The field of **statistics** is concerned with collecting, analyzing, interpreting, and presenting data.

In the field of finance, statistics is important for the following reasons:

**Reason 1**: Descriptive statistics allow financial analysts to summarize data related to revenue, expenses, and profit for companies.

**Reason 2**: Regression models allow financial analysts to quantify the relationship between variables related to promotions, advertising, sales, and other variables.

**Reason 3**: Time series forecasting allows financial analysts to predict future revenue, expenses, new customers, sales, etc. for a variety of companies.

In the rest of this article, we elaborate on each of these reasons.

**Reason 1: Using Descriptive Statistics to Summarize Data**

Descriptive statistics are used to *describe* data.

Financial analysts often use descriptive statistics to summarize data related to the finances of companies.

For example, a financial analyst who works for a retail company may calculate the following descriptive statistics during one business quarter:

- Mean number of daily sales
- Median number of daily sales
- Standard deviation of daily sales
- Total revenue
- Total expenses
- Percentage change in new customers
- Percentage of products returned by customers

Using these metrics, the analyst can gain a strong understanding of the current financial state of the company and also compare these metrics to previous quarters to understand how the metrics are trending over time.

They can then use these metrics to inform the organization on areas that could use improvement to help the company increases revenue or reduce expenses.

**Reason 2: Using Regression Models to Quantify the Relationship Between Variables**

Another way that statistics is used in finance is in the form of regression models.

These are models that allow financial analysts to quantify the relationship between one or more predictor variables and a response variable.

For example, an analyst may have access to data on total money spent on TV advertising, online advertising, and total revenue generated.

They might then build the following multiple linear regression model:

Revenue = 76.4 + 4.2(online advertising) + 0.8(TV advertising)

Here’s how to interpret the regression coefficients in this model:

- For each additional dollar spent on online advertising, revenue increases by an average of $4.20 (assuming dollars spent on TV advertising is held constant).
- For each additional dollar spent on TV advertising, revenue increases by an average of $0.80 (assuming dollars spent on online advertising is held constant).

Using this model, a financial analyst can quickly understand that money spent on online advertising results in much higher average revenue compared to money spent on TV advertising.

**Reason 3: Using Time Series Forecasting to Predict Future Values**

Another way that statistics is used in finance is in the form of time series forecasting.

For example, a financial analyst may use historical data to forecast the total revenue, expenses, new customers, product sales, etc. for a company.

By forecasting these values, the analyst can inform the company on how many new customers to expect, how many new employees to hire based on increase revenue, and a variety of other metrics.

**Additional Resources**

The following articles explain the importance of statistics in other fields:

The Importance of Statistics in Research

The Importance of Statistics in Healthcare

The Importance of Statistics in Business

The Importance of Statistics in Economics

The Importance of Statistics in Education