In financial modeling and accounting, we have dependent variables and independent variables. Dependent variables are those which vary when influenced by other factors, while independent variables are those that are unaffected by changes in other variables.
Knowing the relationship between independent variables and dependent variables can help you plan ahead and identify potential weaknesses in your business model.
Independent variable definition
In accounting, the independent variable is an attribute that can be changed in order to measure the impact on a dependent variable. Essentially, the independent variable is an input while the dependent variable is the output. We can use independent variables to assess performance, sales, expenses, profitability and more.
When it comes to sales, the independent variable might be something like customer demographics. For example, it could be how many stores you have and where they’re located, or it might be how many salespeople you have in your team. If changing any of these factors would impact your sales and profits, then these are independent variables. It could even be something like weather. If you have a physical store, perhaps you find you make more sales when the weather is good than on a rainy day.
The state of the economy itself could be considered an independent variable. During a recession, for example, people may be earning less which in turn means they’re spending less, which could impact your bottom line. In terms of expenses, things like rental rates, costs for raw materials, or labor wage rates can be considered independent variables.
How to identify independent variables
Almost any factor might be an independent variable depending on the dependent variable you’re measuring. When it comes to identifying independent variables, you might need to do some hypothesizing. You can probably predict the most influential factors that would impact the dependent variable just by understanding your product, service, market and industry.
To put it in perspective – let’s imagine the price of a house is the dependent variable. There are a number of attributes that affect the price, like location, number of rooms, nearby amenities, age of the building, property size, the economy and property market itself, and so on. There might be other factors, like the color of the walls, that can impact price, but you should be able to predict which independent variables would have the strongest relationship with the dependent variable.
If you were measuring how different independent variables affect customer satisfaction for a product you sell, you might choose to conduct research and survey your customers on their experience. Some may say ease of use is most important, others may look for quality, and some may be focused on design. By conducting customer research, you can identify which factors are most important, and then quantify how these factors influence customer satisfaction.
A root-cause analysis is another useful way to identify independent variables.
How to use independent variables
A sensitivity analysis can be used to measure how sensitive a dependent variable is to changes in an independent variable. Regression analysis helps you identify trends and correlations between variables.
This will be useful as you plan your budget for the year. You might opt to make a scatter graph and measure changes in independent variables against their effect on dependent variables. The dependent variable, such as maintenance costs for example, would be the y-axis of the graph while the independent variable, such as number of products manufactured, would form the x-axis. You can then use this scatter graph to quantifiably determine regression.
The regression value can then be used to forecast how dependent values may change in the future based on changes in independent variables.
For example, the number of employees you have will impact how much you spend on wages, which in turn will impact your revenue. If you reduced your number of employees and kept everything else the same, your revenue will be higher, and if you increase your number of employees and keep everything the same, your revenue will be lower. You can use regression to predict how your revenue will look in five years’ time if your team were to grow by a certain amount each year.
The same could go for pricing. Increasing the price of your product, an independent variable, might decrease sales, while lowering the price might increase sales. Less sales doesn’t necessarily mean less revenue, as you’ve increased the earnings from each sale – which is why regression is useful as it measures and predicts the true relationship between dependent variables and independent variables.
You can use it to see which independent variables require the most attention. Going back to our customer satisfaction example, you might find that design has a great impact on satisfaction than you’d assumed, and you may realize functionality is less important than predicted – so understanding independent variables can help a business stay focused on what counts.
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