Managerial Statistics Chapter 7 Quiz

Course Information

• Course Name: Managerial Statistics
• Course Code: BSMS2209
• Specialization: College Requirement
• College: Economics and Business Administration
• University: University of Technology and Applied Sciences

Course Objective

• Develop practical computing skills and problem-solving skills in business.
• Use appropriate statistical tools and graphs.
• Use spreadsheets for data management and analysis.

Learning Outcome 7

• Demonstrate knowledge of time series concepts.
• Apply concepts to real-world data.
• Interpret analysis results.

Learning Outcome 8

• Practice descriptive and inferential data analysis.
• Use Excel spreadsheets and SPSS.

Chapter 7 Outline

• Time Series Introduction
• Importance of Time Series Analysis in Business
• Components of Time Series:
  • Secular trend (long-term variation)
  • Seasonal variation
  • Cyclical variation
  • Irregular variation
• Measurement of Trend:
  • Freehand Graphic method
  • Semi-average method
  • Moving average method
  • Least Square method
  • Time Series Analysis using Excel Spreadsheet
  • Time Series Analysis using SPSS

Time Series - Introduction

• A time series is a collection of values of a variable at different time periods.
• Values are not expected to be the same for every time period.
• Example: The price of a commodity over several years will vary.

Meaning of Time Series Data

• Time series data is a sequence of observations.
• Observations are collected from a process with equally spaced time periods.

Definition of Time Series

• A time series is a set of data collected sequentially, usually at fixed time intervals.
• Example: Number of packets of milk sold daily in a small shop.

Example: Oil Production in Oman

• Data on oil production in Oman over time, displayed graphically.

Example: Population in Oman

• Data on Oman's population over time, displayed graphically, with values in millions.

Importance of Time Series Analysis in Business

• Profit planning
• Sales forecasting
• Stock market analysis
• Process and quality control
• Economic forecasting
• Risk analysis and evaluation of changes

Components of Time Series

• Any time series may contain some or all of the following components:
  • Secular trend or Long-term Variation
  • Seasonal variation
  • Cyclical variation
  • Irregular variation

Secular Trend

• Increase or decrease in the movement of a time series.
• Long-term movement in the time series.
• Factors like population growth, technological progress, and consumer demand shifts can impact the trend.

Seasonal Variation

• Short-term fluctuations that occur periodically in a time series usually within a year.
• Regular and recurring patterns affected by weather conditions and human customs.
• Example: Increased sales of winter clothing, or ice cream sales peaking during summer.

Cyclical Variation

• Recurrent upward or downward movements in a time series but with a period longer than a year.
• Not as regular as seasonal variations.
• Examples include business cycles, economic activity patterns, or consumer spending trends.

Irregular Variation

• Fluctuations in a time series that are short in duration, erratic in nature, and lacks any discernible pattern.
• Unforeseen factors like floods, earthquakes, wars, and famines can cause these variations.

Measurement of Trend

• Freehand Graphic method
• Semi-average method
• Moving average method
• Least Square method

Free Hand Graphic Method

• Plot data on a graph with time on the x-axis and the variable on the y-axis.
• Draw a smooth curve through the plotted points.
• Example exercise provided.

Semi-Average Method

• Divide the data into two equal parts.
• Calculate the average for each part.
• Plot these averages against the mid-points of each period.
• Connect these points to create the trend line.
• Exercise provided with example data.

Moving Average Method

• Calculate the moving average of a predetermined number of periods (e.g., 3-year moving average).
• This method provides a smoothed estimate of the trend.
• Example Exercise provided with example data

Least Squares Method (Linear Trend)

• Calculate the linear trend equation to determine a trend line.
• Key formula and variables (a, b) are provided for calculating the equation of the trend line through the data.
• Provided with example exercise and solution data.

Additional Exercises

• Calculate 5- yearly, 7-yearly, 9-yearly moving averages.
• Use Least Square method for a given data set (example provided).