Question

Differentiate Linear Regression from Moving Average forecasting method and give an example of each.

Answer 1

Linear Regression

Regression Analysis is a forecasting method. It includes a large group of methods that can be used to predict future values of a variable using information about other variables. These methods include both linear or non-linear techniques.

Linear regression is a statistical tool used to predict future values from past values.

The linear regression equation has the form Y= a + bX, where Y is the dependent variable (Y axis), X is the independent variable (X axis), b is the slope of the line and a is the y-intercept

It can be highly beneficial for companies to develop a forecast of the future values of some important metrics, such as demand of the product or variables that describe the economic climate. ... Linear regression forecasting uses basic statistics to project future values for a target variable.

Example

Show below are some data of a clothing company X. Each row in the table shows Xs sales for a year and the amount spent on advertising that year. In this case, our outcome of interest is sales—it is what we want to predict. If we use advertising as the predictor variable,

linear regression estimates that Sales = 168 + 23 Advertising. That is, if advertising expenditure is increased by one million Euro, then sales is expected to increase by 23 million Euros, and if there was no advertising we would expect sales of 168 million Euros.

Moving Average

An average generally represents the “middle” value of a set of numbers. The moving average is exactly the same, but the average is calculated several times for several subsets of data. Moving averages are a smoothing technique that looks at the underlying pattern of a set of data to establish an estimate of future values.

  Moving average is a technique to get a overall idea of the trends in a data. It is an average of any subset of numbers.Moving average is extremely useful for forecasting long-term trends.