### Answer Explanation

In simple exponential smoothing, the technique indeed places greater weight on more recent data compared to older data. This is achieved through a smoothing constant (often denoted as $\alpha$) which determines the weighting, where $\alpha$ is between 0 and 1. A higher $\alpha$ gives more emphasis to recent observations, while a lower $\alpha$ means that older data is weighted more.

Therefore, the statement "In simple exponential smoothing technique, the more recent data weighted more heavily" is **True**.

### Final Answer
**True**

Question

### Answer Explanation

In simple exponential smoothing, the technique indeed places greater weight on more recent data compared to older data. This is achieved through a smoothing constant (often denoted as $\alpha$) which determines the weighting, where $\alpha$ is between 0 and 1. A higher $\alpha$ gives more emphasis to recent observations, while a lower $\alpha$ means that older data is weighted more.

Therefore, the statement "In simple exponential smoothing technique, the more recent data weighted more heavily" is **True**.

### Final Answer
**True**

Knowee AI · Accepted Answer

### Answer Explanation

In simple exponential smoothing, the technique indeed places greater weight on more recent data compared to older data. This is achieved through a smoothing constant (often denoted as $\alpha$) which determines the weighting, where $\alpha$ is between 0 and 1. A higher $\alpha$ gives more emphasis to recent observations, while a lower $\alpha$ means that older data is weighted more.

Therefore, the statement "In simple exponential smoothing technique, the more recent data weighted more heavily" is **True**.

### Final Answer
**True**

In simple exponential smoothing technique, the more recent data weighted more heavily Group of answer choices True False

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In simple exponential smoothing technique, the more recent data weighted more heavily

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