To calculate MAD and MAPE for management’s technique. Introduction: A sequence of data points in successive order is known as time series. Time series forecasting is the prediction based on past events, which are at uniform time interval. Moving average method and trend projections are two of the time series methods, which use weights to prioritize past data.

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Answer 1

Question

Chapter 4, Problem 23P

a)

Summary Introduction

To determine: To calculate MAD and MAPE for management’s technique.

Introduction: A sequence of data points in successive order is known as time series. Time series forecasting is the prediction based on past events, which are at uniform time interval. Moving average method and trend projections are two of the time series methods, which use weights to prioritize past data.

b)

Summary Introduction

To determine: To determine whether the management’s results outperform naïve forecast by comparing MAD and MAPE.

c)

Summary Introduction

To determine: To compare the results of management forecast and naïve forecast and to recommend a forecasting technique based on lower forecast error.

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Answer 2

Question

Chapter 4, Problem 23P

a)

Summary Introduction

To determine: To calculate MAD and MAPE for management’s technique.

Introduction: A sequence of data points in successive order is known as time series. Time series forecasting is the prediction based on past events, which are at uniform time interval. Moving average method and trend projections are two of the time series methods, which use weights to prioritize past data.

b)

Summary Introduction

To determine: To determine whether the management’s results outperform naïve forecast by comparing MAD and MAPE.

c)

Summary Introduction

To determine: To compare the results of management forecast and naïve forecast and to recommend a forecasting technique based on lower forecast error.

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Answer 3

Question

Chapter 4, Problem 23P

a)

Summary Introduction

To determine: To calculate MAD and MAPE for management’s technique.

Introduction: A sequence of data points in successive order is known as time series. Time series forecasting is the prediction based on past events, which are at uniform time interval. Moving average method and trend projections are two of the time series methods, which use weights to prioritize past data.

b)

Summary Introduction

To determine: To determine whether the management’s results outperform naïve forecast by comparing MAD and MAPE.

c)

Summary Introduction

To determine: To compare the results of management forecast and naïve forecast and to recommend a forecasting technique based on lower forecast error.

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Answer 4

Question

Chapter 4, Problem 23P

a)

Summary Introduction

To determine: To calculate MAD and MAPE for management’s technique.

Introduction: A sequence of data points in successive order is known as time series. Time series forecasting is the prediction based on past events, which are at uniform time interval. Moving average method and trend projections are two of the time series methods, which use weights to prioritize past data.

b)

Summary Introduction

To determine: To determine whether the management’s results outperform naïve forecast by comparing MAD and MAPE.

c)

Summary Introduction

To determine: To compare the results of management forecast and naïve forecast and to recommend a forecasting technique based on lower forecast error.

Expert Solution & Answer

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Answer 5

Chapter 4, Problem 23P

a)

Summary Introduction

To determine: To calculate MAD and MAPE for management’s technique.

Introduction: A sequence of data points in successive order is known as time series. Time series forecasting is the prediction based on past events, which are at uniform time interval. Moving average method and trend projections are two of the time series methods, which use weights to prioritize past data.

b)

Summary Introduction

To determine: To determine whether the management’s results outperform naïve forecast by comparing MAD and MAPE.

c)

Summary Introduction

To determine: To compare the results of management forecast and naïve forecast and to recommend a forecasting technique based on lower forecast error.

Answer 6

Chapter 4, Problem 23P

a)

Summary Introduction

To determine: To calculate MAD and MAPE for management’s technique.

Introduction: A sequence of data points in successive order is known as time series. Time series forecasting is the prediction based on past events, which are at uniform time interval. Moving average method and trend projections are two of the time series methods, which use weights to prioritize past data.

Answer 7

Chapter 4, Problem 23P

a)

Summary Introduction

To determine: To calculate MAD and MAPE for management’s technique.

Introduction: A sequence of data points in successive order is known as time series. Time series forecasting is the prediction based on past events, which are at uniform time interval. Moving average method and trend projections are two of the time series methods, which use weights to prioritize past data.

Answer 8

b)

Summary Introduction

To determine: To determine whether the management’s results outperform naïve forecast by comparing MAD and MAPE.

Answer 9

b)

Summary Introduction

To determine: To determine whether the management’s results outperform naïve forecast by comparing MAD and MAPE.

Answer 10

c)

Summary Introduction

To determine: To compare the results of management forecast and naïve forecast and to recommend a forecasting technique based on lower forecast error.

Answer 11

c)

Summary Introduction

To determine: To compare the results of management forecast and naïve forecast and to recommend a forecasting technique based on lower forecast error.

Answer 12

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Answer 13

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Answer 14

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Answer 15

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Solution Summary: The author explains how to calculate MAD and MAPE for management's technique. The absolute error for March is the modulus of the difference between 101 and 120.

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To determine: To determine whether the management’s results outperform naïve forecast by comparing MAD and MAPE.

To determine: To compare the results of management forecast and naïve forecast and to recommend a forecasting technique based on lower forecast error.

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