Complete Guide to Excel 365: What-If Analysis, Solver, & Analysis ToolPak

Excel Office 365    |    Expert
  • 12 Videos | 1h 40m 51s
  • Includes Assessment
  • Earns a Badge
Excel's Scenario Manager allows you to create named scenarios with different values for key variables. These scenarios can be quickly examined and summarized using the What-If Analysis tool. In this course, you'll use GoalSeek and Solver, both of which are mathematical tools. Goal Seek comes in handy when solving quadratic, cubic, or other equations. Solver is much more powerful and allows complex constrained optimization problems to be easily defined and addressed. As you'll recognize, Solver's interface, used for specifying objective functions and constraints, is intuitive and easy to use. Next, you'll use several heavy-duty statistical techniques in Analysis ToolPak. These range from the creation of correlation and covariance matrices, hypothesis testing, and F-test and T-test interpretations to ANOVA, random and periodic sampling, and the construction and analysis of linear regression models.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    Use the What-If Analysis feature in Excel, create scenarios based on specific cell values, assign names to these scenarios, and toggle between them, summarize a range of outcomes across scenarios by performing What-If Analysis, and interpret the returned results  
    use Goal Seek for solving simple univariate optimization problems, such as solving univariate equations where a target output cell must be set to a specific value by changing a single input cell
    enable the Solver add-in and use it to perform complex multi-variable optimization, specify objective functions to min/max or specific values, and specify non-negativity conditions and solution methods for contraints
    perform simple statistical analysis of two-dimensional data, compute measures of central tendency (mean, median, mode) and dispersion (standard deviation and variance), define Bessel's correction, and calculate skew and kurtosis  
    enable Analysis ToolPak and use it to compute correlation and covariance matrices, interpret the results, and recognize the link between covariance and variance
  • implement hypothesis testing using the Analysis ToolPak, perform the two-sample F-test for variance equality and two-sample t-test for equality of means, and interpret the significance level (alpha), test statistic, and p-value
    implement ANOVA (Analysis of Variance) using Analysis ToolPak to analyze variances within and between groups  
    use Analysis ToolPak for histogram analysis and descriptive statistic computing, compute ranks and percentiles, and perform both random and periodic sampling  
    generate random numbers drawn from various distributions using Analysis ToolPak, apply normal, Bernoulli, Poisson, and other distributions, specify population parameters, such as mean and variance, and recognize why sample mean and variance might differ from them  
    perform linear regression using Analysis ToolPak, interpret the results of regression including R-square, p-values, and t-statistics of individual regression coefficients, and identify the benefits of using Analysis ToolPak over worksheet functions such as LINEST(),SLOPE(), and INTERCEPT()  
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 20s
    UP NEXT
  • Playable
    2. 
    Implementing What-If Analysis
    11m 23s
  • Locked
    3. 
    Using Goal Seek
    7m 17s
  • Locked
    4. 
    Using Solver for Complex Optimization
    7m 44s
  • Locked
    5. 
    Calculating Simple Descriptive Statistics
    11m 34s
  • Locked
    6. 
    Analysis ToolPak: Analyzing Matrices
    10m
  • Locked
    7. 
    Using Analysis ToolPak for Hypothesis Testing
    11m 36s
  • Locked
    8. 
    Implementing ANOVA Using Analysis ToolPak
    7m 50s
  • Locked
    9. 
    Analysis ToolPak: Analyzing Data Characteristics
    9m 28s
  • Locked
    10. 
    Generating Random Numbers
    5m 7s
  • Locked
    11. 
    Performing Linear Regression
    9m 42s
  • Locked
    12. 
    Course Summary
    1m 48s

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