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Even You Can Learn Statistics and Analytics

Master key statistical methods and analytics skills with hands-on practice training. 

(STATS-ANALYTICS.AB1) / ISBN : 978-1-64459-389-9
Lessons
Lab
TestPrep
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109 Rezension
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Über diesen Kurs

Fully updated to cover “Big Data” analytics and the latest applications, this beginner-friendly analytics course offers a practical introduction. 

You’ll master all the key statistical techniques for fields like finance, marketing, quality, science, social science, and more. This course also features gamified test preps and hands-on labs to make sure you grasp every concept.

With hands-on labs, you’ll get to polish your skills on the latest versions of Microsoft Excel. These practice labs are packed with real-world tasks and solutions that you can apply immediately in business or other settings. 

Fähigkeiten, die Sie erwerben werden

  • Master statistical tools to analyze massive datasets and generate actionable knowledge. 
  • Learn to build and interpret compelling statistical charts and tables using Excel or OpenOffice.org Calc 3. 
  • Explore fundamental statistical measures like mean, median, mode, standard deviation, Z scores, skewness, etc. 
  • Utilize probability and probability distributions. 
  • Use sampling distributions and confidence intervals to represent whole populations based on smaller samples. 
  • Test hypotheses with powerful techniques like Z, t, chi-square, and ANOVA.
  • Apply regression analysis and modeling to forecast trends based on past data. 
  • Employ multiple regression to build models that account for the influence of multiple variables. 
Kursübersicht herunterladen

1

Introduction

  • Mathematics Is Always Optional!
  • Learning with the Concept-Interpretation Approach
2

Fundamentals of Statistics

  • The First Three Words of Statistics
  • The Fourth and Fifth Words
  • The Branches of Statistics
  • Sources of Data
  • Sampling Concepts
  • Sample Selection Methods
  • One-Minute Summary
  • References
3

Presenting Data in Tables and Charts

  • Presenting Categorical Variables
  • Presenting Numerical Variables
  • “Bad” Charts
  • One-Minute Summary
  • References
4

Descriptive Statistics

  • Measures of Central Tendency
  • Measures of Position
  • Measures of Variation
  • Shape of Distributions
  • Important Equations
  • One-Minute Summary
  • References
5

Probability

  • Events
  • More Definitions
  • Some Rules of Probability
  • Assigning Probabilities
  • One-Minute Summary
  • References
6

Probability Distributions

  • Probability Distributions for Discrete Variables
  • The Binomial and Poisson Probability Distributions
  • Continuous Probability Distributions and the Normal Distribution
  • The Normal Probability Plot
  • Important Equations
  • One-Minute Summary
  • References
7

Sampling Distributions and Confidence Intervals

  • Foundational Concepts
  • Sampling Error and Confidence Intervals
  • Confidence Interval Estimate for the Mean Using the t Distribution (σ Unknown)
  • Confidence Interval Estimation for Categorical Variables
  • Confidence Interval Estimation When Normality Cannot Be Assumed
  • Important Equations
  • One-Minute Summary
  • References
8

Fundamentals of Hypothesis Testing

  • The Null and Alternative Hypotheses
  • Hypothesis Testing Issues
  • Decision-Making Risks
  • Performing Hypothesis Testing
  • Types of Hypothesis Tests
  • One-Minute Summary
  • References
9

Hypothesis Testing: Z and t Tests

  • Test for the Difference Between Two Proportions
  • Test for the Difference Between the Means of Two Independent Groups
  • The Paired t Test
  • Important Equations
  • One-Minute Summary
  • References
10

Hypothesis Testing: Chi-Square Tests and the One-Way Analysis of Variance (ANOVA)

  • Chi-Square Test for Two-Way Tables
  • One-Way Analysis of Variance (ANOVA): Testing fo...ferences Among the Means of More Than Two Groups
  • Important Equations
  • One-Minute Summary
  • References
11

Simple Linear Regression

  • Basics of Regression Analysis
  • Developing a Simple Linear Regression Model
  • Measures of Variation
  • Inferences About the Slope
  • Common Mistakes When Using Regression Analysis
  • Important Equations
  • One-Minute Summary
  • References
12

Multiple Regression

  • The Multiple Regression Model
  • Coefficient of Multiple Determination
  • The Overall F Test
  • Residual Analysis for the Multiple Regression Model
  • Inferences Concerning the Population Regression Coefficients
  • One-Minute Summary
  • References
13

Introduction to Analytics

  • Basic Concepts
  • Descriptive Analytics
  • Typical Descriptive Analytics Visualizations
  • One-Minute Summary
  • References
14

Predictive Analytics

  • Predictive Analytics Methods
  • More About Predictive Models
  • Tree Induction
  • Clustering
  • Association Analysis
  • One-Minute Summary
  • References
A

Appendix A: Microsoft Excel Operation and Configuration

  • Conventions for Keystroke and Mouse Operations
  • Microsoft Excel Technical Configuration
B

Appendix B: Review of Arithmetic and Algebra

  • Symbols
C

Appendix C: Statistical Tables

D

Appendix D: Spreadsheet Tips

  • Chart Tips
  • Function Tips
E

Appendix E: Advanced Techniques

  • Advanced How-To Tips
  • Analysis ToolPak Tips

1

Presenting Data in Tables and Charts

  • Creating a Bar Chart
  • Creating a Pie Chart
  • Creating a Pivot Table
  • Modifying and Refreshing the Pivot Table
  • Creating a Bar Chart
  • Creating a Line Chart
  • Creating a Scatter Plot
2

Descriptive Statistics

  • Calculating Mean
  • Calculating Median and Mode
  • Calculating First Quartile
3

Probability

  • Working with Probability
4

Probability Distributions

  • Calculating the Value of a Normal Random Variable
  • Using the Poisson Distribution
  • Computing Binomial Probabilities
5

Sampling Distributions and Confidence Intervals

  • Calculating the Confidence Interval
6

Hypothesis Testing: Z and t Tests

  • Analyzing the Hypothesis Test for the Pooled-Variance t-Test
7

Hypothesis Testing: Chi-Square Tests and the One-Way Analysis of Variance (ANOVA)

  • Analyzing the Hypothesis Test for the Chi-Square Test
8

Simple Linear Regression

  • Creating a Histogram
9

Multiple Regression

  • Using Multiple Regression
10

Introduction to Analytics

  • Creating a Sparkline

Haben Sie Fragen? Schauen Sie sich die FAQs an

Read more on our basic statistics and data analysis course.

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Big data analytics is the process of examining large columns of data to discover patterns, trends, and insights. It involves using advanced statistical techniques and machine learning algorithms to extract meaningful information from complex datasets.

Statistics and analytics for beginners are essential for making data-driven decisions in various fields such as business, finance, healthcare, and science.

By taking this course, you’ll gain hands-on experience in using statistical software and tools and pursue careers in data science, analytics, and related fields.

No, you don’t need to purchase any additional software. This simple analytics and statistics learning course contains simulations and non-production virtual machines that can be assessed through your browser. 

This course is ideal for beginners as no prior experience in related fields is needed.  This is our best course for learning statistics as it is designed for beginners and will start from the basics.

    Develop Advanced Data and Statistical Skills

      Get hands-on experience with big data analytics and statistical methods to solve real-world problems.

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