1.2: Methods for Describing Data
1.2.1: Graphical Methods for Describing Quantitative Data
Read these sections and complete the questions at the end of each section. First, we'll look at the available methods to portray distributions of quantitative variables. Then, we'll introduce the stem and leaf plot and how to capture the frequency of your data. We'll also discuss box plots for the purpose of identifying outliers and for comparing distributions and bar charts for quantitative variables. Finally, we'll talk about line graphs, which are based on bar graphs.
This section elaborates on how to describe data. In particular, you will learn about the relative frequency histogram. Complete the exercises and check your answers.
1.2.2: Numerical Measures of Central Tendency and Variability
Read these sections and complete the questions at the end of each section. First, we will define central tendency and introduce mean, median, and mode. We will then elaborate on median and mean and discusses their strengths and weaknesses in measuring central tendency. Finally, we'll address variability, range, interquartile range, variance, and the standard deviation.
This section elaborates on mean, median, and mode at the population level and sample level. This section also contains many interesting examples of range, variance, and standard deviation. Complete the exercises and check your answers.
Watch this video series, which begins with a discussion on descriptive statistics and inferential statistics and then talks about mean, median, and mode, as well as sample variance.
1.2.3: Methods for Describing Relative Standing
This section discusses percentiles, which are useful for describing relative standings of observations in a dataset.
1.2.4: Methods for Describing Bivariate Relationships
Watch this tutorial to learn how to create the scatter plot for bivariate data using two variables, x and y.
This section introduces Pearson's correlation and explains what the typical values represent. It then elaborates on the properties of r, particularly that it is invariant under linear transformation. Finally, it introduces several formulas we can use to compute Pearson's correlation.