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In this Excel video tutorial, we gonna see How to use FORECAST Function in Excel to Predict a Future Value or Appraised Value for a House or Car or any other thing you like. Forecast.linear.

Basically, Microsoft Excel has 6 different forecast formulas, they are:

FORECAST.ETS, FORECAST.ETS.SEASONALITY, FORECAST, FORECAST.LINEAR, FORECAST.ETS.CONFINT and FORECAST.ETS.STAT.

The FORECAST function in Excel is a statistical function used to predict a future value based on existing values. It's particularly useful in scenarios where you have a series of data points and you want to forecast what a future data point might be based on the trend of the existing data.

FORECAST(x, known_y's, known_x's)

x: This is the value for which you want to forecast a new value.
known_y's: This is the array of dependent values (y-values) in your data series.
known_x's: This is the array of independent values (x-values) in your data series.

The function calculates the best-fit line through your data points using linear regression and then uses that line to predict the value of x. It's important to note that FORECAST assumes a linear relationship between x and y, which may not always be accurate for all datasets. If your data doesn't exhibit a linear trend, you might want to consider using other forecasting methods or functions.

Here's a simple example of how you might use the FORECAST function in Excel: Suppose you have a dataset of monthly sales figures (known_y's) and corresponding months (known_x's). If you want to predict the sales for the month of April (x), you could use the FORECAST function to do so. Here's how the formula might look:

=FORECAST(4, B2:B13, A2:A13)

In this example:

4 represents April (the month you want to predict).
B2:B13 is the range of sales figures (dependent values) for each month.
A2:A13 is the range of months (independent values).
Excel would then calculate the forecasted sales figure for April based on the existing data.

It's worth noting that while the FORECAST function can be useful for making predictions, it's always important to interpret the results with caution and consider factors such as the quality of your data, the assumptions underlying the linear regression model, and whether other forecasting methods might be more appropriate for your specific scenario.

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