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discussed in this book. It is clear that with an understanding of the main ideas of statistics, engaged citizens can grasp what the professional number crunchers have produced and evaluate the results. This book grew out of a course designed by Gudmund R. Iversen to meet the challenges created by this greater reliance on statistical It was one of a series of courses designed at Swarthmore information. College to fulfill the mission of a liberal arts college to educate its students for the challenges of the twenty-first century. The idea was that students should not become so involved with the intricacies of a single discipline that they lose sight of the big picture. These courses were intended to educate students to understand how the major ideas of a field relate to the world. In many respects statistics seemed an ideal subject for one such course. While statistics could be a mystifying, self­ aggrandized, and esoteric discipline, it could also be a key to under­ standing many other disciplines. The course, Stat 1: Statistical Think­ ing, was created to produce this understanding. The course proved to be very popular, and each year it grew in size. Over time the lecture notes for the course became more refined and extensive, and eventu­ ally the course material served as the basis for this book. Fonnulas As most statistics instructors are keenly aware, the teaching of statistics has changed dramatically.

Inhaltsverzeichnis

Chapter 1. Statistics: Randomness and Regularity

Abstract
You probably are reading this book because you think it is important to know something about the subject of statistics. At the same time, you may suspect that studying statistics won’t be the pleasantest task you have ever undertaken. We have seen too many reluctant students to think statistics courses are automatically crowd pleasers. We know some of you would prefer to analyze a poem, sing a ballad, or dissect a frog. But we think we have enough knowledge of student temperament to speak to all of you, eager and less than eager.
Gudmund R. Iversen, Mary Gergen

Chapter 2. Collection of Data

Abstract
To answer these questions and an enormous number of other ones, information must be gathered. In these instances, we need to know many things, from sexual habits to recycling practices. At first glance it seems easy to get this information. One needs only to go out and ask people or do an experiment to see how things work. But then the quandaries begin: Who should do the asking—you, me, unemployed college students, retired executives? And who should be asked? Can we afford to ask everyone concerned with the problem? For the first question, that would be the entire population of Los Angeles! Well, if not everyone, how about people who walk by a certain store at the mall on Saturday afternoon? Or those buying beer at the baseball stadium? Or do you think a presumably fairer way should be found?
Gudmund R. Iversen, Mary Gergen

Chapter 3. Description of Data: Graphs and Tables

Abstract
In Chapter 2 we discussed ways of collecting data. Once the data are gathered, we must search them for the information they contain. The data are available in the data file, but with so many numbers there, we cannot comprehend them all. Some way or other we must extract information from the data and put it into usable form. This means we need to analyze the data by graphing, tabulating, and computing.
Gudmund R. Iversen, Mary Gergen

Chapter 4. Description of Data Computing Summary Statistics

Abstract
As noted in Chapter 2, the original observations in a data file contain all the information there is in a set of data, but it is almost impossible simply to look at a data file and extract the information. All the information is there, but it is hidden by the randomness in the data.
Gudmund R. Iversen, Mary Gergen

Chapter 5. Probability

Abstract
Questions about probability occur regularly in everyday conversations as well as in statistics classes. In this chapter we discuss what statisticians mean by the term probability and how we use it in statistical analyses. From the questions on page 177, you can see that the word probability has to do with the chance, or likelihood, or degree of certainty that some event will happen, and that the term is used well beyond the scope of statistics.
Gudmund R. Iversen, Mary Gergen

Chapter 6. Drawing Conclusions Estimation

Abstract
Survey results and other statistical reports are often presented in newspapers and magazines and on television news. Statistical studies show, among many varied results, what percentages of African Americans in a sample prefer “African American” to “Black” as a name for their race (26%; 1989 telephone poll taken by Yankelovich Partners, Inc., for Time/CNN); what percentage of white Americans say they do not have enough money to buy food (13%; 1989 Gallup poll); what the mean age of female gymnasts equals (12.3 years; G. E. Theintz, et al., “Evidence for a reduction of growth potential in adolescent female gymnasts,” Journal of Pediatrics, vol. 122 (1993), pp. 306–313); what percentage of their time people spend sleeping (30.9; The New York Times, Tuesday, September 6, 1995, p. C6).
Gudmund R. Iversen, Mary Gergen

Chapter 7. Drawing Conclusions Hypothesis Testing

Abstract
In 1988 ( July 28) The New York Times carried a story on people’s knowledge of geography. The article described a study commissioned by The National Geographic Society and carried out by the Gallup Organization in which the researchers asked a large number of people in random samples from different countries to identify 16 locations (13 countries, Central America, the Persian Gulf, and the Pacific Ocean) on a world map. The researchers added up the number of correct identifications (from 0 to 16) each person made.
Gudmund R. Iversen, Mary Gergen

Chapter 8. Relationships between Variables

Abstract
In previous chapters, we have looked at ways in which statistical methods are used to collect, summarize, and draw conclusions from data on a single variable at a time. Now we analyze the data on two or more variables at the same time; that is, we study relationships between variables. Most sciences have as a goal establishing relationships between variables, and statistics plays in important role in this task. In this chapter we suggest the scope of concerns for statistical analysis. A good deal of the rest of the book expands on the study of relationships between variables.
Gudmund R. Iversen, Mary Gergen

Chapter 9. Chi-Square Analysis for Two Categorical Variables

Abstract
Let us compare two of the countries, Denmark and France (Table 9.1). Table 9.1 is called a contingency table. The table shows how the people in the poll were distributed on the two categorical variables country and attitude toward people. Note that this contingency table has two rows and two columns (as well as a row and a column labeled total), because each of the two variables has two values (categories). The country variable has the two values Denmark and France, and the attitude variable has the two values trust and suspicion. Of course, it is possible for categorical variables to have more than two categories, or values. With four countries the table would have four columns and two rows.
Gudmund R. Iversen, Mary Gergen

Chapter 10. Regression and Correlation for Two Metric Variables

Abstract
Questions about relationships between metric variables with well-defined units of measurement, such as food calories and fat content, gas mileage and vehicle weight, are answered using the statistical methods know as regression analysis and correlation analysis. Regression and correlation analyses represent two major and complementary aspects of the analysis of the relationship between metric variables.
Gudmund R. Iversen, Mary Gergen

Chapter 11. Anova: Analysis of Variance for a Categorical and a Metric Variable

Abstract
In this chapter we focus on one particular kind of crime—violent crime: murder, forcible rape, robbery, and aggravated violent crime. We address the question of whether the chances of being a victim of violent crime are the same from one part of the country to another. Then, if we do find that the number of violent crimes is not the same in different parts of the country, where are the high and low incidences of violent crime? These questions are more than theoretical. Insult and injury are at stake.
Gudmund R. Iversen, Mary Gergen

Chapter 12. Rank Methods for Two Rank Variables

Abstract
Most of the variables mentioned so far have been categorical or metric variables, and by now you are used to distinguishing between them. Have you ever wondered how you might measure variables such as class rank, runners in a race, or an attitude? These variables are neither categorical nor metric but rank variables.
Gudmund R. Iversen, Mary Gergen

Chapter 13. Multivariate Analysis

Abstract
For most problems, the outcome, or dependent variable, is determined by the influences of more than a single independent variable. Therefore, in the statistical analysis of a dependent variable, we often use more than one independent variable. When we analyze the relative impact of several independent variables, we are doing a multivariate statistical analysis.
Gudmund R. Iversen, Mary Gergen

Chapter 14. Statistics in Everyday Life

Abstract
Often, when we are blazing a trail through the woods in unfamiliar territory, we get so caught up in cutting through the surrounding brambles that we lose track of the bigger forest in which we are (possibly) lost. Now that we have arrived at a clearing and have survived the challenge of finding our way, we can take stock of what we have accomplished and look forward to future prospects.
Gudmund R. Iversen, Mary Gergen

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