Which of the following techniques is commonly used to handle time series data with a linear trend?Review LaterDifferencingSeasonal adjustmentMoving average smoothingLinear regression

The technique commonly used to handle time series data with a linear trend is Linear Regression.

Here's a step-by-step explanation:

1. Identify the Problem: The problem here is to handle time series data with a linear trend.

2. Choose the Right Technique: For a linear trend, the most suitable technique is Linear Regression. This is because linear regression is used to predict the value of a variable based on the value of another, and it works by fitting a linear equation to observed data.

3. Implement the Technique: In linear regression, the steps are to first plot the data to see if a linear trend exists, then define your variables (the variable you want to predict and the variable you want to base your predictions on), and finally use a software or programming language to run the linear regression.

4. Interpret the Results: The output of a linear regression is an equation that can be used to predict values within the range of the data. If the trend is truly linear, as it is assumed to be in this question, then the predictions should be accurate.

Other techniques like Differencing, Seasonal adjustment, and Moving average smoothing are also used in time series analysis but they are more suitable for data with a seasonal component or when the trend is not linear.