Data chapter 5

descriptive analytics

What happened? What is happening?

Diagnostic analytics

Why did it happen? What are the reasons for the past results?

predictive analytics

Will it happen in the future? Is ti forecastable

prescriptive analytics

What should we do, based on what we expect will happen

descriptive analytics techniques

counts, totals, sums, averages, subtotals, minimum, maximum, histograms, pivot tables, standard deviations, and ratio analysis.

counts

show how frequently an attempt occurs

totals, sums, averages, subtotals

summarize measures of performance

minimums, maximums, medians, standard deviation

summarize measures showing extreme values to gelp explain what happened

Graphs (bar charts), histograms

Summarizes all the accounts receivable still owing

Benford’s law

A type of analytics performed to find anomalies/outliers. The principle that in any large, randomly produced set of natural numbers, there is an expected distribution of the first, or leading, digit with there being more 1s, 2s next most and down successively to the number 9.

Internal controls testing

evaluate internal controls for cash disbursements and check for appropriate authorization.

Fuzzy matching

check addresses of employees and vendors and see if they are similar

sequence checks and sequence analysis

Check to see if any checks or purchase orders are missing by running a sequence check to make sure all checks are accounted for

Duplicate transactions

check all amounts, dates, and vendors to see if duplicate transactions were erroneously recorded.

Variance analysis

Using standard costs, evaluate the labor use and labor rate and see if it is different than we predicted

cash/ bank reconciliation

compare transactions from our general ledger cash account to the transactions reported by the bank

techniques for performing Drill-Down analytics

uncover the details by summarizing the data at different levels. Use of cross-tabulations to view transactions from a different perspective

test approach determines relations/patterns/linkages between variables

correlation, regression, and hypothesis testing

correlation test

run correlation analysis to assess the relationship between advertising expense and sales revenue

regression

run regression analysis to assess the relationship between R&D expenditures and captial expenditures and net income

hypothesis testing

statistically compare the mean sales returns during the holiday season with the mean sales during the nonholiday season

time series analysis

A tool/technique used to predict future values based on past values of the same variable.

classification

a predictive analytics technique used to separate or classify a sample into two or more groups of glasses

regression

a predictive analytics technique used to predict a specific dependent variable outcome value based on independent variable inputs

Forecasting

A predictive analytics technique used to predict future values based on past values of the same variable

Diagnostic techniques

hypothesis testing, variances, differences from expectations, correlations, visualize and cluster, identify outliers/anomalies, drill- downs and roll ups, pivot tables, sequence check

predictive techniques

classification, regression, time series.

prescriptive techniques

marginal analysis, cash flow analysis, scenario analysis, goal- seek analysis, sensitivity analysis,optimization.

p-value > alpha

fail to reject the null hypothesis

p value <= alpha

reject the null hypothesis

confidence interval

The probability that a population parameter will fall between two set values.

null hypothesis

An assumption that the hypothesized relationship does not exist, that there is no significant difference between two samples or populations.

alternative hypothesis

A hypothesis used in hypothesis testing that is opposite of the null hypothesis, a potential result that the analyst may expect.

p- value is compared to the

alpha threshold