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.

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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