R Tutorial : Research question (Inference for Linear Regression in R)

Want to learn more? Take the full course at https://learn.datacamp.com/courses/inference-for-linear-regression at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Consider a situation where you are interested in determining whether or not there is a linear model connecting protein and carbohydrates in the entire population of foods from Starbucks. we will walk through the pieces of the linear model output, and then in the following chapters we will explore all the pieces of inference in further detail. The variables in the Starbucks dataset include: calories, fat, carbohydrates, fiber, and protein. If interest is in determining a linear relationship between two of the variables, we can approach the linear model investigation in two ways: with a one sided or two sided hypothesis. A two-sided research questios investigates whether the two variables are linearly associated. A one-sided research question (in this scenario) investigates whether the two variables have a positive linear association. In order to avoid excessive false positives, the research question is always decided on before looking at the data. Note the two different (but similar) ways to output the linear model information. recall that the estimates have been calculated using least squares optimization, the value for the slope (0.381) is exactly the same regardless of the format of the output. As with the slope, the intercept (37.1) is given in the long or tidy format. The variability of both the intercept and the slope are given in the column called standard error. the standard error represents how much the line varies in units associated with either the intercept (row 1) or the slope (row 2). In both outputs, there is a column labeled "statistic" which combines the least squares estimate with the standard error. the statistic is a standardized estimate, it measures the number of standard errors that the estimate is above zero