Regression – NBC Institute

PM – May 2024 – L2 – SB – Q4 – Environmental and Social Performance Management

Forecast sales with seasonality adjustments and regression analysis to produce cash forecasts.

Question

Some time ago Robert launched a new product. At first, sales were good, but now the figures are causing concern. Robert wants a more accurate sales forecast to produce detailed cash forecasts.

Since there is some seasonality present in the raw data, the series for sales shown below represents the underlying trend based on an averaging process:

On average, quarters 1 and 3 are 5% and 6% respectively above trend, while quarters 2 and 4 are respectively 2% and 9% below trend.

Some preliminary calculations on the above ten observations have been carried out and the results are summarized below:

Required:
a. Forecast the sales for the next two years, adjusting for seasonality. (12 Marks)
b. Discuss the importance of seasonality adjustments in sales forecasting. (4 Marks)
c. Explain how Robert could use the sales forecasts to produce detailed cash forecasts. (4 Marks)

Answer

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Tags: Cash Forecasting, Regression, Sales Forecasting, Seasonality, Trend Analysis
Level: Level 2

Topic: Environmental and Social Performance Management
Series: MAY 2024

QTB – MAY 2016 – L1 – SA – Q16 – Statistics

Identify the statistical measure used to determine the degree of association between variables

Question

Which of the following is used to measure the degree of association between two variables?

A. Regression

B. Percentiles

C. Quartiles

D. Correlation E. Coefficient of variation

Answer

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Tags: Correlation, Regression, Statistical Analysis
Level: Level 1

Topic: Statistics
Series: MAY 2016

QTB – May 2015 – L1 – SA – Q9 – Statistics

Identifying the dependent and independent variables in regression analysis.

Question

A sales manager of a manufacturing company is to predict the sales of some products for given expenditures on advertising using a simple linear regression analysis. In the fitted regression equation, the sales and advertising expenditure are respectively ____________ and ________________ variables.

A. Independent, dependent
B. Dependent, independent
C. Fixed, dependent
D. Dependent, fixed
E. Nominal, categorical

Answer

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Tags: Regression, Sales Forecasting, Variables
Level: Level 1

Series: MAY 2015

QT – Nov 2016 – L1 – Q3 – Forecasting

Derive a regression forecasting equation for iron rod demand based on construction permits and analyze the results.

Question

The Branch Manager of a building material production plant feels that the demand for iron rod shipments may be related to the number of construction permits issued in the country during the previous quarter. The Manager has collected the data shown in the table below:

Construction Permits	Iron Rods
15	6
9	4
40	16
20	6
25	13
25	9
15	10
35	16

Required:

i) Use the normal equations of the least square regression method to derive a regression forecasting equation for the data. (9 marks)
ii) Interpret your regression coefficient in (i) above. (1 mark)
iii) Using the regression line in (a) above, determine a point estimate for Iron Rods when the number of construction permits is 30. (2 marks)
iv) Is your estimate in (iii) above reliable? Give reason(s) for your answer. (2 marks)
v) Calculate the coefficient of determination and interpret it. (6 marks)

Answer

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Tags: Construction Permits, Forecasting, Iron Rods, Regression
Level: Level 1

Topic: Forecasting
Series: NOV 2016

QT – May 2018 – L1 – Q2 – Forecasting

Develop a regression model for energy consumption based on temperature and calculate the standard error of estimate.

Question

A Ghanaian student studying abroad wants to develop an empirical model for energy consumption (in kilowatts per day) as a function of the daily high temperature (in degrees Celsius) in winter. For nine days the following information was obtained:

Temperature (°C)	-0.4	-0.2	0.3	0.8	1.1	1.4	1.8	2.1	2.5
Energy used (kW)	28	30	26	25	26	26	27	26	22

Required:
a) Identify the response and predictor variables, based on the purpose for developing the regression model. (2 marks)
b) Determine the coefficient of regression and the regression constant. Give your values to 2 decimal places. (5 marks)
c) Interpret your results in b) above. (3 marks)
d) Write the equation of the regression line of energy use on temperature in the form $y = a + b x$ . (2 marks)
e) Estimate the student’s daily energy consumption when the daily high temperature is 2 degrees Celsius. (2 marks)
f) Determine the standard error of estimate. (6 marks)

Answer

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Tags: Energy Consumption, Regression, Standard Error of Estimate, Temperature
Level: Level 1

Topic: Forecasting
Series: MAY 2018

QT – Nov 2018 – L1 – Q1a – Forecasting

Explain the term coefficient of correlation in forecasting.

Question

Explain the term Coefficient of Correlation in forecasting.

Answer

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Tags: Coefficient of Correlation, Forecasting, Regression
Level: Level 1

Topic: Forecasting
Series: NOV 2018

PM – May 2024 – L2 – SB – Q4 – Environmental and Social Performance Management

Forecast sales with seasonality adjustments and regression analysis to produce cash forecasts.

Question

Some time ago Robert launched a new product. At first, sales were good, but now the figures are causing concern. Robert wants a more accurate sales forecast to produce detailed cash forecasts.

Since there is some seasonality present in the raw data, the series for sales shown below represents the underlying trend based on an averaging process:

On average, quarters 1 and 3 are 5% and 6% respectively above trend, while quarters 2 and 4 are respectively 2% and 9% below trend.

Some preliminary calculations on the above ten observations have been carried out and the results are summarized below:

Answer

Find Related Questions by Tags, levels, etc.

Tags: Cash Forecasting, Regression, Sales Forecasting, Seasonality, Trend Analysis
Level: Level 2

Topic: Environmental and Social Performance Management
Series: MAY 2024

QTB – MAY 2016 – L1 – SA – Q16 – Statistics

Identify the statistical measure used to determine the degree of association between variables

Question

Which of the following is used to measure the degree of association between two variables?

A. Regression

B. Percentiles

C. Quartiles

D. Correlation E. Coefficient of variation

Answer

Find Related Questions by Tags, levels, etc.

Tags: Correlation, Regression, Statistical Analysis
Level: Level 1

Topic: Statistics
Series: MAY 2016

QTB – May 2015 – L1 – SA – Q9 – Statistics

Identifying the dependent and independent variables in regression analysis.

Question

A. Independent, dependent
B. Dependent, independent
C. Fixed, dependent
D. Dependent, fixed
E. Nominal, categorical

Answer

Find Related Questions by Tags, levels, etc.

Tags: Regression, Sales Forecasting, Variables
Level: Level 1

Series: MAY 2015

QT – Nov 2016 – L1 – Q3 – Forecasting

Derive a regression forecasting equation for iron rod demand based on construction permits and analyze the results.

Question

Construction Permits	Iron Rods
15	6
9	4
40	16
20	6
25	13
25	9
15	10
35	16

Required:

Answer

Find Related Questions by Tags, levels, etc.

Tags: Construction Permits, Forecasting, Iron Rods, Regression
Level: Level 1

Topic: Forecasting
Series: NOV 2016

QT – May 2018 – L1 – Q2 – Forecasting

Develop a regression model for energy consumption based on temperature and calculate the standard error of estimate.

Question

Temperature (°C)	-0.4	-0.2	0.3	0.8	1.1	1.4	1.8	2.1	2.5
Energy used (kW)	28	30	26	25	26	26	27	26	22

Answer

Find Related Questions by Tags, levels, etc.

Tags: Energy Consumption, Regression, Standard Error of Estimate, Temperature
Level: Level 1

Topic: Forecasting
Series: MAY 2018

QT – Nov 2018 – L1 – Q1a – Forecasting

Explain the term coefficient of correlation in forecasting.

Question

Explain the term Coefficient of Correlation in forecasting.

Answer

Find Related Questions by Tags, levels, etc.

Tags: Coefficient of Correlation, Forecasting, Regression
Level: Level 1

Topic: Forecasting
Series: NOV 2018

Question Tag: Regression

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PM – May 2024 – L2 – SB – Q4 – Environmental and Social Performance Management

Forecast sales with seasonality adjustments and regression analysis to produce cash forecasts.

QTB – MAY 2016 – L1 – SA – Q16 – Statistics

Identify the statistical measure used to determine the degree of association between variables

QTB – May 2015 – L1 – SA – Q9 – Statistics

Identifying the dependent and independent variables in regression analysis.

QT – Nov 2016 – L1 – Q3 – Forecasting

Derive a regression forecasting equation for iron rod demand based on construction permits and analyze the results.

QT – May 2018 – L1 – Q2 – Forecasting

Develop a regression model for energy consumption based on temperature and calculate the standard error of estimate.

QT – Nov 2018 – L1 – Q1a – Forecasting

Explain the term coefficient of correlation in forecasting.

PM – May 2024 – L2 – SB – Q4 – Environmental and Social Performance Management

Forecast sales with seasonality adjustments and regression analysis to produce cash forecasts.

QTB – MAY 2016 – L1 – SA – Q16 – Statistics

Identify the statistical measure used to determine the degree of association between variables

QTB – May 2015 – L1 – SA – Q9 – Statistics

Identifying the dependent and independent variables in regression analysis.

QT – Nov 2016 – L1 – Q3 – Forecasting

Derive a regression forecasting equation for iron rod demand based on construction permits and analyze the results.

QT – May 2018 – L1 – Q2 – Forecasting

Develop a regression model for energy consumption based on temperature and calculate the standard error of estimate.

QT – Nov 2018 – L1 – Q1a – Forecasting

Explain the term coefficient of correlation in forecasting.

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PM – May 2024 – L2 – SB – Q4 – Environmental and Social Performance Management

Forecast sales with seasonality adjustments and regression analysis to produce cash forecasts.

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QTB – MAY 2016 – L1 – SA – Q16 – Statistics

Identify the statistical measure used to determine the degree of association between variables

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QTB – May 2015 – L1 – SA – Q9 – Statistics

Identifying the dependent and independent variables in regression analysis.

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QT – Nov 2016 – L1 – Q3 – Forecasting

Derive a regression forecasting equation for iron rod demand based on construction permits and analyze the results.

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QT – May 2018 – L1 – Q2 – Forecasting

Develop a regression model for energy consumption based on temperature and calculate the standard error of estimate.

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QT – Nov 2018 – L1 – Q1a – Forecasting

Explain the term coefficient of correlation in forecasting.

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PM – May 2024 – L2 – SB – Q4 – Environmental and Social Performance Management

Forecast sales with seasonality adjustments and regression analysis to produce cash forecasts.

You're reporting an error for "PM – May 2024 – L2 – SB – Q4 – Environmental and Social Performance Management"

QTB – MAY 2016 – L1 – SA – Q16 – Statistics

Identify the statistical measure used to determine the degree of association between variables

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QTB – May 2015 – L1 – SA – Q9 – Statistics

Identifying the dependent and independent variables in regression analysis.

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QT – Nov 2016 – L1 – Q3 – Forecasting

Derive a regression forecasting equation for iron rod demand based on construction permits and analyze the results.

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QT – May 2018 – L1 – Q2 – Forecasting

Develop a regression model for energy consumption based on temperature and calculate the standard error of estimate.

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QT – Nov 2018 – L1 – Q1a – Forecasting

Explain the term coefficient of correlation in forecasting.

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