91Ó°ÊÓ

The weighted moving average is an indicator that is accustomed generates trade directions and making a buy or sells decision. It assigns greater weighting to recent data and less weighting to past data. The weighted moving average is calculated by multiplying each observation within the information set by a predetermined weighting factor.

By using a weighted moving average with weights of 0.60, 0.30, and 0.10, the July forecast is as follows:

F (July) = .60(15) + .30(16) + .10(12)

= 15.0

Amoving average could be a calculation to investigate data points by creating a series of averages of various subsets of the complete data set. It's also called a moving mean.

By using a simple three-month moving average, the July forecast is as follows:

J_uly = (15 + 16 + 12) / 3 = 14.3

Single exponential smoothing is the most employed in all forecasting techniques. It’s an integral part of all electronic forecasting programs, and it is widely employed in ordering inventory in retail businesses, manufacturing units, and repair agencies.

Using single exponential smoothing witha= 0.2 and a June forecast =13, the July forecast is:

F_(July) = F_{(June) +}a(A_(June) – F_(June)

F_(July) = 13 + 0.2(15-13)

F_(July) = 13.4

July forecast = 13.4

Simple linear regression could be a tool of statistics accustomed help predict future values from past values. It’s commonly used as a quantitative way to find the underlying trend and when prices are overextended.

$$\begin{gathered} \stackrel{¯}{\text{x}}=3.5 \\ \stackrel{¯}{\text{y}}=13.5 \\ \text{a}=\frac{∑\text{y}-\text{b}∑\text{x}}{\text{n}}=\stackrel{¯}{\text{y}}-\text{b}\stackrel{¯}{\text{x}}=10.8 \\ \text{b}=\frac{∑\text{xy}-\text{n}\stackrel{¯}{\text{xy}}}{∑\text{x}-\text{n}\stackrel{¯}{\text{x}}}=\frac{297-47.25*6}{91-12.25*6}=0.77 \\ \text{so},\text{y}=10.8+0.77\text{x} \end{gathered}$$

Theregression equation is the algebraic expression of the regression lines. It's accustomed predict the values of the variable quantity from the given values of independent variables.

By using regression equation y = 10.8+0.77x, the forecast for July is explained as below-

F_(July) = a+bx

F_(July) =10.8+0.77*7

F_(July) =16.19

The forecast for july is 16.19