Create a standard curve graphical representation using measurement data you've gathered from multiple samples to help determine its substance concentration. While Microsoft Excel does not provide a standard curve chart by default, you can manually create one using the chart tools for a scatter chart. The graph requires some minor changes, including removing the horizontal and vertical guidelines from the plot area.
Adding a linear trendline to your scatter chart will effectively create a normal curve line that represents the standard curve of your data.
Professor Wayne Winston has taught advanced forecasting techniques to Fortune 500 companies for more than twenty years. In this course, he shows how to use Excel's data-analysis tools—including charts, formulas, and functions—to create accurate and insightful forecasts. Learn how to display time-series data visually; make sure your forecasts are accurate, by computing for errors and bias; use trendlines to identify trends and outlier data; model growth; account for seasonality; and identify unknown variables, with multiple regression analysis. A series of practice challenges along the way helps you test your skills and compare your work to Wayne's solutions. Lynda.com is a PMI Registered Education Provider. This course qualifies for professional development units (PDUs).
To view the activity and PDU details for this course, click. The PMI Registered Education Provider logo is a registered mark of the Project Management Institute, Inc. Instructor. Wayne Winston is the professor of decision sciences at Indiana University's Kelly School of Business. He has won over 30 teaching awards, including the John and Esther Reese Professorship, and written over 20 journal articles and 15 books. His most recent book, Mathletics, explains the use of mathematics by professional sports teams and gamblers. Wayne has consulted for many organizations including the Dallas Mavericks, USA Diving, Cisco, Microsoft, US Army, Eli Lilly, Diamond Consulting, Tellabs, and Medtronics.
We were running Mac OS X Lion and the latest version of Office and had problems with Excel being horribly slow as well. I did some testing and noticed that Firstly the mac has a default set of fonts that you cannot and should not remove. These are found under Macintosh HD>System>Library>Fonts.
He has also developed online spreadsheet modeling and mathematics courses for Harvard Business School Publishing. Finally, Wayne is a two time Jeopardy! By: Wayne Winston course. 50m 38s. 15,412 viewers. Course Transcript - In this video we'll discuss an important measure of trendline performance, the standard error of the trendline, which is sometimes also called the 'standard error of the regression.' We'll also discuss the important concept of outliers.
An outlier is simply a point that doesn't fit the usual trendline pattern, and often you can learn a lot from your outliers. Let's open up the file standarderror.start and the chapter three video four folder. You'll see two worksheets: one involving the caucasian data, one involving the home run data. And we'll be using both here. There's a function in Excel to compute the standard error of a trendline or regression. It's called STEYX. And so if you start typing S-T-E-Y-X, then Excel prompts you for the syntax, which is the known y's, which is what you want to predict, and the known X's, which you want to use to make the predictions.
So the known Y's would be the percentage caucasians. Then you put a comma, and the known X's are the year. So the. Practice while you learn with exercise files. Watch this course anytime, anywhere. Course Contents. Introduction Introduction.
1. Visually Displaying Your Time-Series Data 1.
Visually Displaying Your Time-Series Data. 2.
How Good Are Your Forecasts? Errors, Accuracy, and Bias 2. How Good Are Your Forecasts? Errors, Accuracy, and Bias.
3. Using a Trendline for Forecasting 3. Using a Trendline for Forecasting. 4. Modeling Exponential Growth and Compound Annual Growth Rate (CAGR) 4.
Modeling Exponential Growth and Compound Annual Growth Rate (CAGR). 5. Seasonality and the Ratio-to-Moving-Average Method 5. Seasonality and the Ratio-to-Moving-Average Method.
6. Forecasting with Multiple Regressions 6.
Forecasting with Multiple Regressions. Conclusion Conclusion.