Data analysis turns observations into information that can support a decision. A typical workflow starts with raw data, checks its structure and completeness, and then uses a method suited to the question. Cloud computing provides access to remote infrastructure and software; Software as a Service (SaaS) is one way to use software through a service provider.
Prepare data in a spreadsheet
Google Sheets can organize tabular data and calculate summaries. The source course reviews basic functions, random values with RAND, array formulas, matrices, and date fields such as year, month, and day. Check that each row represents the intended observation, formats are consistent, and missing values are identified rather than silently treated as zero.
Functions such as COUNTBLANK, COUNTA, SUM, AVERAGE, MIN, MAX, STDEV, COUNTIF, and AVERAGEIF can help summarize a dataset. Use the function names and syntax supported by your current spreadsheet locale.
Understand slope and intercept
A simple linear model represents a relationship as y = intercept + slope × x. The slope describes the model’s expected change in y for a one-unit change in x; it is not proof that changing x causes the outcome. The intercept is the fitted value when x equals zero, which may or may not be meaningful in context.
Fit and assess a simple model
Plot the observations, fit a line, and inspect the residuals. The course uses spreadsheet functions such as INTERCEPT and SLOPE to estimate a line. Prediction error is the difference between an actual observation and a predicted value; the sum of squared errors (SSE) adds squared residuals. A low SSE alone does not show that a model generalizes or is useful. Check sample size, data quality, assumptions, and a separate validation approach before using a model for decisions.
Contact
For questions about the website, use the contact option below.
