Variance exists when there is a difference between the budgeted figures and the actual outcome.
Variance = Actual outcome − Budgeted outcome
Favourable variance exists when discrepancies are financially beneficial to the organisation (i.e., when the actual figures are higher than the).
Adverse variance exist when the discrepancies are financially detrimental to the organisation. They occur when actual costs are higher than expected, or when actual revenue is lower than budgeted (i.e., underselling).
Frequently Asked Questions About Variance Analysis (Accounting & Statistics)
What is Variance Analysis (in accounting/finance)?
In accounting and finance, variance analysis is the process of comparing planned or budgeted results to actual results, identifying the differences (variances), and investigating the reasons for those differences. It's a key tool for cost control, performance evaluation, and decision-making.
What does Variance Analysis mean in accounting?
It means examining how actual financial outcomes (like costs or revenues) differ from expected or standard outcomes. For example, comparing the actual cost of raw materials used to the budgeted cost, or comparing actual sales revenue to forecasted sales revenue. Analyzing these variances helps management understand what went right or wrong and take corrective action.
What is Analysis of Variance (ANOVA) (in statistics)?
In statistics, Analysis of Variance (ANOVA) is a collection of statistical models and their associated estimation procedures used to analyze the differences among group means in a sample. It tests whether there is a significant difference between the means of three or more independent groups. It works by comparing the variance within each group to the variance between the group means.
What is the difference between accounting Variance Analysis and statistical Analysis of Variance (ANOVA)?
They are completely different applications of the term "variance":
- Accounting Variance Analysis: Compares actual financial performance to planned performance (budget/standard costs) using subtraction. The "variance" is the difference amount.
- Statistical Analysis of Variance (ANOVA): Compares the *means* of different groups by analyzing the *spread* or variability (variance) within and between those groups using statistical tests. The "variance" here refers to the statistical measure of data dispersion.
Why is Variance Analysis important (in accounting)?
Variance analysis is important for:
- Performance Evaluation: Assessing the efficiency of operations and managers.
- Cost Control: Identifying areas where costs are exceeding expectations.
- Decision Making: Providing information to make adjustments to operations, pricing, or future budgets.
- Continuous Improvement: Highlighting areas for operational efficiency improvements.
- Planning: Improving the accuracy of future budgets and forecasts.
When is Analysis of Variance (ANOVA) used (in statistics)?
ANOVA is used when you want to compare the means of three or more independent groups to see if at least one group mean is statistically different from the others. For example:
- Comparing the average test scores of students taught using three different methods.
- Comparing the average yield of crops grown with four different fertilizers.
- Comparing the average response times of people using three different website designs.
What is One-Way Analysis of Variance?
One-Way ANOVA (or one-factor ANOVA) is the simplest type of ANOVA. It is used when you have one categorical independent variable (factor) with three or more levels (groups) and one continuous dependent variable, and you want to test if the means of the dependent variable are different across the levels of the independent variable.
What is Two-Way Analysis of Variance or Factorial ANOVA?
Two-Way ANOVA (or two-factor ANOVA) is used when you have two categorical independent variables (factors) and one continuous dependent variable. It allows you to test if there are main effects for each factor and if there is an interaction effect between the two factors on the dependent variable.
