Excel 2007 Level 3: Data Analysis

IT@Emory computer education - Microsoft Office Training

Excel can help control your bottom line

Use statistical techniques and powerful tools in Excel to help you make informed, data-driven decisions. Whether you own a business or work within a large corporation, this class provides in-depth information that will maximize your use of the tools within Excel. Save time and money by getting the most out of Excel.

Prerequisite: Excelling at Excel or equivalent experience with Excel

Instructor: Jacob Ensign, Certified Technical Trainer
Note: new date and/or time
2 session(s): Mon and Tue: Dec 7-8 / 9:00 am-4:00 pm
Registration fee: $495   CEUs: 1.3      

After this class, you will be able to

• Install the Data Analysis ToolPak and use some of its advanced tools to study data
• Discuss and identify the three levels of data measurement
• Compute the appropriate measure of central tendency for a data variable
• Build a histogram using the Data Analysis ToolPak to present complex data in a concise chart
• Compute measures of distribution and determine whether a distribution is significantly non-normal
• Apply various hypothesis tests to data such as t-tests, z-tests, analysis of variance (ANOVA), and chi-square tests
• Perform regression analysis to correlate multiple data variables using a scatter plot and spreadsheet functions

What will be covered

Module 1. Data analysis ToolPak (30 minutes)


• Install the data analysis ToolPak in Excel 2007
  
• Use the descriptive statistics function on a list of numbers
  
• Discuss advantages and disadvantages of the ToolPak’s functions versus Excel’s worksheet functions

  Module 2.Data levels and measurements (30 minutes)
  

• Identify the differences between each level of data: interval, ordinal, and nominal

  Module 3. Categorize a given list of measurements according to level Measures of Central Tendency (45 minutes)
  

• Define the three measures of central tendency

  Module 4. Identify which measure can be used with which data levelMeasures of Dispersion (45 minutes)
  

• Define dispersion, and the two measures of dispersions: standard deviation and range
  
• Use dispersion to identify differences between datasets that share a central tendency

  Module 5. Frequency charts and histograms (60 minutes)
  

• Define frequency charts and histograms
  
• Build a histogram given a set of data
  
• Compare two distributions with different measures of dispersion

  Module 6. Compare two distributions that have different measures of central tendencySkewness and kurtosis (60 minutes)
  

• Define skew and kurtosis, and identify each: positively skewed, negatively skewed, not skewed, leptokurtic, platykurtic, and mesokurtic
  
• Compare distributions with different values of skewness and kurtosis
  
• Determine whether a set is significantly skew or kurtotic
  
• Describe a dataset using skewness and kurtosis; compare this description to the actual frequency chart
  
• Identify characteristics of a normal distribution

  Module 7. Capstone module: Measures of Distribution: visual and numerical representations (30 minutes)
  

• Identify characteristics of a frequency chart (visually represented)

  Module 8. Introduction to hypothesis testing (45 minutes)
  

• Construct testable hypotheses from casual statements
  
• Identify the six-step process to implementing a hypothesis test
  
• Determine whether a theory can be tested using hypothesis testing

  Module 9. Sampling (30 minutes)
  

• Define sampling and identify the two types: probability and non-probability samples
  
• Identify the advantages and disadvantages of convenience sampling and simple random sampling

  Module 10. T-test and z-test comparisons (45 minutes)
  

• Recite the assumptions of the t and z-tests
  
• Complete a hypothesis test using the t-test for population means
  
• Complete a hypothesis test using the z-test for population means

  Module 11. ANOVA (60 minutes)
  

• Recite the assumptions for all three variations of the ANOVA test
  
• Complete a hypothesis test using the ANOVA single factor test
  
• Complete a hypothesis test using the ANOVA two factor comparison without replication

  Module 12. Ordinal and Nominal Comparisons (60 minutes)
  

• Compare ordinal or nominal data with a multinomial chi-square test
  
• Construct a contingency table and use the chi-square test to compare frequency data
  
• Compare non-normal interval level data with a chi-square test

  Module 13. Regression analysis (60 minutes)
  

• Build a scatter plot chart for two variables and add a trendline
  
• Use the LINEST array function to compute all important linear model statistics
  
• Convert ordinal data into interval data by using dummy variables
  
• Create a matrix of correlation coefficients to consider building a multiple regression model
  
• Construct a multiple regression model

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