Define regress series#

should we go with this promotion or a different one?).” According to the Environmental Systems Research Institute (ESRI): “Regressive analyses attempt to demonstrate the degree to which one or more variables potentially promote positive or negative change in another variable.” ( Image: adapted from )Īccording to, regression analysis (RA) by definition is: what will sales look like over the next six months?) or to decide what to do (e.g. why did customer service calls drop last month?) predict things about the future (e.g. “Most companies use regression analysis to explain a phenomenon they want to understand (e.g. In an article published in the Harvard Business Review in November 2015, – A Refresher on Regression Analysis – Amy Gallo wrote: You then plot all that information on a graph. Regression analysis – exampleįor example, if you think snow might impact sales, you will need snowfall data for the past three years. You also gather any data on the independent variables that you want to consider.

You collect all data on your monthly sales numbers for the past quarter, half year, year, or three years. There are also independent variables these are other factors which you believe may potentially have an impact on the dependent variable.įor your regression analysis, you have to gather all the information on the variables. Colleagues’ comments that snowfall has an impact on sales figures appears to be accurate. Each red dot represents one month’s worth of data, i.e., sales totals and how much it snowed that same month. This regression analysis chart relates to the situation I describe in this text, i.e., where you are a sales manager. In your case as the sales manager, the dependent variable is monthly sales. There is a dependent variable, i.e., the main factor that we are trying to predict or understand. It also helps us find out what their effects are on sales figures. Regression analysis helps us determine which factors really matter and their relationships. Others, on the other hand, may comment that sales take a nosedive about six weeks after a competitor’s promotion. They might say, for example, that when it snows the company sells more. Maybe work colleagues add their own variables to the mix. In fact, there may be hundreds of factors. For example, the time of year or rumors that a better model is coming out soon can impact the number. You know that there are dozens that can impact the number. Imagine you are a sales manager and you are trying to predict next month’s figures. You get a negative relationship when they move in opposite directions. A positive relationship is one where both the independent and dependent variables move together. We try to form a relationship between these two variables and draw a line. The independent variable (e.g., price) is on the x-axis.

The dependent variable (e.g., sales figures) is on the y-axis. For example, how the price of commodities relates to the shares of companies that deal in those commodities. It also helps them understand the relationships between different variables. Dependent receives the impact, while Independent provides (or not) the impact.įinancial and investment managers say that it helps them value assets. Independent variables are the factors that may or may not affect the dependent variable. Put simply, we want to know whether it is being affected, and if so, by how much, and by what. The dependent variable is the one that we focus on. We usually refer to them as independent variables.

Define regress series#

It tries to determine how strongly related one dependent variable is to a series of other changing variables. Regression analysis is a statistical measure that we use in investing, finance, sales, marketing, science, mathematics, etc. Regression analysis – a statistical measure Goodness of fit refers to how accurate expected values of a financial model are versus their actual values. Goodness of fit, for example, is a component of regression analysis. Furthermore, and most importantly, it helps us find out how certain we are about all the factors we are examining. It also helps us determine which factors interact with each other. In other words, regression analysis helps us determine which factors matter most and which we can ignore. We use it to determine which variables have an impact and how they relate to one another. Regression analysis, in statistical modeling, is a way of mathematically sorting out a series of variables.