Introduction to Statistical Analysis/Unit 1 Navigation
This is the main navigation page for Unit 1 of the course Introduction to Statistical Analysis, developed using openly licensed materials from Saylor.org's Introduction to Statistics. Below you will find a full description of Unit 1 in general, as well as for each subunit. Follow the links within each subunit description to access particular topics, or proceed directly to the Unit 1 Content Page.
UNIT 1: DATA AND DESCRIPTIVE STATISTICSEdit
In today's world, we have access to large volumes of data every day. The first step of data analysis is to accurately summarize all of this data, both graphically and numerically, so that we can understand what the data is saying. We see and use data in our lives every day. To be able to use and interpret the data correctly is essential to making informed decisions. In this unit, you will learn about descriptive statistics, which is used to summarize and display data. After completing this unit, you will know what you can do once you have collected data and how to present that data.
For example, suppose you are interested in buying a new mobile phone with a particular type of a camera. Suppose you are not sure about the prices of any of the phones with this feature so you log on to a website that provides you with a sample data set of prices, given your requirements. Now, looking at all the prices in the sample can sometimes be confusing. A better way to compare might be to look at the median price and the variation of prices. The median and variation are two of several ways that you can describe data. You can also ask the website to graph the data so that it is easier to see what the price distribution looks like. In this unit, you will study precisely this, i.e. you will learn numerical and graphical ways to describe and display your data. You will understand the essentials of calculating common descriptive statistics for measuring center, variability, and skewness in data. You will learn to not only calculate, but to interpret these measurements and graphs.
Remember: Descriptive statistics are, as their name suggests, “descriptive.” They do not generalize beyond the data considered. Inferential statistics can be used to generalize the findings from sample data to a broader population.
Time Advisory: This unit will take you approximately 10 hours to complete.
Upon completion of this this unit, you will be able to:
- Apply various types of sampling methods to data collection.
- Create and interpret frequency tables.
- Display data graphically and interpret the following types of graphs: stemplots, histograms, and boxplots.
- Recognize, describe, and calculate the measures of location of data: quartiles and percentiles.
- Recognize, describe, and calculate the measures of the center of mean, median, and mode.
- Recognize, describe, and calculate the following measures of the spread of data: variance, standard deviation, and range.
Subunit one consists of three main topics:
Introduction to StatisticsEdit
This topic will introduce you to a definition of statistics, as well as some broad concepts related to data, sampling, variation, and frequency. Learning about these general topics is essential to creating a basic understanding of the practice of statistics.
- 1.1.1: Statistics, Probability, and Key Terms
- 1.1.2: Data and Sampling
- 1.1.3: Variation and Frequency
Descriptive Statistics: Displaying DataEdit
This topic will introduce you to some of the basic methods for displaying descriptive statistical data including stem plots, line graphs, bar graphs, as well as histograms and box plots.
- 1.2.1: Stem and Leaf Graphs (Stemplots), Line Graphs, and Bar Graphs
- 1.2.2: Histograms
- 1.2.3: Box Plots
Descriptive Statistics: MeasuresEdit
This topic will introduce you to some of the basic measures used in statistics such as those for central tendency (Mean, Median, and Mode), as well as for the spread of data (Variance and Standard Deviation).
About the Resources in This CourseEdit
This course project draws upon three main types of resources:
The first are readings and video lectures from Barbara Illowsky and Susan Dean’s Collaborative Statistics, which is available freely under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license from the following location: http://cnx.org/content/col10522/latest/
The second type of resources in this course are lectures from Kahn Academy. These lectures are available under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) license. Kahn Academy has many lectures available from http://www.khanacademy.org/
Finally, the above resources have been woven together and organized into a format analogous to a traditional college-level course by professional consultants that work as experts within the subject area. This process was facilitated by The Saylor Foundation. Additionally, if you have worked through all of the material contained in this project, you may be interested in taking the final exam provided by Saylor.org or completing other courses available there that are not yet on Wikiversity.