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Statistics for Business and Economics
Chapter 14 Time Series: Descriptive Analyses, Models, & Forecasting

Copyright © 2014, 2011, and 2008 Pearson Education, Inc.

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Content
1 2 3 4 5 6 Descriptive Analysis: Index Numbers Descriptive Analysis: Exponential Smoothing Time Series Components Forecasting: Exponential Smoothing Forecasting Trends: Holt’s Method Measuring Forecast Accuracy: MAD and RMSE
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Content
7 8 9 Forecasting Trends: Simple Linear Regression Seasonal Regression Models Autocorrelation and the DurbinWatson Test

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Learning Objectives
• Focus on methods for analyzing data generated by a process over time (i.e., time series data). Present descriptive methods for characterizing time series data. Present inferential methods for forecasting future values of time series data.
Copyright © 2014, 2011, and 2008 Pearson Education, Inc.

• •

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Time Series
• • • • Data generated by processes over time Describe and predict output of processes Descriptive analysis
– Understanding patterns

Inferential analysis
– Forecast future values
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14.1
Descriptive Analysis: Index Numbers

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Index Number
• • • Measures change over time relative to a specific base period Price Index measures changes in price
– e.g. Consumer Price Index (CPI)

Quantity Index measures changes in quantity
– e.g. Number of cell phones produced annually
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Simple Index Number
A simple index number is based on the relative changes (over time) in…...

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