Seasonal Variation – NBC Institute

QTB – May 2017 – L1 – SA – Q14 – Statistics

This question involves identifying the estimable components of a time series.

Question

The TWO components of a Time Series which are usually estimable are:
A. Trend and Cyclic variation
B. Seasonal variation and Trend
C. Random movements and Trend
D. Seasonal variation and Random movements
E. Cyclic variation and Seasonal variation

Answer

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Tags: Forecasting, Seasonal Variation, Time series, Trend
Level: Level 1

Topic: Statistics
Series: MAY 2017

MI – May 2024 – L1 – SB – Q1 – Forecasting Techniques-Analysis, Moving Averages

Analyze a time series and describe two models for estimating seasonal variation.

Question

a. Describe how a time series can be analyzed. (10 Marks)

b. “There are TWO models used to estimate seasonal variation.” List and briefly describe the TWO models. (10 Marks)

Answer

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Tags: Additive model, Moving Averages, Proportional Model, Seasonal Variation, Time series
Level: Level 1

Topic: Forecasting Techniques-Analysis, Moving Averages
Series: MAY 2024

QT – May 2019 – L1 – Q1a – Forecasting

Calculate centered trend values using moving average, determine seasonal variations, adjust variations, and forecast future clients using a multiplicative model.

Question

The number of clients who consulted Tsoo Consult within a period of three years were recorded as follows:

Year	Quarter 1	Quarter 2	Quarter 3	Quarter 4
1	75	70	75	80
2	95	85	80	65
3	100	105	115	90

Required:
a) Assuming a 4 quarterly cycle, calculate the centred trend values for the data by moving average method. (4 marks)

b) Using (a) above and the multiplicative model, calculate the average seasonal variations. (5 marks)

c) Using (b) above, calculate the adjusted average seasonal variations for the data. (5 marks)

d) Using the trend and the adjusted average seasonal variation, forecast the number of clients for Year 4 based on the multiplicative model. (6 marks)

Answer

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Tags: Forecasting, Moving Averages, Multiplicative Model, Seasonal Variation, Time series
Level: Level 1

Topic: Forecasting
Series: MAY 2019

QT – Nov 2018 – L1 – Q6b – Forecasting

Calculate the moving average, trend values, seasonal variation, and forecast membership.

Question

Membership of Pro Amalion, a network of professional volunteers, has grown over the years but in the months of the second quarter, there was always a decline. The table below shows membership records for a period of four years:

Year	1st Quarter	2nd Quarter	3rd Quarter	4th Quarter
Year 1	713	694	735	755
Year 2	767	733	766	780
Year 3	787	755	798	814
Year 4	816	790	826	843

Required:
i) Calculate the centered four-quarterly moving average of membership. (4 marks)
ii) Using a least squares trend equation based on (i) above, calculate the trend values. (5 marks)
iii) Using (ii) above, calculate the percentage seasonal variation and the average seasonal variation of membership. (5 marks)
iv) Determine the seasonally adjusted forecast of membership for each of the four quarters of Year 5. (4 marks)

Answer

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Tags: Forecasting, Least Squares, Membership Forecast, Moving Averages, Seasonal Variation, Time series
Level: Level 1

Topic: Forecasting
Series: NOV 2018

QT – Nov 2015 – L1 – Q1b – Forecasting

Calculate the trend, average seasonal variation, and forecast for Year 4 using a 4-point centered moving average method for Golden Tree Chocolate Bar sales.

Question

Quarterly sales of a Golden Tree Chocolate Bar at a popular mall in KoKoLand City are given as follows:

YEAR	QUARTER 1	QUARTER 2	QUARTER 3	QUARTER 4
1	1260	756	588	1596
2	1352	966	579	2028
3	1786	920	865	2273

Required:
(i) Calculate the trend in the series using a 4-point centered moving average method. (4 Marks)

(ii) Using (i), calculate the Average Seasonal Variation based on the Additive Model of Time Series analysis. (4 Marks)

(iii) Using the values in (ii), determine the Adjusted Average Seasonal Variation for the Time Series Data. (4 Marks)

(iv) Prepare Seasonal Adjusted Forecast for Year 4 using the Additive Model. (4 Marks)

Answer

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Tags: Additive model, Forecasting, Seasonal Variation, Time series, Trend Analysis
Level: Level 1

Topic: Forecasting
Series: NOV 2015

IMAC – NOV 2023 – L1 – Q5 – Forecasting | Standard Costing and Variance Analysis

Determine the trend and seasonal variation in sales using moving averages and calculate factors for estimating direct material costs.

Question

a) A company operates from Monday to Friday. Sales data for the most recent three weeks as well as the moving total are as follows:

Day	Sales	Moving Total
Day 1	78
Day 2	83
Day 3	89	420
Day 4	85	430
Day 5	85	440
Day 6	88	450
Day 7	93	460
Day 8	99	470
Day 9	95	480
Day 10	95	490
Day 11	98	500
Day 12	103	510
Day 13	109	520
Day 14	105
Day 15	105

Required:
i) State the length of the cycle. (2 marks)
ii) Using the moving averages, establish the trend of the historical data above. (6 marks)
iii) Calculate the seasonal variation for each day of the week. (7 marks)

b) The value of variances as a control technique for management depends on the reliability and accuracy of the standard costs. If the standard costs are inaccurate, comparisons between actual cost and standard cost will have no meaning.

Required:
Explain TWO (2) factors to be considered by the purchasing department in estimating the direct material costs per unit of raw material. (5 marks)

Answer

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Tags: Direct Material Costs, Moving Averages, Sales Forecasting, Seasonal Variation
Level: Level 1

Topic: Forecasting, Standard Costing and Variance Analysis
Series: NOV 2023

IMAC – NOV 2019 – L1 – Q5 – Standard Costing and Variance Analysis

Calculate seasonal variation using moving averages and explain uses of standard costing.

Question

Question:
FB Logistics has been clearing containers from the port of Tema over the past seven years. Management is aware that the business has been facing seasonal fluctuations but there is no scientific basis for the determination of such variations that can be used to predict future revenue.

As a newly engaged Cost Accountant, you have been provided with some past quarterly performance over a three-year period. Details of the performance are shown below:

Year	1st Quarter	2nd Quarter	3rd Quarter	4th Quarter
1	120	140	160	180
2	180	160	185	210
3	150	200	230	220

Required:
Using quarterly moving average, calculate the seasonal variation for the company. (15 marks)

b) Standards as used in performance measurement are norms or benchmarks set for comparison purposes in performance evaluation.

Required:
Explain FIVE (5) uses of standard costing. (5 marks)

Answer

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Tags: Forecasting, Moving Average, Seasonal Variation, Standard Costing
Level: Level 1

Topic: Standard Costing and Variance Analysis

QTB – May 2017 – L1 – SA – Q14 – Statistics

This question involves identifying the estimable components of a time series.

Question

Answer

Find Related Questions by Tags, levels, etc.

Tags: Forecasting, Seasonal Variation, Time series, Trend
Level: Level 1

Topic: Statistics
Series: MAY 2017

MI – May 2024 – L1 – SB – Q1 – Forecasting Techniques-Analysis, Moving Averages

Analyze a time series and describe two models for estimating seasonal variation.

Question

a. Describe how a time series can be analyzed. (10 Marks)

b. “There are TWO models used to estimate seasonal variation.” List and briefly describe the TWO models. (10 Marks)

Answer

Find Related Questions by Tags, levels, etc.

Tags: Additive model, Moving Averages, Proportional Model, Seasonal Variation, Time series
Level: Level 1

Topic: Forecasting Techniques-Analysis, Moving Averages
Series: MAY 2024

QT – May 2019 – L1 – Q1a – Forecasting

Calculate centered trend values using moving average, determine seasonal variations, adjust variations, and forecast future clients using a multiplicative model.

Question

The number of clients who consulted Tsoo Consult within a period of three years were recorded as follows:

Year	Quarter 1	Quarter 2	Quarter 3	Quarter 4
1	75	70	75	80
2	95	85	80	65
3	100	105	115	90

Required:
a) Assuming a 4 quarterly cycle, calculate the centred trend values for the data by moving average method. (4 marks)

b) Using (a) above and the multiplicative model, calculate the average seasonal variations. (5 marks)

c) Using (b) above, calculate the adjusted average seasonal variations for the data. (5 marks)

d) Using the trend and the adjusted average seasonal variation, forecast the number of clients for Year 4 based on the multiplicative model. (6 marks)

Answer

Find Related Questions by Tags, levels, etc.

Tags: Forecasting, Moving Averages, Multiplicative Model, Seasonal Variation, Time series
Level: Level 1

Topic: Forecasting
Series: MAY 2019

QT – Nov 2018 – L1 – Q6b – Forecasting

Calculate the moving average, trend values, seasonal variation, and forecast membership.

Question

Year	1st Quarter	2nd Quarter	3rd Quarter	4th Quarter
Year 1	713	694	735	755
Year 2	767	733	766	780
Year 3	787	755	798	814
Year 4	816	790	826	843

Answer

Find Related Questions by Tags, levels, etc.

Tags: Forecasting, Least Squares, Membership Forecast, Moving Averages, Seasonal Variation, Time series
Level: Level 1

Topic: Forecasting
Series: NOV 2018

QT – Nov 2015 – L1 – Q1b – Forecasting

Calculate the trend, average seasonal variation, and forecast for Year 4 using a 4-point centered moving average method for Golden Tree Chocolate Bar sales.

Question

Quarterly sales of a Golden Tree Chocolate Bar at a popular mall in KoKoLand City are given as follows:

YEAR	QUARTER 1	QUARTER 2	QUARTER 3	QUARTER 4
1	1260	756	588	1596
2	1352	966	579	2028
3	1786	920	865	2273

Required:
(i) Calculate the trend in the series using a 4-point centered moving average method. (4 Marks)

(ii) Using (i), calculate the Average Seasonal Variation based on the Additive Model of Time Series analysis. (4 Marks)

(iii) Using the values in (ii), determine the Adjusted Average Seasonal Variation for the Time Series Data. (4 Marks)

(iv) Prepare Seasonal Adjusted Forecast for Year 4 using the Additive Model. (4 Marks)

Answer

Find Related Questions by Tags, levels, etc.

Tags: Additive model, Forecasting, Seasonal Variation, Time series, Trend Analysis
Level: Level 1

Topic: Forecasting
Series: NOV 2015

IMAC – NOV 2023 – L1 – Q5 – Forecasting | Standard Costing and Variance Analysis

Determine the trend and seasonal variation in sales using moving averages and calculate factors for estimating direct material costs.

Question

a) A company operates from Monday to Friday. Sales data for the most recent three weeks as well as the moving total are as follows:

Day	Sales	Moving Total
Day 1	78
Day 2	83
Day 3	89	420
Day 4	85	430
Day 5	85	440
Day 6	88	450
Day 7	93	460
Day 8	99	470
Day 9	95	480
Day 10	95	490
Day 11	98	500
Day 12	103	510
Day 13	109	520
Day 14	105
Day 15	105

Required:
Explain TWO (2) factors to be considered by the purchasing department in estimating the direct material costs per unit of raw material. (5 marks)

Answer

Find Related Questions by Tags, levels, etc.

Tags: Direct Material Costs, Moving Averages, Sales Forecasting, Seasonal Variation
Level: Level 1

Topic: Forecasting, Standard Costing and Variance Analysis
Series: NOV 2023

IMAC – NOV 2019 – L1 – Q5 – Standard Costing and Variance Analysis

Calculate seasonal variation using moving averages and explain uses of standard costing.

Question

As a newly engaged Cost Accountant, you have been provided with some past quarterly performance over a three-year period. Details of the performance are shown below:

Year	1st Quarter	2nd Quarter	3rd Quarter	4th Quarter
1	120	140	160	180
2	180	160	185	210
3	150	200	230	220

Required:
Using quarterly moving average, calculate the seasonal variation for the company. (15 marks)

b) Standards as used in performance measurement are norms or benchmarks set for comparison purposes in performance evaluation.

Required:
Explain FIVE (5) uses of standard costing. (5 marks)

Answer

Find Related Questions by Tags, levels, etc.

Tags: Forecasting, Moving Average, Seasonal Variation, Standard Costing
Level: Level 1

Topic: Standard Costing and Variance Analysis

Question Tag: Seasonal Variation

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QTB – May 2017 – L1 – SA – Q14 – Statistics

This question involves identifying the estimable components of a time series.

MI – May 2024 – L1 – SB – Q1 – Forecasting Techniques-Analysis, Moving Averages

Analyze a time series and describe two models for estimating seasonal variation.

QT – May 2019 – L1 – Q1a – Forecasting

Calculate centered trend values using moving average, determine seasonal variations, adjust variations, and forecast future clients using a multiplicative model.

QT – Nov 2018 – L1 – Q6b – Forecasting

Calculate the moving average, trend values, seasonal variation, and forecast membership.

QT – Nov 2015 – L1 – Q1b – Forecasting

Calculate the trend, average seasonal variation, and forecast for Year 4 using a 4-point centered moving average method for Golden Tree Chocolate Bar sales.

IMAC – NOV 2023 – L1 – Q5 – Forecasting | Standard Costing and Variance Analysis

Determine the trend and seasonal variation in sales using moving averages and calculate factors for estimating direct material costs.

IMAC – NOV 2019 – L1 – Q5 – Standard Costing and Variance Analysis

Calculate seasonal variation using moving averages and explain uses of standard costing.

QTB – May 2017 – L1 – SA – Q14 – Statistics

This question involves identifying the estimable components of a time series.

MI – May 2024 – L1 – SB – Q1 – Forecasting Techniques-Analysis, Moving Averages

Analyze a time series and describe two models for estimating seasonal variation.

QT – May 2019 – L1 – Q1a – Forecasting

Calculate centered trend values using moving average, determine seasonal variations, adjust variations, and forecast future clients using a multiplicative model.

QT – Nov 2018 – L1 – Q6b – Forecasting

Calculate the moving average, trend values, seasonal variation, and forecast membership.

QT – Nov 2015 – L1 – Q1b – Forecasting

Calculate the trend, average seasonal variation, and forecast for Year 4 using a 4-point centered moving average method for Golden Tree Chocolate Bar sales.

IMAC – NOV 2023 – L1 – Q5 – Forecasting | Standard Costing and Variance Analysis

Determine the trend and seasonal variation in sales using moving averages and calculate factors for estimating direct material costs.

IMAC – NOV 2019 – L1 – Q5 – Standard Costing and Variance Analysis

Calculate seasonal variation using moving averages and explain uses of standard costing.

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Question Tag: Seasonal Variation

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QTB – May 2017 – L1 – SA – Q14 – Statistics

This question involves identifying the estimable components of a time series.

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MI – May 2024 – L1 – SB – Q1 – Forecasting Techniques-Analysis, Moving Averages

Analyze a time series and describe two models for estimating seasonal variation.

You're reporting an error for "MI – May 2024 – L1 – SB – Q1 – Forecasting Techniques-Analysis, Moving Averages"

QT – May 2019 – L1 – Q1a – Forecasting

Calculate centered trend values using moving average, determine seasonal variations, adjust variations, and forecast future clients using a multiplicative model.

You're reporting an error for "QT – May 2019 – L1 – Q1a – Forecasting"

QT – Nov 2018 – L1 – Q6b – Forecasting

Calculate the moving average, trend values, seasonal variation, and forecast membership.

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QT – Nov 2015 – L1 – Q1b – Forecasting

Calculate the trend, average seasonal variation, and forecast for Year 4 using a 4-point centered moving average method for Golden Tree Chocolate Bar sales.

You're reporting an error for "QT – Nov 2015 – L1 – Q1b – Forecasting"

IMAC – NOV 2023 – L1 – Q5 – Forecasting | Standard Costing and Variance Analysis

Determine the trend and seasonal variation in sales using moving averages and calculate factors for estimating direct material costs.

You're reporting an error for "IMAC – NOV 2023 – L1 – Q5 – Forecasting | Standard Costing and Variance Analysis"

IMAC – NOV 2019 – L1 – Q5 – Standard Costing and Variance Analysis

Calculate seasonal variation using moving averages and explain uses of standard costing.

You're reporting an error for "IMAC – NOV 2019 – L1 – Q5 – Standard Costing and Variance Analysis"

QTB – May 2017 – L1 – SA – Q14 – Statistics

This question involves identifying the estimable components of a time series.

You're reporting an error for "QTB – May 2017 – L1 – SA – Q14 – Statistics"

MI – May 2024 – L1 – SB – Q1 – Forecasting Techniques-Analysis, Moving Averages

Analyze a time series and describe two models for estimating seasonal variation.

You're reporting an error for "MI – May 2024 – L1 – SB – Q1 – Forecasting Techniques-Analysis, Moving Averages"

QT – May 2019 – L1 – Q1a – Forecasting

Calculate centered trend values using moving average, determine seasonal variations, adjust variations, and forecast future clients using a multiplicative model.

You're reporting an error for "QT – May 2019 – L1 – Q1a – Forecasting"

QT – Nov 2018 – L1 – Q6b – Forecasting

Calculate the moving average, trend values, seasonal variation, and forecast membership.

You're reporting an error for "QT – Nov 2018 – L1 – Q6b – Forecasting"

QT – Nov 2015 – L1 – Q1b – Forecasting

Calculate the trend, average seasonal variation, and forecast for Year 4 using a 4-point centered moving average method for Golden Tree Chocolate Bar sales.

You're reporting an error for "QT – Nov 2015 – L1 – Q1b – Forecasting"

IMAC – NOV 2023 – L1 – Q5 – Forecasting | Standard Costing and Variance Analysis

Determine the trend and seasonal variation in sales using moving averages and calculate factors for estimating direct material costs.

You're reporting an error for "IMAC – NOV 2023 – L1 – Q5 – Forecasting | Standard Costing and Variance Analysis"

IMAC – NOV 2019 – L1 – Q5 – Standard Costing and Variance Analysis

Calculate seasonal variation using moving averages and explain uses of standard costing.

You're reporting an error for "IMAC – NOV 2019 – L1 – Q5 – Standard Costing and Variance Analysis"

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