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Statistics: Regression Analysis Using Excel
Rating: 4.5 out of 5(47 ratings)
897 students

Statistics: Regression Analysis Using Excel

Quantifying relationships within data using Excel
Created byStephen Peplow
Last updated 7/2024
English
English [Auto],

What you'll learn

  • Use Excel to quantify relationships within data
  • Use data to predict quantifiable outcomes
  • Incorporate seasonality into predictions
  • Measure the strength of the relationship between one or more independent and one dependent variable

Course content

12 sections • 13 lectures • 1h 50m total length

  • Why do regression anyway?4:06

    Learn how regression links a dependent variable to multiple independent variables using scatterplots and trend lines to predict outcomes, with dummy variables for categorical information and time-series seasonality in Excel.

  • Measuring the degree of correlation between two variables4:37

    Learn how correlation measures association between variables and underpins regression, using the Pearson product moment r from -1 to 1. See how Excel's correlation function reveals relationship types.

  • Correlation Quiz

Requirements

  • Basic familiarity with Excel
  • This is an introductory course, similar to a first year uni course. I take it step by step.

Description

Regression is an important statistical tool. Using regression, we can detect and quantify relationships within a data set. For example, you have a data set of truck distances driven and stops made. Using this information, we can construct an equation which allows prediction of duration given distance number of stops. This would be of great use to anyone having to give out quotations.

I show you how to check whether your regression actually works and how accurate it is.

Indicator or 'dummy' variables are an important source of information, and I show you how to convert textual data into dummy variables for inclusion in the regression analysis. We know how long repair jobs take and months since last service. Does including information about whether the job was electrical or mechanical make predicted repair time any more accurate?

Sales go up at certain seasons: being able to measure those increases and predict them is highly useful.

We also cover elasticity, a topic often missed out in regression courses. Using elasticity, we can predict the effect on sales volume in precent of a percent change in selling price.

I provide detailed explanations and provide the datasets so that you can follow along.

The pace of the course is measured and step by step, each section building on the last. The datasets I use in the examples are included so that you can run your own regressions and compare results.

Who this course is for:

  • Beginning statisticians

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