Which forecasting method consider several variables that are related to the variable being predicted?

Video Transcript

L. Issuance we need to right here with forecasting method considers several variables that are variables being predicted, right? So we can write here multiple regulation is the answer for that. So multiple regression, if I write for this multiple recreation is a statistical bad knee. That can be used to analyze the relationship, the relationship between a single between a single dependent variable and several independent variables. So we can right here multiple regression. Multiple recreation is the method multiple recreation considers. Consider several variables, variables that are variables that are very well being variable. So we predicted if I right here for this predicted right, and if I right here for this multiple regression as this, our final answer and this is our final statement for this. Thank you.

Inhaltsverzeichnis Show

Video Transcript
Regression Analysis
Dependent and Independent Variables
Forecasting Example
Unpredictable Independent Variables
Associative Forecasting Methods: Regression and Correlation Analysis
Student Tip
Which method is used for prediction and forecasting?
What are the 4 forecasting techniques?
What are forecasting variables?
Which forecasting model is based upon salespersons estimates of expected sales?

When predicting a critical variable, such as the quantity of product that will be sold over a specific time span, the business manager can use various approaches. Methods that use cause and effect relationships are especially useful, as they help predict future trends and grasp underlying business dynamics. Thanks to affordable computers and software programs, such analysis is accessible to even the smallest business.

Regression Analysis

When intending to uncover cause and effect, statisticians rely on regression analysis. This mathematical model uses past data to quantify the relationships among variables. By understanding how one or several variables impact another variable, it is possible to understand the key drivers of a business, such as the factors affecting the number of walk-ins to a supermarket. Regression analysis also allows the statistician to run scenario analysis and produce “best,” “worst” and “likely case” predictions. Especially for the small-business owner who must constantly balance the cash flow, such scenarios help her prepare for potential hardship ahead of time.

Dependent and Independent Variables

To perform a regression analysis, the business owner or manager first identifies the dependent and independent variables. The dependent variable represents the figure the model will predict. The independent variables are those that impact the dependent variable. Therefore, the dependent variable is the effect or outcome, whereas the independent variables are the causes. A simple regression uses one dependent and one independent variable. A multiple regression model uses one dependent and multiple independent variables. A small-business owner won't have access to the same detailed data sets available to a statistician in a multinational company, which is why small-business owners tend to use simple regression models -- like the types that relate prices to sales levels.

Forecasting Example

The owner of an ice cream parlor intending to predict sales may start by building a regression model, where the sales level is the dependent variable and price and weather temperature are the independent variables. The resulting equation might look like this: Ice Cream Sales (in pounds) = 2.5(100/Price) + 0.7(Weather Temperature). This equation implies that the higher the price, the lower the sales, as the right side of the equation uses 100 divided by the price. So the higher the price, the lower the result of the ratio expressed by this equation. On the other hand, weather temperature has a positive influence on ice cream sales; higher temperatures elevate sales.

Unpredictable Independent Variables

A regression model is relatively useless for predictions if the independent variables are impossible to predict. If sales are dependent on the average price of competing products, for example, which is impossible to predict with accuracy, the regression model will not be useful as a predictive tool. This is especially important for the small-business owner who rarely has access to experts trying to forecast every detail for the next budgeting season. Therefore, the small-business owner must resist the temptation to use unpredictable independent variables. It's more useful to keep the equation simple and centered on publicly available data, such as economic growth and population forecasts.

Associative Forecasting Methods: Regression and Correlation Analysis

Student Tip

We now deal with the same mathematical model that we saw earlier, the least-squares method. But we use any potential “cause-and-effect” variable as x.

Unlike time-series forecasting, associative forecasting models usually consider several variables that are related to the quantity being predicted. Once these related variables have been found, a statistical model is built and used to forecast the item of interest. This approach is more powerful than the time-series methods that use only the historical values for the forecast variable.

Many factors can be considered ...

Which method is used for prediction and forecasting?

#1 Straight-line Method The straight-line method is one of the simplest and easy-to-follow forecasting methods. A financial analyst uses historical figures and trends to predict future revenue growth.

What are the 4 forecasting techniques?

Four common types of forecasting models.

Time series model..

Econometric model..

Judgmental forecasting model..

The Delphi method..

What are forecasting variables?

A deterministic forecast results in a specific forecast value of the variable which can then be compared with the corresponding observation. A probabilistic forecast of a continuous variable is usually in the form of a probability distribution of values, as might be obtained from an ensemble system.