Last updated on Mar 14, 2023

## Latest Forecasting MCQ Objective Questions

#### Forecasting Question 1:

Which one of the following is the simple and easiest method of forecasting?

1. exponential smoothing
2. moving average method
3. regression
4. extrapolation

Option 4 : extrapolation

#### Forecasting Question 1 Detailed Solution

Explanation:

Types of Forecasting:

• Qualitative methods
• Quantitative (TIme series) methods
• Casual methods

​Qualitative methods:

1. Delphi method
2. Market research
3. Panel consensus
4. Visionary forecast
5. Historical analogy

Quantitative methods:

1. Moving average
2. Exponential smoothing
3. Box-Jenkins
4. X-11
5. Trend Projections

Casual methods:

1. Regression model
2. Economic model
3. Anticipations survey
4. Input-output model
5. Extrapolation

Moving Average method:

• Each point of the moving average of a time series is the arithmetic or weighted average of a number of consecutive points of the series.
• When there is enough available history data available, then this method is used.

Exponential smoothing method:

• It is similar to the moving average method except for the fact that recent data points are given more weightage.
• It needs a single parameter called alpha (α), also known as the smoothing factor.
• Alpha controls the rate at which the influence of past observations decreases exponentially. The parameter is often set to a value between 0 and 1.

Regression:

• In regression. there is a relationship between two different variables, called the dependent and independent variables.
• After deciding the relationship between two variables, the equation can be generated to forecast future data.
• Simple linear regression is commonly used in forecasting and financial analysis.

Extrapolation:

• a method of prediction that assumes that the patterns that existed in the past will continue on into the future and that those patterns are regular and can be measured. In other words, the past is a good indicator of the future.
• It is one of the easiest and simplest methods of forecasting and it is used where there is enough past data available that is not subjected to change drastically in near future.

#### Forecasting Question 2:

Name the human resource demand (need) forecasting technique, which solicits estimates of personnel needs from a group of experts, usually managers. The HRP experts act as intermediaries, summarise the various responses and report the findings back to the experts. The experts are surveyed again after they receive this feedback. Summaries and surveys are repeated until the experts' opinions begin to agree. The agreement reached is the forecast of the personnel needs.

Select the correct option :

1. Ratio - trend Analysis
2. Regression Analysis
3. Delphi Technique
4. Venture Analysis

Option 3 : Delphi Technique

#### Forecasting Question 2 Detailed Solution

The correct option is Delphi Technique Key Points

Delphi technique

• The Delphi technique is defined as a multistage survey that attempts to ultimately achieve consensus on an important issue
• In its original form Delphi listing of two or more rounds of questionnaires administered to the expert panel for their opinion on certain issues
• Every round the panelist receives feedback in the form o a statistical representation of the overall group response
• The benefits of conducting the Delphi technique are
1. It allows the panels of participants to reconsider and reflect on their opinion in light of the contribution of other people's views
2. Expert knowledge can result in accurate solutions to current problems
• itas the expert opinion is taken into consideration there by the decision can be taken in an easy way.
• it is one of the effective forecasting used

Hence the correct answer is Delphi Technique.

#### Forecasting Question 3:

The sales of water purifiers in a shop in 4 consecutive months are as given below. The magnitude of difference in the forecast for the next month using the exponential smoothening method with a smoothening constant of 0.3 and  weighted moving average method with the weights as 0.1, 0.2, 0.3 and 0.4 will be ______water purifiers (in Integer)

 Months Di January 1080 February 950 March 1050 April 1120

#### Forecasting Question 3 Detailed Solution

Calculation:

Forecast for the January is not given.

Assuming the forecast for January is equal to demand in January. so, the table can be generated using the forecasting formula for February, March, and April month to get the forecast for May month.

 Months Di Fi (Forecast) January 1080 1080 February 950 1080 March 1050 1041 April 1120 1044

F= F+ α (D- F1)

⇒ F= 1080 + 0.3 (1080 - 1080) = 1080

⇒ F= 1080 + 0.3 (950 - 1080) = 1041

⇒ F= 1041 + 0.3 (1050 - 1041) = 1044

Next month's forecast using exponential smoothening

F5 = F+ α (D- F4)

F =1044 + 0.3 (1120-1044) = 1066.8

⇒ F5 ≃1067 water purifiers
Forecast using weighted moving average is given as,

⇒ F'=1120 × 0.4 + 1050 × 0.3 + 950 × 0.2 + 1080 ×  0.1

⇒ F'= 1061 water purifiers

Therefore difference in forecast = 1067-1061 = 6

#### Forecasting Question 4:

For product demand and forecasting values for various periods are given in the table below :

 Period Demand Forecast Value A 110 96 B 124 127 C 119 134 D 134 112

The ratio of running sum forecast error to mean absolute deviation will be______(correct up to two decimal places).

#### Forecasting Question 4 Detailed Solution

Calculation:

$$MAD = \frac{{\sum\limits_{i = 1}^n {|{D_i}\, - \,{F_i}|} }}{n}$$

$$⇒ MAD = \frac{{\left\{ {|110 - 96| + |124 - 127| + |119 - 134| + |134 - 112|} \right\}}}{4}$$

$$⇒ MAD= \frac{{14 + 3 + 15 + 22}}{4}$$

$$⇒ \left[ {MAD = \frac{{54}}{4}} \right]$$
Running sum forecast error (RSFE),

$$RSFE = \displaystyle \sum ^ n _{i =1} (Di-Fi)$$

$$⇒ RSFE = \left( {110 - 96} \right) + \left( {124 - 127} \right) + \left( {119 - 134} \right) + \left( {134 - 112} \right)$$
$$⇒ RSFE = 14 - 3 - 15 + 22 = 18$$
$$⇒ \left[ {\frac{{RSFE}}{{MAD}} = \frac{{18}}{{\left( {54/4} \right)}} = \frac{{18 \times 4}}{{54}} = 1.33} \right]$$

#### Forecasting Question 5:

_______ has a fair to very good accuracy for short and long term forecasts.

1. Judgmental technique
2. Prior knowledge
3. Trend line technique
4. Delphi method

Option 4 : Delphi method

#### Forecasting Question 5 Detailed Solution

Explanation:-

Delphi method -

It is an iterative group process that is used especially for long-term forecasting and it can also be used for short-term forecasting.

As opposed to regular panels where the individuals involved are in direct communication, this method eliminates the effects of group potential dominance of the most vocal members.

The group involves individuals from inside as well as outside the organization. Typically, the procedure consists of the following steps:

• Each expert in the group makes his/her own forecasts in form of statements.
• The coordinator collects all group statements and summarizes them.
• The coordinator provides this summary and gives another set of questions to each group member including feedback on the input of other experts.
• The above steps are repeated until a consensus is reached.

Trend line technique -

It is a time series model technique that is part of quantitative methods of forecasting.

Time series models look at past patterns of data and attempt to predict the future based on the underlying patterns contained within those data.

A technique that uses the least squares method to fit a straight line to the data. Judgemental technique -

Judgemental (subjective) methods are those in which the process used to analyze the data has not been well specified.

They may use objective data or subjective impressions as inputs, they may be supported by formal analysis.

Various methods of judgemental forecasting are listed below:

1) Personal Interviews

2) Telephone Interviews

4) Structured Meetings

5) Role Playing

## Top Forecasting MCQ Objective Questions

#### Forecasting Question 6

Which one of the following is not a casual forecasting method?

2. Econometric models
3. Linear regression
4. Multiple regression

Option 1 : Trend adjusted exponential smoothing

#### Forecasting Question 6 Detailed Solution

Explanation:

• Forecasting is the prediction of future sells or demand of the particular product.
• It is a projection based upon past data and art of human judgement.

Types of forecasting method

 Qualitative or Subjective Quantitative or Objective Judgemental Opinion Survey Market trial Market research Delphi technique Time series Past average Moving average Weighted moving average Experimental smoothing Casual or Econometrics Correlation Regression Used for long-range and new product Used for Short-range and for old products

#### Forecasting Question 7

In exponential smoothening method, which one of the following is true?

1. 0 ≤ α ≤ 1 and high value of α is used for stable demand
2. 0 ≤ α ≤ 1 and high value of α is used for unstable demand
3. α ≥ 1 and high value of α is used for stable demand
4. α ≤ 0 and high value of α is used for unstable demand

Option 2 : 0 ≤ α ≤ 1 and high value of α is used for unstable demand

#### Forecasting Question 7 Detailed Solution

Concept:

The general form of forecasting is Ft = Ft-1 + α. (Dt-1 – Ft-1)

where α = smoothing constant and its range is 0 ≤ α ≤ 1

For Immediate forecast → high value of "α" is high and less for other forecasts.

Hence the high value of forecast is only chosen when the nature of demand is not reliable rather unstable.

$$α = \frac{2}{{n + 1}}$$

where, n = no. of period of moving average, Ft = recent forecast, Ft-1 = previous forecast, Dt-1 = previous demand

• If α = 0, then Ft = Ft-1 ……….…..(limit of stability)
• If α = 1 then Ft = Dt-1…………….(limit of responsiveness)​

​

​ #### Forecasting Question 8

In a time series forecasting model, the demands for five time periods were 10, 13, 15, 18 and 22. A linear regression fit resulted in an equation F = 6.9 + 2.9t where F is the forecast for period t. The sum of the absolute deviations for the five data is

1. 2.3
2. 0.2
3. -1.2
4. 2.2

Option 4 : 2.2

#### Forecasting Question 8 Detailed Solution

Concept:

The absolute deviation is |D – F|

The forecast for each period can be calculated using the regression line equation.

Calculation:

 Period(t) Dt Ft = 6.9 + 2.9t |Dt – Ft| 1 10 9.8 0.2 2 13 12.7 0.3 3 15 15.6 0.6 4 18 18.5 0.5 5 22 21.4 0.6

Sum of absolute deviations is = 0.2 + 0.3 + 0.6 + 0.5 + 0.6 = 2.2

#### Forecasting Question 9

The number of averaging period in the simple moving average method of forecasting is increased for greater smoothing but at the cost of

1. Accuracy
2. Stability
3. Visibility
4. Responsiveness to changes

Option 4 : Responsiveness to changes

#### Forecasting Question 9 Detailed Solution

Explanation:

Exponential smoothing method:

$${F_t} = {F_{t - 1}} + α \left[ {{D_{t - 1}} - {F_{t - 1}}} \right]$$

where α = smoothing constant.

Moving Average Method:

The moving average method uses the average of the most recent 'n' data values in the time series as the forecast for the next period.

$${F_{t + 1}} = \frac{{{D_t} + {D_{t - 1}} + \ldots + {D_{t - n + 1}}}}{n}$$

Note that the 'n' past observations are equally weighted.

The simple moving average model described above has the undesirable property that it treats the last 'k' observations equally and completely ignores all preceding observations.

Intuitively, past data should be discounted in a more gradual fashion -- for example, the most recent observation should get a little more weight than 2nd most recent, and the 2nd most recent should get a little more weight than the 3rd most recent, and so on. The simple exponential smoothing model accomplishes this.

Thus, the simple exponential smoothing forecast is somewhat superior to the simple moving average forecast because it places relatively more weight on the most recent observation i.e., it is slightly more "responsive to changes" occurring in the recent past.

#### Forecasting Question 10

Which of the following forecasting methods takes a fraction of forecast error into account for the next period forecast?

1. simple average method
2. moving average method
3. weighted moving average method
4. exponential smoothening method

Option 4 : exponential smoothening method

#### Forecasting Question 10 Detailed Solution

Explanation:

Forecast error (ei) for a period is defined as the difference between actual and actual and forecasted demand.

ei = Actual demand - Forecast demand ⇒ Di - Fi

Exponential forecasting:

$${F_T} = {F_{T - 1}} + α ({D_{T - 1}} - {F_{T - 1}})$$

where

FT is the forecast for the next period

$$({D_{T - 1}} - {F_{T - 1}})$$ is the forecast error and

α is the smoothing constant.

Thus exponential smoothing takes into account the forecast error of the previous period, for the forecast of the next period. Additional Information

Simple Average Method:

In the simple moving average, we take the average of the past data points for future demand.

For 'n' period moving average forecast will be given by:

$${F_{n+1}} = \frac{{{D_1}\;+\;{D_2}\;+\;{D_3}\;+ \;{D_4}\;+\;\ldots \ldots \ldots \ldots \ldots \ldots \;+\;{D_n}}}{n}$$

Weighted Moving Average Method:

In weighted moving average, the highest weightage is given to recent data & it decreases for older data points.

For n period weighted moving average, weightage is as follows:

$$\frac{n}{{{\rm{\Sigma }}n}},\;\frac{{n - 1}}{{{\rm{\Sigma }}n}},\;\frac{{n - 2}}{{{\rm{\Sigma }}n}}, \;- - - - - - - ,\frac{1}{{{\rm{\Sigma }}n}}$$

$${F_{n+1}} = \left[ ({{w_{1}} \times {D_{1}})\;+\;({w_{2}\times {D_{2}}})\;+\;..........+\;({w_{n}} \times {D_{n}}})\right]$$

#### Forecasting Question 11

The correlation coefficient between two variables X and Y is found to be 0.6. All the observations on X and Y are transformed using the transformations U = 2 – 3X and V = 4Y + 1. The correlation coefficient between the transformed variables U and V will be

1. -0.5
2. +0.5
3. -0.6
4. +0.6

Option 3 : -0.6

#### Forecasting Question 11 Detailed Solution

Concept:

• Corr(a + bX, c +dY) = Corr(bX, dY), Where, var (X) = b & var (Y) = d
• Corr(aX, bY) = (ab)Corr (X, Y) where a, b, c, and d are constants
• \begin{aligned} \operatorname{Corr}(X, Y) &=\frac{\operatorname{Corr} (X,Y)}{\sqrt{\operatorname{Var}(X) \operatorname{Var}(Y)}} \\ \end{aligned}

Calculation:

Given:

\begin{aligned} \operatorname{Corr}(X, Y) &=0.6 \\ \operatorname{Corr}(U, V) &=\frac{\operatorname{Corr} (U,V)}{\sqrt{\operatorname{Var}(U) \operatorname{Var}(V)}} \\ &=\frac{\operatorname{Corr}(2-3 X, 4Y+1)}{\sqrt{(-3)^{2}} \sqrt{4^{2}}} \\ &=\frac{\operatorname{Corr} (-3 X, 4Y)}{12} \\ \\ &=\frac{(-3\times 4)\operatorname{Corr} ( X, Y)}{12} \\\\ &=\frac{(-12)\operatorname{Corr} ( X, Y)}{12} \\&=\frac{-12}{12}\times{0.6} \\ &=-0.6 \end{aligned}

Hence, option (3) is correct.

#### Forecasting Question 12

Which of the following forecasting technique uses three types of participants: decision-makers, staff personnel and respondents?

1. Expert's opinion
2. Sales force survey
3. Consumer survey
4. Delphi method

Option 4 : Delphi method

#### Forecasting Question 12 Detailed Solution

Explanation:

Forecasting:

Forecasting is the prediction of future sales or demand for a particular product in the market. Forecasts can be made by using the past data of a product.

It can be done in two ways

i) Qualitative Technique:

This approach is used for new product and used for long term forecasting. In this approach, there is no need for any data.

Opinion survey:

• In this method, opinions are collected from the customer, retailer and distributor regarding the demand pattern of the product.

Market trial:

• It is applied for new product and in this case, a product is introduced between a limited population in the form of a free sample.
• It is applied for low-cost products like toothpaste, chocolate, coldrinks etc.

Market research:

• In this method, the work of survey is assigned to an external marketing agency and the purpose of the research is to collect information regarding the demand of a product and the various factors which influence the demand like customer income, location, quality, quantity etc are required to get the forecast.

Delphi technique:

• This technique is used to make more realistic judgemental methods by minimizing bias.
• In this method, a panel of experts (including decision-makers, staff personnel, and respondents) is asked sequential questions.
• It is the step by step procedure and the final forecast is obtained by the common opinion of all the experts.

ii) Quantitative Technique:

This is used to forecast the demand for the existing product for short term

Here some previous data are given and based on that forecasting is done.

• Simple Moving Average Method
• Weighted Moving Average Method
• Simple Exponential Smoothing Method
• Trend Line Estimate or Linear Regression Method

#### Forecasting Question 13

Which of the following is a technique used for forecasting?

1. PERT/CPM
2. Exponential smoothing
3. Gantt Chart
4. Control Chart

Option 2 : Exponential smoothing

#### Forecasting Question 13 Detailed Solution

Explanation:

Forecasting

• Forecasting is defined as estimating the future value that a parameter will take. Most scientific forecasting methods forecast the future value using past data.
• Some simple forecasting models using time series data are simple average, moving average and simple exponential smoothing.

Moving average Method or rolling average Method:

• In this method, fresh average is calculated at the end of each period by adding the actual demand data for the most recent period and deleting the data for the order period. It gives equal weight to each of the most recent observations.

$${F_{n+1}} = \frac{{{D_1} + {D_2} + {D_3} + {D_4} + \ldots \ldots \ldots \ldots \ldots \ldots + {D_n}}}{n}$$

Weighted moving average Method:

• This method gives unequal weight to each demand data with more weight to recent data.

$${F_{n+1}} = \left[ {{w_{1}} \times {D_{1}} +{w_{2}\times {D_{2}}} +..........+ {w_{n}} \times {D_{n}}} \right]$$

Exponential Smoothing Method:

• This method gives weight to all the previous data and the pattern of weight assigned is exponentially decreasing in order with most recent data is given the highest weight.
• In exponential smoothing method of forecast, the forecast for the next period is equal to

F= α Dt-1 + (1 - α) Ft-1

where, Dt-1 = latest figure sale or latest demand,  Ft-1 = old forecast, α = exponential smoothing constant Additional Information

Project

• A project may be defined as a combination of interrelated activities which must be executed in a certain order before the entire task can be completed.
• The aim of planning is to develop a sequence of activities of the project so that the project completion time and cost are properly balanced.
• To meet the objective of systematic planning, the management has evolved several techniques applying network strategy.
• PERT (Programme Evaluation and Review Technique) and CPM (Critical Path Method) are network techniques which have been widely used for planning, scheduling and controlling the large and complex projects.

Difference between PERT and CPM (Critical Path Method)

 PERT CPM 1. Probabilistic approach 1. Deterministic approach 2. Three-time estimate 2. One - time estimate 3. Event oriented network model 3. Activity-oriented network model 4. The slack concept is used 4. Float concept is used 5. Project crashing is not possible 5. Project crashing is possible 6. Deals with probabilistic time estimates 6. Deals with deterministic time estimates

Gantt charts:

• Gantt charts are mainly used to allocate resources to activities.
• The resources allocated to activities include staff, hardware, and software.
• Gantt charts are useful for resource planning. A Gantt chart is a special type of bar chart where each bar represents an activity.
• The bars are drawn along a timeline.
• The length of each bar is proportional to the duration of time planned for the corresponding activity. Control charts:

• Control chart is a graphical representation of the collected information.
• It indicates whether a process is in control or out of control.
• It determines process variability and detects unusual variations taking place in a process.
• It ensures product quality level.
• It provides information about the selection and setting of tolerance limits.

#### Forecasting Question 14

Which of the following is not a part of the quantitative approach for forecasting?

1. Moving average
2. Simple average
3. Delphi method
4. Exponential smoothing

Option 3 : Delphi method

#### Forecasting Question 14 Detailed Solution

Explanation:

Forecasting:

Forecasting is the prediction of future sales or demand for a particular product in the market. Forecasts can be made by using the past data of a product.

It can be done in two ways

i) Qualitative Technique: This approach is used for new product and used for long term forecasting. In this approach, there is no need for any data.

Opinion survey:

• In this method, opinions are collected from the customer, retailer and distributor regarding the demand pattern of the product.

Market trial:

• It is applied for new product and in this case, a product is introduced between a limited population in the form of a free sample.
• It is applied for low-cost products like toothpaste, chocolate, coldrinks etc.

Market research:

• In this method, the work of survey is assigned to an external marketing agency and the purpose of the research is to collect information regarding the demand of a product and the various factors which influence the demand like customer income, location, quality, quantity etc are required to get the forecast.

Delphi technique:

• This technique is used to make more realistic judgemental methods by minimizing bias.
• In this method, a panel of experts is asked sequential questions.
• It is the step by step procedure and the final forecast is obtained by the common opinion of all the experts.

ii) Quantitative Technique: This is used to forecast the demand for the existing product for short term

Here some previous data are given and based on that forecasting is done.

• Simple Moving Average Method
• Weighted Moving Average Method
• Simple Exponential Smoothing Method
• Trend Line Estimate or Linear Regression Method

#### Forecasting Question 15

Sales data of a product is given in the following table:

 Month January February March April May Number of units sold 10 11 16 19 25

Regarding forecast for the month of June, which one of the following statements is TRUE?

1. Moving average will forecast a higher value compared to regression
2. Higher the value of order N, the greater will be the forecast value by moving average.
3. Exponential smoothing will forecast a higher value compared to regression.
4. Regression will forecast a higher value compared to moving average
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