PDF- Chapter 3, PHAK Chapter 3, -Title 305 - NEBRASKA REAL ESTATE COMMISSION Chapter 3 - Chapter-3

## Description

### Chapter- 3 (FORECASTING) 1

What are some of the consequences of poor forecasts

• ? Explain

## Contrast the terms ‘sales’ and ‘demand’

Contrast the reactive and proactive approaches to forecasting

Give several examples of types of organizations or situations in which each type is used

### Which type of forecasting approach,

• qualitative or quantitative,
• is better

## Choose the type of forecasting technique (survey,

Delphi,

• averaging,
• seasonal naive,

or associative) that would be most appropriate a) b) c) d)

for predicting Demand for Mother’s Day greeting cards

# Demand for vacations on the moon

The impact of a price increase of 10 percent would have on sales of

e) Demand for toothpaste in a particular supermarket

Explain the trade-off between responsiveness and stability in a forecasting system that uses time series data

A commercial bakery has recorded sales (in dozens) for three products,

as shown below: Day 1 2 3 4 5 6 7 8 9 10 11

Blueberry Muffins 30 34 32 34 35 30 34 36 29 31 35

# Cinnamon Buns 18 17 19 19 22 23 23 25 24 26 27

### Cupcakes 45 26 27 23 22 48 29 20 14 18 47

• 12 13 14 15
• 31 37 34 33
• 28 29 31 33
• 26 27 24 22

a) Predict orders for the following day for each of the products using an appropriate naive method

(Hint: Plot each data set) b) What should the use of sales data instead of demand imply

## National Scan,

Monthly

sale for a seven-month period were as follows: Month Feb

May Jun

Sales (000 units) 19 18 15 20 18 22 20

Forecast September sales volume using each of the following: I

## Exponential smoothing with a smoothing constant equal to

assuming a march forecast of 19(000)

### The naïve approach A weighted average using

• 60 for August
• 30 for July,

### Which method seems least appropriate

? (Hint: Refer to your plot from part a

What does use of the term sales rather than demand presume

### An electrical contractor’s records during the last five weeks

indicate the number of job requests: Week:

# Requests:

Predict the number of requests for week 6 using each of these methods: a

#### Naive b

A four-period moving average

### Exponential smoothing with α =

Use 20 for week 2 forecast

### Saturday,

Sunday)

The owner-manager hopes to improve scheduling of part-time employees by determining seasonal relatives for each of these days

### Data on recent traffic at the center have been tabulated and are shown in the following table:

• 1 149 250 166

# Friday Saturday Sunday

• 2 154 255 162

WEEK 3 152 260 171

• 4 150 268 173
• 5 159 273 176
• 6 163 276 183

#### Use naïve and simple average technique to predict sales transactions for the following week

Obtain estimates of daily relatives for the number of customers

at a restaurant for the evening meal,

• given the following data

(Hint: Use a seven-day moving average

• ) Day 1 2 3 4 5 6 7 8 9 10

#### Number Served 80 75 78 95 130 136 40 82 77 80

Day 15 16 17 18 19 20 21 22 23 24

Number Served 84 78 83 96 135 140 44 87 82 88

• 11 12 13 14
• 94 131 137 42
• 25 26 27 28
• 99 144 144 48
• ## Chapter-3

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## Chapter 3

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