Data Analysis & Business Modelling

Distinguish raw data, information, and business insight in a given scenario.

Classify a business question as descriptive, predictive, or prescriptive analytics.

Represent a business problem with a visual input-process-output diagram that identifies assumptions and performance measures.

Create a flowchart that applies the quantitative model-development process to a small business problem.

Organize a business dataset in Excel using consistent rows, columns, headers, and data types.

Use relative, absolute, and mixed cell references correctly in spreadsheet formulas.

Audit a spreadsheet model by separating inputs, calculations, and outputs and tracing common formula errors.

Sort and filter a tabular dataset in Excel using single-level and multi-level criteria.

Classify variables as categorical or numerical and identify their measurement scales.

Clean a small Excel dataset by resolving missing values, duplicate records, and inconsistent data types.

Construct frequency and relative-frequency tables from business data.

Calculate and interpret the mean, median, and mode for a business dataset.

Calculate and interpret the range, variance, standard deviation, and coefficient of variation.

Calculate quartiles and use the interquartile range to identify potential outliers.

Select and create an appropriate Excel chart for a stated business question.

Create an Excel PivotTable with grouped categories, filters, and subtotals.

Calculate empirical probabilities from a frequency table or contingency table.

Use a Venn diagram to apply the complement and addition rules to business probability problems.

Use a probability tree or contingency table to calculate conditional probabilities and apply the multiplication rule.

Determine whether two events are independent using given probabilities.

Calculate the expected value of a discrete random variable.

Calculate the variance and standard deviation of a discrete random variable.

Calculate binomial probabilities with Excel functions and interpret the result.

Standardize a normally distributed value and calculate a corresponding probability with Excel.

Create a scatterplot and describe the direction, form, and strength of a relationship.

Calculate and interpret the correlation coefficient between two numerical variables.

Estimate a simple linear regression equation using Excel functions or a trendline.

Interpret the slope and intercept of a regression equation in business context.

Use a fitted regression equation to predict an outcome within the observed data range.

Calculate regression residuals and create a residual plot in Excel.

Interpret the coefficient of determination as the proportion of variation explained by a model.

Explain why a fitted association does not by itself establish a causal relationship.

Identify the response variable and candidate predictor variables in a business problem.

Fit a multiple linear regression model using Excel or a SIMnet-supported workflow.

Interpret a multiple-regression coefficient while holding the other predictors constant.

Represent a two-category qualitative predictor with a dummy variable.

Compare models using R-squared and adjusted R-squared.

Use regression p-values to identify predictors with evidence of a linear association.

Recognize multicollinearity from strong relationships among predictor variables.

Generate a prediction from a multiple-regression model and state its limitations.

Plot time-series data and identify level, trend, seasonality, and irregular variation.

Produce a naïve forecast and calculate its forecast errors.

Produce a moving-average forecast for a specified window length.

Produce an exponential-smoothing forecast for a specified smoothing constant.

Calculate MAD, MSE, and MAPE and use them to compare forecasting models.

Fit a linear trend model and forecast a future period.

Calculate seasonal indices from a repeating seasonal pattern.

Produce a seasonally adjusted forecast using trend and seasonal indices.

Use IF and nested logical functions to encode a business rule in Excel.

Use SUMIFS, COUNTIFS, and AVERAGEIFS to summarize records that meet stated criteria.

Use a lookup function to retrieve matching information from a business table.

Apply text and date functions to standardize imported business data.

Apply Excel data-validation rules to prevent invalid data entry.

Use Goal Seek through Excel desktop or SIMnet to determine the input required to reach a specified output.

Transform and combine data using a Power Query or SIMnet-supported workflow.

Record, run, and inspect a simple Excel macro using a SIMnet or Windows-supported workflow.

Define decision variables for a linear optimization problem.

Express a business objective as a linear objective function.

Translate resource limits and business requirements into linear constraints.

Graph a two-variable feasible region and identify its corner points.

Build and configure a linked Excel Solver or SIMnet Solver model with an objective, changing cells, and constraints.

Interpret the optimal decision values and objective value returned by a Solver model.

Identify binding constraints and calculate slack from an optimization solution.

Interpret shadow prices and allowable changes from a linear-programming sensitivity report.

Identify entities, attributes, records, and fields in a business scenario.

Select suitable primary and foreign keys for relational tables.

Identify one-to-one, one-to-many, and many-to-many relationships.

Resolve a many-to-many relationship by introducing a junction table.

Restructure a redundant flat dataset into related tables with atomic fields and reduced duplication.

Draw a relational schema showing tables, keys, and cardinalities.

Explain how referential integrity prevents invalid foreign keys and orphan records.

Specify field-validation rules and permitted domains for database attributes.

Create Access tables with appropriate field names, data types, properties, and primary keys using Access or SIMnet.

Create table relationships and enforce referential integrity using Access or SIMnet.

Create a select query with criteria, sorting, and selected fields using Access or SIMnet.

Create a query that joins records from related tables using Access or SIMnet.

Create a query with a calculated field using Access or SIMnet.

Create an aggregate query using grouping and summary functions in Access or SIMnet.

Build a data-entry form based on a table or query using Access or SIMnet.

Build a grouped and sorted report based on a table or query using Access or SIMnet.

Distinguish rule-based automation, machine learning, and generative AI in business applications.

Identify business-analysis tasks for which AI assistance is suitable or unsuitable.

Write a structured prompt that states the business context, available data, constraints, and required output.

Use Copilot in a SIMnet simulation, or an equivalent AI assistant, to propose an Excel formula for a stated business task.

Validate an AI-generated formula or analytical claim against source data and independent calculations.

Identify hallucination, bias, and unsupported assumptions in an AI-generated analysis.

Remove or anonymize sensitive information before using an AI system.

Distinguish decision making under certainty, risk, and uncertainty.

Construct a payoff table from decision alternatives and possible states of nature.

Apply maximax, maximin, and minimax-regret criteria to a payoff table.

Calculate expected monetary value for each decision alternative.

Calculate the expected value of perfect information.

Construct and solve a decision tree containing decision and chance nodes.

Test how changes in probabilities or payoffs affect a recommended decision.

Select and justify an appropriate descriptive, predictive, prescriptive, database, or AI-supported method for a business scenario.