The first broad category of statistics we discuss concerns descriptive statistics. What remains after their application is simply for us to interpret and tell the story. These statistical procedures are designed to identify or display specific patterns or trends in the data. Rather we utilise procedures and measures which provide a general depiction of how the data are behaving. We seldom interpret individual data points or observations primarily because it is too difficult for the human brain to extract or identify the essential nature, patterns, or trends evident in the data, particularly if the sample is large. Along the way, we explore the fundamental concepts of probability and the normal distribution. a histogram, box plot, radar plot, stem-and-leaf display, icon plot or line graph) or the computation of an index or number designed to summarise a specific characteristic of a variable or measurement (e.g., frequency counts, measures of central tendency, variability, standard scores). By ‘describe’ we generally mean either the use of some pictorial or graphical representation of the data (e.g. The purpose of the procedures and fundamental concepts reviewed in this chapter is quite straightforward: to facilitate the description and summarisation of data. Bar graphs are especially useful when categorical data is being used.This chapter discusses and illustrates descriptive statistics. Some bar graphs present bars clustered in groups of more than one (grouped bar graphs), and others show the bars divided into subparts to show cumulative effect (stacked bar graphs). One axis of the chart shows the specific categories being compared, and the other axis represents a discrete value. A bar graph is a chart that uses either horizontal or vertical bars to show comparisons among categories. That is, finding a general pattern in data sets including temperature, sales, employment, company profit or cost over a period of time. These graphs are useful for finding trends. A line graph is often used to represent a set of data values in which a quantity varies with time. The advantage in a stem-and-leaf plot is that all values are listed, unlike a histogram, which gives classes of data values. In a stem-and-leaf plot, all data values within a class are visible. The frequency points are connected using line segments.Ī stem-and-leaf plot is a way to plot data and look at the distribution. In the particular line graph shown in Example, the x-axis (horizontal axis) consists of data values and the y-axis (vertical axis) consists of frequency points. It takes some background information to explain outliers, so we will cover them in more detail later.Īnother type of graph that is useful for specific data values is a line graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500) while others may indicate that something unusual is happening. When you graph an outlier, it will appear not to fit the pattern of the graph. An outlier is an observation of data that does not fit the rest of the data. You want to look for an overall pattern and any outliers. The stemplot is a quick way to graph data and gives an exact picture of the data. \right)\) were in the 90s or 100, a fairly high number of As.įor the Park City basketball team, scores for the last 30 games were as follows (smallest to largest):ģ2 32 33 34 38 40 42 42 43 44 46 47 47 48 48 48 49 50 50 51 52 52 52 53 54 56 57 57 60 61Ĭonstruct a stem plot for the data.
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