2720 Time Series Smoothing

Moving Mean or Median Smoothing is the best method to smooth the data in this scenario. This method involves taking the average or median of a set number of data points over a certain time period. By using this method, the fluctuations in the number of inquiries generated each day can be reduced, providing a more consistent and stable representation of the data over the three month period.

Moving Mean or Median Smoothing is the best method to smooth the data in this scenario. This technique involves calculating the average or median of a specified number of data points, known as the window size, and then moving the window along the data set to calculate the average or median for each window. This helps to reduce the impact of outliers and random fluctuations in the data, providing a more stable and representative trend over time. By applying this smoothing method to the statistics on the number of inquiries generated each day, the significant difference in results over a three-month period can be minimized, allowing for a clearer understanding of the overall trend.

The correct answer is "Viewing statistics for a TV show that airs on Monday and Friday night for 6 weeks." This is because smoothing techniques like Moving Median or Mean Smoothing are typically used to remove noise and fluctuations in data over time. In this case, the TV show viewing statistics would likely have daily fluctuations due to various factors, and smoothing the data would help to identify any underlying trends or patterns in the viewership. The other options, such as sales data, entry figures, or pie sales, may not necessarily require smoothing as they may already exhibit clear trends or patterns.

Moving Mean or Median Smoothing is the best method to smooth the data in this scenario. This method involves calculating the average or median of a specific number of consecutive data points, typically over a fixed window size. By using this technique, the fluctuations in the number of inquiries generated each day can be reduced, providing a more stable and representative trend over the four-month period.

The answer is "Viewing statistics for a TV show that airs from Tuesday to Thursday night for 2 Months" because this data set represents a specific time period (2 months) and a specific range of days (Tuesday to Thursday). Using 3 Moving Median or Mean Smoothing would help in smoothing out any fluctuations or anomalies in the viewing statistics during this time period, providing a more accurate representation of the overall trends and patterns.

Moving mean or median smoothing is a suitable method for smoothing the data in this scenario. Since the farmer records the outcomes of shearing his sheep twice a year, the data is likely to have some level of variability or noise. Moving mean or median smoothing can help to reduce this variability and provide a clearer trend in the data. By calculating the mean or median of a moving window of data points, the extreme values or fluctuations can be smoothed out, making it easier to identify any underlying patterns or changes over time.

The correct answer is Quarterly Sales figures for Cars at a Holden Dealership for 3 years. This is because the data set consists of quarterly sales figures over a long period of time, which makes it suitable for smoothing using moving median or mean techniques. Smoothing techniques are typically used to reduce noise and fluctuations in data, and are more effective when applied to data sets with larger sample sizes and longer time spans.

The correct answer is Monday to Friday Sales of Bread and Milk from a Milk Bar for 5 weeks and Sale of pies from the school canteen over a three week period. Both of these cases involve data sets that have a clear and consistent pattern over a specific time period. Smoothing the data in these cases would help to eliminate any random fluctuations or outliers, providing a clearer representation of the overall trend in sales.