Excel Statistics for Business Analytics
Explore, visualize, and make inferences about data using spreadsheets
Inferential Statistics involves inferring parameters of a population based on the values of a sample. Professionals in e-commerce, manufacturing, and other industries use inferential statistics as the basis for decision-making. Conducting their work in Excel, participants will take a hands-on approach to conducting statistical inference. By the end of this course, users will be able to organize, present, and draw valid conclusions from data, using inferential statistics for business impact.
What you'll learn-and how you can apply it
By the end of this live, hands-on, online course, you’ll understand:
- What variables are, and how to explore them given their type
- How the central limit theorem provides the “missing link” between descriptive and inferential statistics
- How statistics and visualizations each play a part in effective quantitative analysis
And you’ll be able to:
- Explore a dataset for potential research questions, check assumptions and build hypotheses
- Test formally whether the value of one group is greater than another, on average, given their respective samples
- Make compelling business recommendations using inferential statistics
This training course is for you because...
- You want to apply more rigorous methods to your business decision-making
- You’re an Excel user interested in learning more about data science
- You’re a researcher or analyst looking to apply statistical methods to business
- Intermediate Excel skills. You should be familiar and comfortable with relative and absolute cell references, PivotTables, and building bar and line charts.
- No previous statistical knowledge required.
- Read Chapters 1 “Evaluating Data in the Real World” and 2 “Understanding Excel’s Statistical Capabilities” in Statistical Analysis with Excel for Dummies, 4th edition (book)
- Load the Excel Data Analysis ToolPak on your computer. Follow Microsoft’s installation instructions.
About your instructor
George Mount is an independent analyst and data analytics educator helping clients manage their data so they think more creatively. He’s a technical expert and lead curriculum developer for Thinkful’s Data Analytics program and the instructor of the DataCamp course Survey and Measure Development in R. George blogs about data, innovation, and career development at Georgejmount.com. He holds a master’s degree in information systems with a certificate of achievement in quantitative methods from Case Western Reserve University.
The timeframes are only estimates and may vary according to how the class is progressing
Exploratory data analysis in Excel (50 minutes)
- Presentation: What is a variable and how do you measure it? Different types of variables, both quantitative and qualitative, and how they are used in business analytics.
- Presentation: Looking at a variable with visualizations. Using histograms and box plots to paint a picture of a variable’s distribution.
- Presentation: Listening to a variable with descriptive statistics. Using measures of central tendency and dispersion to explore the data statistically.
- Exercise: Identify and visualize variables in a real-world business dataset.
- Break (10 minutes)
Foundations of inferential statistics (60 minutes)
- Presentation: Introducing the Data Analysis ToolPak. Load and explore the free Office plug-in for various statistical analyses.
- Presentation: The central limit theorem -- saved by the bell curve. Demonstrate the central limit theorem’s role in providing valid inferences about a population, given a sample.
- Presentation: What is a hypothesis and how do you test it? Introduce the concept of hypothesis testing in statistical analysis and how to craft one.
- Presentation: What is a t-test and when do you use it? Introduce the use case for an independent samples t-test, along with how to check for the necessary assumptions and pre-process the data.
- Exercise: Inspect and prepare a dataset to test
- Break (10 minutes)
T-tests for business impact (50 minutes)
- Presentation: Evaluating for substantive and statistical significance. Analyze the p-value and confidence interval to make informed and well-rounded business decisions.
- Exercise: Conduct a t-test using the Analysis ToolPak.
- Presentation: Presenting the results for management buy-in. Prepare recommendations and visualizations to present before a general business audience
- Exercise: Visualizing a t-test’s results.