Moving average in Tableau - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ dax_lod_result

intermediate

Calculate 3-Period Moving Average Using WINDOW_AVG

You have a sales dataset with daily sales. You want to calculate a 3-day moving average of sales using Tableau's WINDOW_AVG function. Which of the following calculated fields will correctly compute the 3-day moving average for each day?

A WINDOW_AVG(SUM([Sales]), -1, 1)

B WINDOW_AVG(SUM([Sales]), -2, 0)

C WINDOW_AVG(SUM([Sales]), 0, 2)

D WINDOW_AVG(SUM([Sales]), -3, 0)

💡 Hint

Think about how the window offsets define the range of days included in the average.

The WINDOW_AVG function calculates the average of an expression over a range of data points. Using offsets -2 to 0 includes the current day and the two previous days, making a 3-day moving average.

❓ visualization

intermediate

Identify Correct Moving Average Line Chart

You created a line chart showing daily sales and added a 7-day moving average line. Which visualization best follows best practices for showing moving averages?

A Bar chart with daily sales and moving average combined in one bar without legend or axis labels.

B Scatter plot with daily sales points and moving average points overlapping without connecting lines.

C Pie chart showing proportion of sales per day with moving average as a separate pie slice.

D Line chart with daily sales as thin blue line and 7-day moving average as thick red line with legend and clear axis labels.

💡 Hint

Moving averages are best shown as smooth lines to show trends over time.

Option D uses a line chart with clear distinction between daily sales and moving average lines, proper legend, and axis labels, which is best practice for trend visualization.

🧠 Conceptual

advanced

Understanding Moving Average Window Size Impact

How does increasing the window size of a moving average affect the trend line in a time series visualization?

A It smooths the trend more, reducing noise but may delay detection of changes.

B It makes the trend line more sensitive to daily fluctuations.

C It causes the trend line to become jagged and less smooth.

D It removes all seasonality and cyclic patterns completely.

💡 Hint

Think about how averaging over more points affects responsiveness.

Larger window sizes average more data points, smoothing short-term fluctuations but causing lag in reflecting recent changes.

🔧 Formula Fix

advanced

Identify Error in Moving Average Calculation

You wrote this Tableau calculated field for a 5-day moving average: WINDOW_AVG(SUM([Sales]), 0, 4). What issue will this cause in the visualization?

Tableau

WINDOW_AVG(SUM([Sales]), 0, 4)

A It calculates a 5-day moving average correctly including past and future days.

B Syntax error due to incorrect WINDOW_AVG parameters.

C The moving average only looks forward, ignoring previous days, causing lag in trend detection.

D It causes division by zero error when there are fewer than 5 days.

💡 Hint

Check the window offsets to see which days are included.

The offsets 0 to 4 include the current day and the next 4 days, so it looks forward only, which can cause lag and misleading trends.

🎯 Scenario

expert

Designing a Dashboard with Multiple Moving Averages

You need to build a sales dashboard showing daily sales with 7-day and 30-day moving averages. Which approach best ensures clarity and usability for non-technical users?

A Use a single line chart with daily sales as a thin line, 7-day moving average as a medium line, and 30-day moving average as a thick line, with a clear legend and color coding.

B Create three separate charts for daily sales, 7-day, and 30-day moving averages without legends or labels.

C Use a stacked bar chart combining daily sales and moving averages in one bar per day.

D Show only the 30-day moving average line to reduce clutter and omit daily sales.

💡 Hint

Think about how to compare multiple trends clearly in one view.

Option A uses a single chart with distinct line styles and colors plus a legend, making it easy for users to compare daily sales and moving averages.