# Statistics for Business and Economics Back to the search result list. Table of Contents Frontmatter 1. Descriptive Statistics Abstract. In this chapter we will look at very basic statistical concepts. The material is facilitated to make it available to a wide audience and does not require any prerequisites. For some readers this will mean that they are already familiar with the contents and it may be sufficient to browse quickly through the chapter before continuing to the next chapters. Otherwise it can be necessary to study the material in detail, and it might be wise to invest some time on the exercises.

In this short chapter we will go through the basic definitions of probability. To ease the exposition, we will only discuss very simple examples. It is important to notice that the concepts we develop in this chapter are central to all thinking about statistics. Regardless of level and purpose these concepts provide tools that can be used to describe statistical methods. From this point of view the theory provides us with a framework that can be used to study any statistical phenomenon.

In this chapter we will study certain types of random selection within a uniform model. Such samples can occur when we select a representative from an audience, when we sample for errors, play lotteries, or generally when we choose between alternatives which are equally probable.

The main problem is then to figure out how many combinations there are of a particular type. When we know the number of combinations, it is easy to figure out the probabilities, since in a uniform model all alternatives are equally probable. We then find the probability as the fraction between the number of combinations of the type we are looking for and the number of combinations in total.

In most cases where we study probabilities, we have some additional information about what has happened. Information of this sort will often pose a restriction on the sample space. In this chapter we will show how to compute probabilities under such restrictions. In this chapter we will introduce a general framework we will use to study statistical distributions. A statistical distribution specifies the probability of the different outcomes that can occur. We can, e. We let x run from 0 to 10, and compute 11 probabilities. The set of these probabilities we call the distribution.

When the distribution is known, we are usually able to compute all the probabilities we may need. The distribution is hence the key to our calculations. In this chapter we will look at situations that occur when we study two discrete random variables at the same time. One example of that sort could be that X is the price of a stock, while Y is the number of that stock that is sold each day. In many cases there can be more or less strong relations between the two variables, and it is important to be able to decide to what extent such relations are present.

The theory has much in common with the theory in Chap. In this chapter we will discuss some of the most commonly used probability distributions. We will discuss basic properties of these distributions; in particular, we will provide explicit formulas for the mean and variance. We focus the binomial distribution, the hyper geometric distribution, the Poisson distribution, and the normal distribution. Current Site. Course Outlines. In today's world, good decision making relies on data and data analysis.

This course helps students develop the understanding that they will need to make informed decisions using data, and to communicate the results effectively. The course is an introduction to the essential concepts, tools and methods of statistics for students in business, economics and similar disciplines, although it may have wider interest. The focus is on concepts, reasoning, interpretation and thinking rather than computation, formulae and theory. Much of the work will require students to write effectively and communicate their ideas with clarity.

## Statistics for Business and Economics

The course covers two main branches of statistics: descriptive statistics and inferential statistics. Descriptive statistics includes collecting data and summarising and interpreting them through numerical and graphical techniques. Inferential statistics includes selecting and applying the correct statistical technique in order to make estimates or test claims about a population based on a sample. Topics covered may include descriptive statistics, correlation and simple regression, probability, point and interval estimation, hypothesis testing, multiple regression, time series analysis and index numbers.

By the end of this course, students should understand and know how to use statistics. Students will also develop some understanding of the limitations of statistical inference and of the ethics of data analysis and statistics. Students will work in small groups in this course; this will develop the skills required to work effectively and inclusively in groups, as in a real work environment. Typically, one component of the assessment requires students to work in teams and collect and analyse data in order to answer a real-world problem of their own choosing.

Open All. Intensive in Summer Semester. These are the Course Coordinators for this course for the three semesters of They will provide further contact details, such as office locations and office hours, at the start of the semester.

Students in this course are expected to attend two 1-hour lectures and one 2-hour practical tutorial class each week. ISBN There are other editions of this book, used in and They are also suitable for this course. Calculator Students will need a calculator; a basic one that can take squares, square roots etc is sufficient.

## Introductory Statistics for Business and Economics

The lecture slides, practical questions and other information will be available for students on MyUni and can be downloaded or printed from there. Alternatively, a booklet containing the lectures slides and prac question set may be available for purchase. Details will be on MyUni by the start of semester. The lecture notes are NOT complete — they indicate what is to be covered in the lecture; you need to attend the lecture and write your own notes. In Semester 1 and 2 but not in Summer Semester it is intended that the lectures be recorded and a recording of each lecture put on MyUni for students who miss a lecture — but be aware that sometimes recordings fail.

In that case, a note will be put on MyUni but the lecture may not always be re-recorded and students may need to make other arrangements, such as obtain notes from other students, read the book or contact the lecturer. The lectures provide an overview of the course content but students must expect that they will need to study the textbook in order to understand the work. The practicals will incorporate team based learning and may include discussion, problem solving activities, individual and group work, student questions and student participation.

hyebitutinur.gq These practicals provide the opportunity for students to practice; they are vital for success in this course. Before the practicals, students will be expected to have attended or watched and understood the lectures and to have read the relevant chapter s from the text book. During the practical, First, students will be asked to record their attendance. Multiple choice question test will be done in teams. Individual written tests will be done as per advised.

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Finally, students will be required to work on worked answer questions, again in their teams. After the practicals, each team is required to hand in their answers to the worked answer questions, to their tutor. Some of these questions will be marked and returned in the following practical. Working in groups teams will be a feature of tutorials practicals — making decisions as a group, planning how to undertake activities collectively and discussing ideas and sharing information respectfully and constructively.

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