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Residual Sum of Squares (RSS)


The Residual Sum of Squares (RSS) is a statistical technique used in regression analysis to measure the amount of variance in the data that is not explained by the regression model. It is also known as the sum of squared residuals. It is calculated by summing the squares of the difference between the observed and predicted values.


The phonetic transcription of “Residual Sum of Squares (RSS)” could be represented as follows: “ˈrɛzɪˌduːəl sʌm ʌv skwɛərz (ɑːr ɛs ɛs)”.

Key Takeaways

  1. Definition: Residual Sum of Squares (RSS), also known as the sum of squared residuals or sum of squared estimate of errors, is a statistical technique used to measure the amount of variance in a data set that is not explained by the regression model. It’s the sum of the squares of the difference between the predicted values by the model and the actual values.
  2. Application: RSS is frequently used in regression analysis and machine learning. It’s a measure of the discrepancy between the data and an estimation model. Lower values of RSS indicate a better fit of the regression line to the data points. Hence, many fitting methods aim at minimizing the RSS.
  3. Limitation: One limitation of using RSS as a measure of model fit is that it depends on the scale of the variables, which makes it difficult to compare across different data sets. It may also disproportionately emphasize large differences, due to the squaring of residuals.


In business and finance, the Residual Sum of Squares (RSS) is a crucial statistical tool utilized in regression analysis. It measures the total amount of error remaining between the predicted and actual values in a regression model. The smaller the RSS, the better the model’s ability to accurately predict data points, implying that the model is a good fit for the data. Hence, RSS is pivotal to estimate the accuracy of a model, help businesses make data-driven decisions, minimize risks, maximize profit, and optimize operational efficiency. It also contributes to the identification and correction of inaccuracies, enhancing the precision of forecasting and planning processes.


Residual Sum of Squares (RSS) is a widely-used statistical concept in the realm of finance and business, primarily employed to measure the efficiency of a model in terms of its predictive capabilities. In essence, it quantifies the variance that’s left unexplained after a model has been applied. If the predictions made are perfect, the RSS would be zero, denoting that the model has explained all the variability in the responses. However, in the real world, due to myriad uncertainties, perfect predictions are rare. Consequently, RSS is beneficial, as it offers a metric through which models can be evaluated and fine-tuned if necessary.In practical application, RSS is frequently used in regression analysis, where it facilitates in minimizing the discrepancy between observed and estimated data points, thereby establishing the best fit line. Moreover, in portfolio management, minimizing the RSS is tantamount to maximizing return for a given level of risk as measured by portfolio variance. When economists and data scientists are developing algorithms or predictive models, RSS acts as a yardstick for measuring how far off the predictions are from the actual outcomes. Thus, RSS serves an indispensable purpose in model selection, performance measurement, and risk management, across both finance and business industries.


1. Predicting Housing Prices: A real estate company may use a regression model to predict housing prices based on various factors like location, size, number of bedrooms, etc. The company needs to know how well their model is predicting the actual housing prices – this is where the Residual Sum of Squares (RSS) comes in. By calculating the residuals (the differences between the actual price and predicted price) and then taking the sum of their squares, the company can quantify the prediction errors. If the RSS is low, it indicates that the errors are low and the model is well fitted to their data. 2. Financial Forecasting: Investment companies extensively use regression models to predict future company earnings, stock prices, or economic indicators. Again, the success of such predictions depends on how accurate they are. Using RSS, these companies can measure the overall distance between the predicted financial values and the actual values, helping them to improve their forecasting models over time.3. Marketing Spend Optimization: Suppose a marketing manager is trying to forecast the impact of different advertising spends on sales. By applying multiple regression analysis on historical advertising and sales data, they can predict sales for different spend levels. However, there will be discrepancies between predicted and actual sales. These discrepancies, or residuals, can be squared and summed up to get the RSS. This can help the manager to quantify the effectiveness of the model and optimize future advertising spends.

Frequently Asked Questions(FAQ)

What is Residual Sum of Squares (RSS)?

Residual Sum of Squares (RSS) is a statistical technique used to measure the amount of variance in a data set that is not explained by the regression model. It measures the total difference between the observed and the expected output values.

Why is Residual Sum of Squares (RSS) important in finance?

In finance, the RSS is crucial as it’s often used to build statistical and predictive models. It’s used to evaluate the efficiency of models in predicting outputs for given inputs. The lower the RSS, the better is the model’s fit to the data.

How do you calculate the Residual Sum of Squares?

RSS can be calculated by taking the difference between the observed output and the predicted output for each data point, squaring these differences, and adding all of them up.

What does a low RSS signify?

A low RSS signifies that the model fits the given data effectively. It means the variation in the output is largely explained by the input(s), thus the model has a relatively high level of prediction accuracy.

How does RSS relate to R-square?

R-Square, also known as the coefficient of determination, is the proportion of variation in the dependent variable that is predictable from the independent variables. It’s calculated as 1 – (RSS/Total Sum of Squares), so a lower RSS would mean a higher R-Square which is an indicator of a better model fit.

Can the RSS ever be a negative number?

No, RSS can’t be negative. It’s the sum of the square of residuals, so it’s always a number equal to or greater than zero. Your model isn’t fitting the data well if the RSS is closer to zero.

Is there an ideal value for the RSS?

There isn’t a universally ‘ideal’ value for the RSS. It generally depends on the data-set size and variance. But generally, the lower the RSS, the better the model’s predictive ability.

Related Finance Terms

  • Least Squares Criterion
  • Regression Analysis
  • Sum of Squared Errors (SSE)
  • Goodness of Fit
  • Ordinary Least Squares (OLS) Method

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