Random Variables

Read this chapter, which covers the basic rules of probability and the ways that randomness affects how probabilities are distributed. Be sure to attempt the practice problems and homework at the end of the chapter.

Probability Distributions

Watch this lecture, in which Professor Stark works through several examples of probability distributions. The second video gives more information on how to find the mean (or expected value) of a discrete random variable.

Unit 2 Problem Set and Assessment Probability Homework

Discrete Random Variables Homework

3.1: The Normal Distribution Qualitative Sense of Normal Distributions

Watch this video, which gives an intuitive explanation of variables in real life that follow a normal distribution.

The Central Limit Theorem

Read this chapter, which covers some of the most important concepts used in statistics: the central limit theorem and the normal distribution. Be sure to attempt the practice problems and homework at the end of the chapter, which will give you a chance to check your understanding of these concepts.

3.2: Practice Problems Normal Distribution Problems: Z-score

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

More Empirical Rule and Z-score Practice

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

Unit 3 Problem Set and Assessment Practice: The Normal Distribution

4.1: Sampling and Sampling Distributions Sampling Distribution of the Sample Mean

Watch these videos, which explain the nature of sampling distribution and how it changes as sample size changes.

Calculating the Sample Size n: Continuous and Binary Random Variables

Read this section, which explains sampling in statistics and discusses some of the possible biases when collecting data for a sample. It also explains what to consider when dealing with continuous versus binary random variables.

Sampling and Sampling Distributions

Watch the first lecture from 1:19:00 to the end, in which Professor Stark covers some problems related to sampling and sampling distributions. Then, watch the second lecture to see some additional examples.

Unit 4 Problem Set and Assessment Sampling and Data Homework

5.1: Estimation and Confidence Intervals Confidence Intervals

Read this chapter, which discusses how to construct a confidence interval for a given population. Be sure to attempt the practice problems and homework at the end of the chapter.

Confidence Intervals and Estimating Parameters

Watch the first lecture from 1:05:00 to the end to learn more about confidence intervals and estimating parameters. Then watch the second lecture, which goes into more detail about confidence intervals and how to use them.

Computing Confidence Intervals

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.

5.2: Hypothesis Testing Hypothesis Testing and P-values

Watch this video, which explains how to test a hypothesis.

Hypothesis Testing with One Sample

This chapter builds on your knowledge of confidence intervals to introduce you to the concept of hypothesis testing, which is how statisticians use the scientific method to learn more about the populations they are studying. Attempt the practice problems and homework at the end of each section.

Hypothesis Testing

Watch the first lecture from 1:12:00 to the end. In it, Professor Stark covers problems related to hypothesis testing. Then, watch the second lecture, in which Professor Stark goes through additional problems related to hypothesis testing.

5.3: Testing Equality of Two Percentages Comparing Population Proportions

Watch these videos, which explain how to compare the proportion of two different samples.

Hypothesis Testing with Two Samples

Read this chapter, which discusses how to compare data from two similar groups. This is useful when, for example, you want to analyze things like how someone's income relates to another sample that you are interested in. Make sure you read the introduction as well as sections 10.1 through 10.6. Attempt the practice problems and homework at the end of the chapter.

Hypothesis Testing

Watch the first lecture from 1:12:00 to the end. It covers problems related to hypothesis testing. Then, watch the second lecture, which goes through additional problems related to hypothesis testing.

5.4: The Chi-Squared Test for Goodness of Fit Introduction to the Chi-Square Distribution

Watch these videos, which introduce chi-square tests and show when each kind of chi-squared test is used.

The Chi-Square Distribution

Read this chapter, which introduces you to the three major uses of the chi-squared distribution: the goodness-of-fit test, the test of independence, and the test of a single variance. Attempt the practice problems and homework at the end of the chapter.

Unit 5 Problem Set and Assessment Confidence Intervals Homework

6.1 Working with More Than One Variable Linear Regression and Correlation

Read this chapter to learn how to use graphs, such as scatterplots, 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.

Examples of Univariate and Multivariate Data

Watch the first lecture from 0:50:00 to the end. In it, Professor Stark differentiates between univariate and multivariate data. It also covers different data types and how to plot and interpret the correlation between data variables, and works through some examples. Then, watch the second lecture until 0:38:00, in which he works through some additional examples.

6.2: Correlation and Association The Correlation Coefficient
Review 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, $Σ$, as the summation symbol. For instance, $∑x_i=x_1+x_2+x_3$ when $i=1,2,3$.
6.3: Regression Linear Regression

Watch this lecture, which discusses how to interpret and understand a linear regression and how regression equations enable you to make predictions.

6.4: Spreadsheet Activity for Unit 6 Paired Data and Scatter Diagrams

Read this chapter, which discusses linear regressions and best fit lines.