Hello and welcome to Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression. I'm Jordan Bakerman, an Analytics Training Consultant at SAS, and I'll be your teacher and guide throughout this course. Whether you need to estimate sale prices of homes, forecast company sales, monitor the health of a region using a public survey, or predict the likelihood of an individual defaulting on a loan, your challenge is to transform data into actionable information. We're in the midst of a data revolution. Data are being collected faster and from more sources than ever before, providing troves of untapped potential. Statistical methodology enables us to learn from data and better inform our decision making. In this course, you'll learn how to transform sampled data into statistical models. For example, imagine you want to predict someone's height using information about that person's weight and shoe size. You would expect that higher weights and larger shoe sizes are correlated to taller people. You can use a statistical model to confirm your suspicion, and predict an individual height given the other two variables. Or, imagine you want to estimate the relationship between sunflower growth and types of fertilizer. You might find that the generic fertilizer produces sunflowers that are, on average, as healthy and tall as the name brand fertilizer. But before we dive into statistical modeling, let's talk about the data. We'll use the Ames housing data to create a variety of statistical models. The data was collected by Truman State University, and contains information about the sale of individual residential properties in Ames, Iowa from 2006 to 2010. The variables included in the data set provide details about individual homes. For example, we have information regarding the sale price of the home, number of bedrooms, house size in square feet, lot shape and size, and many other details. So, what does the Ames housing data look like? Let's find out.
