General

Question 1

r

Answer 1

the coefficient of correlation
1 = +ve
2 = -ve

Question 2

r2

Answer 2

the coefficient of determination - the measure of the proportion of change in one variable that can be explained variations in the value of another variable

HIGH = very likely that variations in one variable cause variations in the other

Question 3

Time Series analysis

Answer 3

TS = a set of data observed over a period of time ie. sales patterns by week, month, year

Trend - underlying LT movement over time
Seasonal variation - short-term periodic fluctuations
Cyclical variation - longer-term fluctuations (ie economic activity, economic cycles)
Random variation - unpredictable fluctuations (ie act of nature)

ADDITIVE MODEL TS = T + S (trend + seasonal) - used where the components are assumed to add together to give the time series (TS) - quoted in absolute terms

MULTIPLICATE MODEL TS = T x S - % or fraction of the trend

Question 4

Alternative approaches to budgeting - imposed/top down, bottom up/participative, incremental, zero-based, rolling

Answer 4

Incremental = the budget for each period is based on CY results and modified for changes in activity levels

Zero-based = each budget is built up from scratch (starting point for CY overheads = 0)

Rolling budget = a new ac period is added as each old one expires (more accurate)

Question 5

Alternative budget structures

Answer 5

Product based - separate production budget established for product A, B, C etc as well as a separate marketing cost budget, dis budget etc

Responsibility based - segregate budgeted revenues + costs into areas of personal responsibility (pos motivational impact so long as they are only responsible for budgets which they have control over)

Activity based - based on a framework of activities + cost drivers are used as a basis for preparing budgets (rate per cost driver * quantity) - involves defining the costs that underlie the financial figures in each function. The level of activity in terms of cost drivers is used to decide how much resources should be allocated + how well the activity is being managed.

Question 6

Big data characteristics

Answer 6

Volume, variety (structured / unstructured), velocity (speed), veracity (accuracy)

Challenge:
Keep data clean + free from bias (consider source)
Merge structured + unstructured to reveal new insights

Benefits:

• forecasting demand
• identifying customer preferences

Problems:

• lack of forecasting tools: the sheer volume, complexity + speed of Big Data means that traditional forecasting tools cannot cope
• privacy (potential to harm individuals
• security
• incorrect data (potentially) harmful if results in incorrect conclusions being reached)
• lack of skilled data analysts: requires skilled data analysts