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

**Econometrics **is simply the application of statistical methods to topics in economics.

For example, a student who takes an introductory statistics course may learn about the following topics:

- How to calculate descriptive statistics
- How to visualize data
- How to construct confidence intervals
- How to perform hypothesis tests
- How to fit regression models
- How to fit ANOVA models

A student who then takes an econometrics course would learn how to apply each of these statistical methods to answer research questions related to the economy.

If a student wants to become an econometrician, they must first learn about the concepts taught in an introductory statistics course.

They can then take an econometrics course to learn how to apply statistical methods to specific research questions in the field of economics.

**Common Statistical Methods Used in Econometrics**

The field of econometrics uses many statistical methods.

The following examples illustrate some methods that are commonly used.

**Example 1: Descriptive Statistics**

Econometricians frequently use descriptive statistics to summarize the current state of an economy in a particular area.

For example, an econometrician might collect the following data about individuals in a particular city:

- Population size: 85,000
- Mean household income: $71,200
- Median household income: $56,400
- Standard deviation of household income: $12,200

Using these descriptive statistics, the econometrician can gain a solid understanding of the income distribution in this city.

The econometrician could also compare these values to other cities or even compare these values to the same city during a different time period.

In practice, econometricians use descriptive statistics all the time to gain a better understanding of the economic standing in different towns, cities, states, and countries.

**Example 2: Regression Models**

Econometricians often use multiple regression models to understand how various factors affect certain response variables.

For example, an econometrician who studies houses might fit the following regression model:

**Response variable**:

- House price

**Predictor variables**:

- Square footage
- Number of bedrooms
- Number of bathrooms
- Yard size

They can then use this regression model to understand exactly how the various predictor variables affect the response variable.

For example, they might find that for each additional one square foot increase in house size (holding all other variables constant) the house price increases by an average of $150.

Or they may find that for each additional bathroom (holding all other variables constant) the house price increases by an average of $8,500.

They can also use this regression model to predict the selling house of a price based on the values of the predictor variables in the model.

**Example 3: Time Series Forecasting**

Econometricians often use time series analysis to forecast the state of the economy for a given county, city, state, or country at some point in the future.

For example, an econometrician may use historical data to predict the GDP, unemployment rate, interest rate, or some other metric for a given country at some point in the future.

**Related:** How to Plot a Time Series in R (With Examples)

**Conclusion**

In conclusion:

The field of **statistics** encompasses a wide variety of methods that can be used with many different types of data.

The field of **econometrics **is simply the application of these statistical methods to various topics in economics.

**Additional Resources**

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

Why is Statistics Important? (10 Reasons Statistics Matters!)

The Importance of Statistics in Business

The Importance of Statistics in Education

The Importance of Statistics in Healthcare