Forecasting in Qlik Sense: Building Your First Linear Regression Model

Qlik Sense is known for powerful data visualization and analytics capabilities, but did you know it also has built-in predictive modeling tools? That‘s right – you can create linear regression models to forecast metrics like sales, web traffic, and more, without ever leaving your Qlik Sense environment.

In this expert guide, we‘ll walk through step-by-step how to build, interpret, and apply linear regression models in Qlik Sense. Whether you‘re a business analyst looking to level up your skills or a data scientist seeking to deploy models more efficiently, this article will give you a solid foundation. Let‘s get started!

Linear Regression 101

Before diving into Qlik Sense specifics, let‘s review some key linear regression concepts. Linear regression is a statistical method for modeling the linear relationship between independent variables (the features you use to make predictions) and a dependent variable (the metric you want to predict).

The simple linear regression equation takes the form:

$y = \beta_0 + \beta_1x + \epsilon$

Where:

• $y$ is the dependent variable
• $x$ is the independent variable
• $\beta_0$ is the y-intercept (value of y when x is 0)
• $\beta_1$ is the slope (change in y for a 1-unit change in x)
• $\epsilon$ is the error term

The most common method for fitting a linear regression model is ordinary least squares (OLS). OLS estimates the coefficients $\beta_0$ and $\beta_1$ by minimizing the sum of the squared residuals between the predicted and actual y values.

Key assumptions for linear regression include:

1. Linearity: The relationship between X and y is linear
2. Independence: The errors are independent of each other
3. Normality: The errors are normally distributed
4. Homoscedasticity: The errors have constant variance
5. No multicollinearity: The independent variables are not highly correlated with each other

When these assumptions hold, linear regression is a powerful tool for understanding relationships between variables and making predictions.

However, linear regression is not appropriate for every situation. It assumes a linear relationship, so if the true pattern is nonlinear (e.g. exponential or sinusoidal), the model will be missspecified. Linear regression is also sensitive to outliers, which can drastically change the slope of the line.

Despite these limitations, linear regression remains one of the most widely used algorithms in machine learning and data science. And with built-in linear regression capabilities, Qlik Sense makes it easy to apply this fundamental technique to your own data.

Building a Linear Regression Model in Qlik Sense

Now let‘s see how to actually build a linear regression model in Qlik Sense, using some sample sales data.

Load and Explore the Data

The first step is to load your data into Qlik Sense. For this sales forecasting example, let‘s assume you have a table with the following columns:

• Date
• Sales ($)
• Marketing Spend ($)
• Average Price ($)
• Holiday Flag (1 if a major holiday occurred in the period, 0 if not)

Before building the model, it‘s always a good idea to explore the data visually. Create a scatterplot with Sales on the y-axis and Date on the x-axis to check for any obvious patterns or outliers.

Create the Model

Next, we‘ll use the Qlik Sense linear regression functions to fit the model. Create a new Master Item measure with the following expression:

LinRegSlope(Total Sales, Date.YearMonth) * Date.YearMonth + LinRegIntercept(Total Sales, Date.YearMonth)

This calculates the predicted Sales value based on the slope and intercept of the linear regression line. The LinRegSlope function returns the slope ($\beta_1$) and LinRegIntercept returns the y-intercept ($\beta_0)$.

Add this measure to a line chart with Date on the x-axis. Overlay the actual Sales values to compare the model‘s predictions to reality:

The R-squared value measures the proportion of variance in Sales that can be explained by the Date variable. Calculate R-squared with the RSquared function:

RSquared(Total Sales, Date.YearMonth)

An R-squared close to 1 indicates the model fits the data well, while a low value suggests Date alone is not sufficient to explain the variation in Sales.

Evaluate and Refine the Model

Before using the model for forecasting, it‘s important to assess its performance and assumptions. Plot the model residuals (actual minus predicted values) to check for patterns:

[Sales] - (LinRegSlope(Total Sales, Date.YearMonth) * Date.YearMonth + LinRegIntercept(Total Sales, Date.YearMonth)

Ideally, the residuals appear randomly distributed with no clear pattern. If you see a curve or increasing/decreasing trend, that indicates a violation of the linearity assumption.

To quantify how well the model fits the data, we can look at metrics beyond the R-squared. Three common ones are:

Metric	Qlik Expression	Interpretation
Mean Absolute Error (MAE)	Avg(Abs([Sales] - (LinRegSlope(Total Sales, Date.YearMonth) * Date.MonthYear + LinRegIntercept(Total Sales, Date.YearMonth))))	Avg absolute difference between actual and predicted Sales. Lower is better.
Mean Squared Error (MSE)	Avg(([Sales] - (LinRegSlope(Total Sales, Date.YearMonth) * Date.MonthYear + LinRegIntercept(Total Sales, Date.YearMonth)))^2)	Avg squared difference between actual and predicted Sales. More sensitive to outliers than MAE.
Root Mean Squared Error (RMSE)	Sqrt(Avg(([Sales] - (LinRegSlope(Total Sales, Date.YearMonth) * Date.MonthYear + LinRegIntercept(Total Sales, Date.YearMonth)))^2))	Square root of MSE, giving a value in the same units as Sales.

The model can be refined by testing out different independent variables. For example, to control for the impact of Marketing Spend, recalculate the model with:

LinRegSlope(Total Sales, Sum(Marketing Spend)) * Sum(Marketing Spend) + LinRegIntercept(Total Sales, Sum(Marketing Spend))  

Evaluate performance on a holdout dataset not used in fitting the model. This provides an unbiased estimate of how well it generalizes. In Qlik Sense, this could look like:

[Actual/Predicted]:
If(Date.YearMonth <= Num#(Month(Today(),-2)),               
"Actual",
"Predicted")

[Sales Measure]:
If([Actual/Predicted]="Actual", 
    Sum(Sales),
    LinRegSlope(Total Sales, Date.YearMonth)*Date.YearMonth + LinRegIntercept(Total Sales, Date.YearMonth)
) 

Forecasting Future Sales

With a refined and validated model, we can now forecast Sales for future time periods. Add rows to your dataset for the periods you want to forecast, leaving the Sales column blank. The model will automatically extend the fitted line to predict Sales.

In the chart below, the shaded region represents 80% prediction intervals – giving a range we‘re 80% confident the true Sales value falls within, based on the model‘s standard error.

Advanced Topics and Considerations

Multicollinearity: Including highly correlated independent variables can lead to unstable coefficient estimates. Check the correlation matrix for your independent variables. Consider dropping one of any pairs with correlation > 0.7.

Heteroscedasticity: If the residuals show a fan or cone shape when plotted against the predicted values, that indicates a violation of the constant variance assumption. Weighted least squares or transformation of the dependent variable can resolve this.

Regularization: For models with many independent variables, regularization techniques like lasso and ridge regression can help prevent overfitting and improve out-of-sample performance. These are not natively available in Qlik Sense but can be implemented with R or Python integration.

Nonlinear Relationships: If the scatterplot shows a clear nonlinear pattern, linear regression is not appropriate. Potential alternatives include polynomial regression, splines, or generalized additive models (GAMs). Again, these can be implemented via Qlik‘s external scripting integrations.

Outliers: Linear regression is sensitive to outliers. A single extreme value can considerably change the slope of the line. If warranted, consider removing outliers before fitting the model. Alternatively, a robust regression technique like Huber regression is more resilient.

The Business Value of In-Platform Predictive Modeling

Qlik Sense‘s built-in linear regression tools offer significant advantages over the traditional approach of building models in a separate data science platform:

1. Efficiency: There‘s no need to move data between systems or reformat it for a specific modeling tool. The data is modeled right where it already lives for analytics.

2. Accessibility: Citizen data scientists and business analysts can leverage predictive modeling without having to learn code or a new platform. The point-and-click approach allows users across skill levels to participate.

3. Integrated Insights: Model results are seamlessly embedded in existing Qlik Sense dashboards and apps. This allows predictive insights to be consumed in the context of the business, rather than siloed off.

A 2020 Gartner survey found that "50% of organizations lack sufficient AI and data literacy skills to achieve business value" (1). Platforms like Qlik Sense that offer code-free, integrated modeling help to democratize machine learning and make AI an accessible part of the decision-making process.

The ability to forecast metrics like sales without writing a single line of code is a major asset for data-driven organizations. In fact, the efficiencies of in-platform modeling were a key value driver behind Qlik‘s 2017 acquisition of CrunchBot AI (2). As Qlik VP of Innovation and Strategy Anthony Deighton stated at the time:

"Developers and analysts won‘t have to break out of their workflow in Qlik and bring data into a separate environment to build and deploy their models. Everything happens directly within Qlik."

Conclusion

This guide has walked through how to build, evaluate, refine, and apply linear regression models in Qlik Sense. You should now have the practical knowledge to start using linear regression to forecast your own business metrics.

Some key takeaways:

• Linear regression is a core machine learning technique for modeling linear relationships and making predictions. It‘s a foundational algorithm for any data scientist or analyst.

• Qlik Sense has native linear regression capabilities, with functions to compute the slope, intercept, R-squared, and predictions.

• A good predictive modeling process includes exploring the data visually, evaluating model assumptions and performance, and testing on holdout data.

• Real-world data often violates assumptions of linear regression. Tools like variable transformation, outlier removal, and regularization can help address violations.

• In-platform modeling, like Qlik Sense offers, brings major efficiency and accessibility benefits over the traditional approach of external data science tools.

Linear regression in Qlik Sense is just the tip of the predictive iceberg. Once you‘ve mastered these techniques, you can explore more advanced algorithms, including nonlinear models and ensembles, via Qlik‘s R and Python integrations. The key is to always let the business question and the data guide your approach – not the algorithm.

To learn more, check out these helpful resources:

• Qlik Sense Regression Analysis Tutorial (3)
• Code for Cause Linear Regression in Machine Learning Guide (4)
• An Introduction to Statistical Learning Textbook (5)

Now it‘s your turn. What metrics will you forecast with linear regression in Qlik Sense? Share in the comments below!

References

1. Gartner Data Science Survey 2020
2. Qlik Acquires CrunchBot and Crunch Data
3. Qlik Sense Regression Analysis Tutorial
4. Linear Regression in Machine Learning
5. An Introduction to Statistical Learning