Microsoft Office Tutorials and References
In Depth Information
Calculating linear regression
the projected sales for March of the following year by giving you the slope and
y-intercept (that is, the point where the line crosses the y-axis) of the line that best
its the sales data. By following the line forward in time, you can estimate future sales,
if you can safely assume that growth will remain linear.
Exponential regression Produces an exponential curve that best its a set of data
that you suspect does not change linearly with time. For example, a series of
measurements of population growth is nearly always better represented by an
exponential curve than by a line.
Multiple regression Is the analysis of more than one set of data, which often
produces a more realistic projection. You can perform both linear and exponential
multiple-regression analyses. For example, suppose you want to project the
appropriate price for a house in your area based on square footage, number of bathrooms, lot
size, and age. Using a multiple-regression formula, you can estimate a price by using
a database of information about existing houses.
Regressing into the future?
The concept of regression might sound strange because the term is usually associated
with movement backward, whereas in the world of statistics, regression is often used
to predict the future. Simply put, regression is a statistical technique that finds a
mathematical expression that best describes a set of data.
Often businesses try to predict the future using sales and percent-of-sales projections
that are based on history. A simple percent-of-sales technique identifies assets and
liabilities that vary along with sales, determines the proportion of each, and assigns them
percentages. Although using percent-of-sales forecasting is often sufficient for slow or
steady short-term growth, the technique loses accuracy as growth accelerates.
Regression analysis uses more sophisticated equations to analyze larger sets of data and
translates them into coordinates on a line or curve. In the not-so-distant past,
regression analysis was not widely used because of the large volume of calculations involved.
Since spreadsheet applications such as Excel began offering built-in regression
functions, the use of regression analysis has become more widespread.
Calculating linear regression
The equation y = mx + b algebraically describes a straight line for a set of data with one
independent variable, where x is the independent variable, y is the dependent variable,
m represents the slope of the line, and b represents the y-intercept. If a line represents a
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