Business Statistics & Data Analysis for Decision Making

Explore how business statistics and data analysis drive decision making by using descriptive statistics, visualizations, and forecasting with regression and time series, across data types, population and sampling.

Define the business problem, collect and clean data, and standardize formats to prepare for descriptive analytics. Visualize insights, report findings, apply predictive analytics, and measure impact with KPIs.

Learn to describe data with mean, median, mode, range, variance, and standard deviation to reveal typical values, dispersion, and patterns for business decisions.

Learn how to determine the median by sorting data, choosing the middle value for odd counts, or averaging the two middle values for even counts, with an Excel example.

Identify the mode, the most frequent value, especially in categorical data where mean and median fall short. See how Excel's mode and mode.dot.mult reveal modes with cup sizes as examples.

Explore dispersion by measuring the range—the difference between the highest and lowest values—to assess sales volatility, shown through a Brew and Bean case study in Excel.

Study variance as a measure of dispersion, comparing population variance and sample variance using x_i, x-bar, n, Excel var.p/var.s. High variance signals volatility; low variance indicates data clustering around mean.

Explore standard deviation as a key measure of dispersion, showing how it links variance to data units, interprets volatility, and flags outliers using Excel formulas stdev.s and stdev.p.

Learn how frequency distribution summarizes data by classifying observations into continuous intervals, counting frequencies, and using this table to plan inventory, staffing, and offers, with real-world examples and Excel steps.

Convert a frequency distribution to a histogram to visualize data spread across intervals, then use descriptive statistics: mean, median, mode, variance, and standard deviation to inform data-driven decisions.

Learn how bar charts quickly compare categories and reveal sales patterns, turning data into actionable insights with tables, pivot tables, and cross tabulations.

Explore line charts to track values over time, spot trends and seasonality, and compare regions with single or multiple charts created in Excel for forecasting.

Explore how pie charts show each slice’s percentage of the whole to identify dominant categories in budgets and revenue. Learn to create pie charts in Excel with percentage labels.

Tables present raw numbers in rows and columns for precise data in finance, operations, and budgeting, the foundation of most business reports; derive charts from tables to show trends.

Explore pivot tables to summarize and aggregate data from detailed tables, enabling multidimensional analysis, comparisons across categories and channels, with a practical excel case study.

Explore trend analysis for identifying patterns in time series data using trends, seasonality, and anomalies; apply to forecasts and data-driven decisions, illustrated by Glow Skin's rising revenue and May dip.

Identify seasonality as regular, calendar-driven patterns in data caused by weather, holidays, and consumer behavior; apply it to inventory planning, marketing, staffing, and sales forecasting.

Identify anomalies as outliers, data points that stand out from surrounding values and signal unusual events. Analyze trend deviations, diagnose root causes, and guide actions with dashboards and decision-making visuals.

learn how simple linear regression links advertising spend to sales to forecast future outcomes, using regression analysis and r-squared to gauge model fit.

Explore multiple linear regression, extending simple regression to include two or more predictors, and use excel's data analysis toolpak to estimate the equation y equals intercept plus coefficients times x.

Explore polynomial regression to model curved relationships, using scatter plots and Excel trendlines to predict customer retention from workout frequency, with r-squared guiding model choice.

Forecast with moving averages to predict future values from past data and distinguish it from regression. Smooth fluctuations, spot trends, and use bar charts, line graphs, and pivot tables.