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Week one introduces business forecasting, with an exercise on plotting a series and describing its pattern before any method is applied.

Week two covers the forecasting process and data considerations, and its exercise usually asks for summary statistics, autocorrelation and a first judgment about trend and seasonality.

Week three brings moving averages and exponential smoothing, including Holt's and Winters' methods for trend and seasonal data.

Week four introduces simple linear regression, and week five extends it to multiple regression with several explanatory variables.

Week six covers time series decomposition, separating trend, seasonal and cyclical parts before rebuilding a forecast from them.

Week seven, on chapter eight, looks at combining forecasts, and week eight closes with chapters nine and twelve on data mining and implementation.

Every week also carries a 15-point forum and a 10-point quiz, and exams fall in weeks two, three, five and eight.

Read together, the weeks climb from describing data to modeling it, which is why an early weak exercise tends to make the regression weeks harder.

Each exercise starts with the data set from your Canvas module and the chapter's method, never a generic template.

Each BUSI 405 workbook follows an analyst's habits: inputs on one sheet, calculations on another, a chart of actual against forecast and a table of error measures.

The written part explains the choice of method, what the accuracy numbers mean and what the forecast says for the business.

Holdout periods are used where the chapter calls for them, so comparisons between models are fair.

Forum threads take the week's technique and apply it to an example from retail, healthcare, logistics or finance, and each reply picks up a point a classmate raised about their own series.

You receive each item early enough to read it, ask a question and upload it before the due time.

Nothing is posted or submitted for you; Canvas stays in your hands.

Where the data set has missing months or a one-time spike, the workbook flags it, shows how it was handled and notes how the choice affects the forecast.

The coursework is done by an analyst who forecasts in industry, with a background in business analytics, economics or statistics.

They work in Excel daily and know the shortcuts that keep a forecasting workbook clean, from named ranges to the Data Analysis ToolPak for regression output.

A second analyst rechecks every workbook, so a wrong lag or a mistyped smoothing constant is caught before delivery.

The same analyst stays on your course for all eight weeks where possible, so the workbooks look consistent.

Your identity sits with the coordinator, not the analyst.

If a quiz is coming and a concept is unclear, such as why adjusted R-squared matters, you can ask for a short explanation.

Any faith prompt in a forum, such as honesty when presenting a forecast, is answered on topic and with respect.

Their day-to-day work includes explaining a forecast to people who do not care about the statistics, which is why the written parts of each exercise stay short and clear.

Holt's and Winters' methods in week three are the first real wall, because each adds a parameter and the results swing with the values chosen.

Regression output is next. Students report the numbers without saying what they mean, and the exercise rubric rewards interpretation.

Multiple regression adds the problems of correlated predictors and dummy variables for seasons, which are easy to set up wrongly.

Decomposition in week six asks for seasonal indexes that must average to one, and small arithmetic slips spread through every month.

Combining forecasts in week seven asks for weights, and choosing them sensibly needs the error measures from earlier weeks.

The exam weeks overlap with exercises, so weeks two, three and five carry three graded items each.

Data mining in the last week introduces new vocabulary at the very end, when energy is lowest.

The forums can also become a trap: a thread that only defines the method, without a worked example from a business, is marked down even when the definition is correct.

Your weekly exercise is done once the week's module opens and the data set is available, and it usually reaches you two days later.

The thread for a given week is ready as that module opens; replies wait until there is something from classmates to answer.

In the exam weeks, deliveries come a day earlier so that the exam day is clear.

The quote prices each exercise and each forum, and you can keep any of them yourself.

Smoothing and early weeks are the smaller lines; regression and decomposition are the larger ones.

Starting mid-course is common after a hard exam, and the order then covers only what remains.

A section that changes an exercise is repriced from the new instructions before work starts on it.

Seen across the term, the larger lines cluster in the middle weeks, so most of the cost sits in weeks four to six in a full order and a partial one alike.