Business Statistics for Beginners

(BUS-STATS.AE1) / ISBN : 978-1-64459-461-2
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About This Course

Skills You’ll Get

Interactive Lessons

20+ Interactive Lessons | 55+ Exercises | 276+ Quizzes | 100+ Flashcards | 100+ Glossary of terms

1

Introduction

  • About This Course
  • Foolish Assumptions
  • Icons Used in This Course
  • Where to Go from Here
2

The Art and Science of Business Statistics

  • Representing the Key Properties of Data
  • Probability: The Foundation of All Statistical Analysis
  • Using Sampling Techniques and Sampling Distributions
  • Statistical Inference: Drawing Conclusions from Data
3

Pictures Tell the Story: Graphical Representations of Data

  • Analyzing the Distribution of Data by Class or Category
  • Histograms: Getting a Picture of Frequency Distributions
  • Checking Out Other Useful Graphs
4

Finding a Happy Medium: Identifying the Center of a Data Set

  • Looking at Methods for Finding the Mean
  • Getting to the Middle of Things: The Median of a Data Set
  • Comparing the Mean and Median
  • Discovering the Mode: The Most Frequently Repeated Element
5

Searching High and Low: Measuring Variation in a Data Set

  • Determining Variance and Standard Deviation
  • Finding the Relative Position of Data
  • Measuring Relative Variation
6

Measuring How Data Sets Are Related to Each Other

  • Understanding Covariance and Correlation
  • Interpreting the Correlation Coefficient
7

Probability Theory: Measuring the Likelihood of Events

  • Working with Sets
  • Betting on Uncertain Outcomes
  • Looking at Types of Probabilities
  • Following the Rules: Computing Probabilities
8

Probability Distributions and Random Variables

  • Defining the Role of the Random Variable
  • Assigning Probabilities to a Random Variable
  • Characterizing a Probability Distribution with Moments
9

The Binomial, Geometric, and Poisson Distributions

  • Looking at Two Possibilities with the Binomial Distribution
  • Determining the Probability of the Outcome That Occurs First: Geometric Distribution
  • Keeping the Time: The Poisson Distribution
10

The Uniform and Normal Distributions: So Many Possibilities!

  • Comparing Discrete and Continuous Distributions
  • Working with the Uniform Distribution
  • Understanding the Normal Distribution
11

Sampling Techniques and Distributions

  • Sampling Techniques: Choosing Data from a Population
  • Sampling Distributions
  • The Central Limit Theorem
12

Confidence Intervals and the Student’s t-Distribution

  • Almost Normal: The Student’s t-Distribution
13

Testing Hypotheses about the Population Mean

  • Applying the Key Steps in Hypothesis Testing for a Single Population Mean
14

Testing Hypotheses about Multiple Population Means

  • Getting to Know the F-Distribution
  • Using ANOVA to Test Hypotheses
15

Testing Hypotheses about the Population Mean

  • Staying Positive with the Chi-Square Distribution
  • Testing Hypotheses about the Population Variance
  • Practicing the Goodness of Fit Tests
  • Testing Hypotheses about the Equality of Two Population Variances
16

Simple Regression Analysis

  • The Fundamental Assumption: Variables Have a Linear Relationship
  • Defining the Population Regression Equation
  • Estimating the Population Regression Equation
  • Testing the Estimated Regression Equation
  • Using Statistical Software
  • Assumptions of Simple Linear Regression
17

Multiple Regression Analysis: Two or More Independent Variables

  • The Fundamental Assumption: Variables Have a Linear Relationship
  • Estimating a Multiple Regression Equation
  • Checking for Multicollinearity
18

Forecasting Techniques: Looking into the Future

  • Defining a Time Series
  • Modeling a Time Series with Regression Analysis
  • Forecasting a Time Series
  • Changing with the Seasons: Seasonal Variation
  • Implementing Smoothing Techniques
  • Comparing the Forecasts of Different Models
19

Ten Common Errors That Arise in Statistical Analysis

  • Designing Misleading Graphs
  • Drawing the Wrong Conclusion from a Confidence Interval
  • Misinterpreting the Results of a Hypothesis Test
  • Placing Too Much Confidence in the Coefficient of Determination (R2)
  • Assuming Normality
  • Thinking Correlation Implies Causality
  • Drawing Conclusions from a Regression Equation when the Data do not Follow the Assumptions
  • Including Correlated Variables in a Multiple Regression Equation
  • Placing Too Much Confidence in Forecasts
  • Using the Wrong Distribution
20

Ten Key Categories of Formulas for Business Statistics

  • Summary Measures of a Population or a Sample
  • Probability
  • Discrete Probability Distributions
  • Continuous Probability Distributions
  • Sampling Distributions
  • Confidence Intervals for the Population Mean
  • Testing Hypotheses about Population Means
  • Testing Hypotheses about Population Variances
  • Using Regression Analysis
  • Forecasting Techniques

The Art and Science of Business Statistics

  • Understanding the Daily Step Counts of Your Club Members
  • Keeping Track of Visitors on a Personal Blog
  • Assessing the Level of Student Participation in Various Extracurricular Activities
  • Conducting a Survey
  • Visualizing the Temperature Fluctuations
  • Visualizing Exam Grades Distribution

Pictures Tell the Story: Graphical Representations of Data

  • Calculating the Relative Frequency
  • Figuring the Class Width
  • Calculating the Cumulative Frequency
  • Illustrating a Cumulative Frequency
  • Illustrating a Relative Frequency
  • Illustrating a Frequency Distribution
  • Representing Fluctuations of Gold Price

Finding a Happy Medium: Identifying the Center of a Data Set

  • Calculating the Arithmetic Mean
  • Calculating the Weighted Geometric Mean
  • Calculating the Weighted Arithmetic Mean
  • Representing Positively Skewed Data Set
  • Representing Negatively Skewed Data Set
  • Representing Symmetrical Data Set
  • Discovering the Mode

Searching High and Low: Measuring Variation in a Data Set

  • Calculating Percentiles
  • Finding Quartiles
  • Finding Coefficient of Variation

Measuring How Data Sets Are Related to Each Other

  • Calculating the Sample Covariance

Probability Theory: Measuring the Likelihood of Events

  • Performing Set Operations
  • Looking at Types of Probabilities
  • Finding Unconditional Probabilities
  • Finding the Conditional Probability
  • Calculating the Multiplication Rule
  • Calculating the Complement Rule

Probability Distributions and Random Variables

  • Calculating the Probability Distribution
  • Calculating the Expected Value

The Binomial, Geometric, and Poisson Distributions

  • Calculating the Binomial Probability
  • Representing the Binomial Distribution
  • Calculating Geometric Probabilities
  • Computing Poisson Probabilities

The Uniform and Normal Distributions: So Many Possibilities!

  • Representing the Discrete Distribution
  • Uniform Distribution: Computing Variance and Standard Deviation
  • Calculating the Expected Value
  • Computing Uniform Probabilities with Formulas

Sampling Techniques and Distributions

  • Portraying Sampling Distributions Graphically
  • Calculating the Moments a Sampling Distribution
  • Converting Random Variable into a Standard Normal Random Variable

Confidence Intervals and the Student’s t-Distribution

  • Graphing the t-distribution
  • Calculating the Variance of a t-distribution

Testing Hypotheses about the Population Mean

  • Graphing the Standard Normal Distribution
  • Determining the Two-Tailed Hypothesis Test
  • Determining the Test Statistic

Testing Hypotheses about Multiple Population Means

  • Calculating the Error Sum of Squares (SSE)

Testing Hypotheses about the Population Mean

  • Testing Hypotheses about the Population Variance

Simple Regression Analysis

  • Calculating the Slope of a Line from Two Given Points
  • Calculating Coefficients and Predicting Sales Revenue in Simple Linear Regression
  • Calculating Total Sum of Squares (TSS)

Multiple Regression Analysis: Two or More Independent Variables

  • Visualizing the Test Statistics

Forecasting Techniques: Looking into the Future

  • Analyzing User Growth Trends

Business Statistics for Beginners

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