Basic statistics

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  • Lecture Basic statistics for business and economics - Chapter 1: What is statistics?. When you have completed this chapter, you will be able to: List ways that statistics is used, know the differences between descriptive and inferential statistics, understand the differences between a sample and a population, explain the difference between qualitative and quantitative variables, compare the discrete and continuous variables, recognize the levels of measurement in data.

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  • Lecture Basic statistics for business and economics - Chapter 14: Multiple regression analysis. After completing this chapter, students will be able to: Describe the relationship between several independent variables and a dependent variable using multiple regression analysis, develop and interpret an ANOVA table, compute and interpret measures of association in multiple regression,...

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  • (BQ) Part 1 book "Mathematics and statistics for financial risk management" has contents: Some basic math, probabilities, basic statistics, distributions, multivariate distributions and copulas, bayesian analysis, hypothesis testing and confidence intervals, matrix algebra.

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  • Chapter 10: One-sample tests of hypothesis. After completing this chapter, students will be able to: Define a hypothesis, explain the five-step hypothesis-testing procedure, define type I and type II errors, define the term test statistic and explain how it is used,...

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  • This compendium aims at providing a comprehensive overview of the main topics that appear in any well-structured course sequence in statistics for business and economics at the undergraduate and MBA levels. The idea is to supplement either formal or informal statistic textbooks such as, e.g., “Basic Statistical Ideas for Managers” by D.K. Hildebrand and R.L. Ott and “The Practice of Business Statistics: Using Data for Decisions” by D.S. Moore, G.P. McCabe, W.M. Duckworth and S.L. Sclove, with a summary of theory as well as with a couple of extra examples.

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  • The original motivation for writing this book was rather personal. The first author, in the course of his teaching career in the Department of Pure Mathematics and Mathematical Statistics (DPMMS), University of Cambridge, and St John’s College, Cambridge, had many painful experiences when good (or even brilliant) students, who were interested in the subject of mathematics and its applications and who performed well during their first academic year, stumbled or nearly failed in the exams. This led to great frustration, which was very hard to overcome in subsequent undergraduate years.

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  • A: Basically, statistics is the “science of data.” There are three main tasks in statistics: (A) collection and organization, (B) analysis, and (C) interpretation of data. (A) Collection and organization of data: We will see several methods of organizing data: graphically (through the use of charts and graphs) and numerically (through the use of tables of data). The type of organization we do depends on the type of analysis we wish to perform.

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  • This compendium aims at providing a comprehensive overview of the main topics that appear in any well-structured course sequence in statistics for business and economics at the undergraduate and MBA levels. The idea is to supplement either formal or informal statistic textbooks such as, e.g., “Basic Statistical Ideas for Managers” by D.K. Hildebrand and R.L. Ott and “The Practice of Business Statistics: Using Data for Decisions” by D.S. Moore, G.P. McCabe, W.M. Duckworth and S.L. Sclove, with a summary of theory as well as with a couple of extra examples.

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  • When sitting in statistics classes or when trying to read and understand statistical material, too many otherwise intelligent and capable students and researchers feel dumb. This book is intended as an antidote. It is designed to make you feel smart and competent. Its approach is conservative in that it attempts to identify and present the essentials of data analysis as developed by statisticians over the last two or three centuries.

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  • R is a powerful tool for statistics and graphics, but getting started with this language can be frustrating. This short, concise book provides beginners with a selection of how-to recipes to solve simple problems with R. Each solution gives you just what you need to know to use R for basic statistics, graphics, and regression.

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  • Chapter 1: What is statistics? When you have completed this chapter, you will be able to: List ways that statistics is used, know the differences between descriptive and inferential statistics, understand the differences between a sample and a population, explain the difference between qualitative and quantitative variables, compare the discrete and continuous variables, recognize the levels of measurement in data.

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  • Chapter 10 - One-sample tests of hypothesis. In this chapter you will be able to: Define a hypothesis, explain the five-step hypothesis-testing procedure, define type i and type ii errors, define the term test statistic and explain how it is used, distinguish between a one-tailed and a two-tailed hypothesis,...

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  • Chapter 2 - Describing data: frequency tables, frequency distributions, and graphic presentation. The main goals of this chapter are to: Make a frequency table for a set of data, organize data into a bar chart, present a set of data using a pie chart, create a frequency distribution for a data set, understand a relative frequency distribution, present data from a frequency distribution in a construct and interpret a cumulative frequency distribution.

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  • When you have completed this chapter, you will be able to: Explain the concept of central tendency, identify and compute the arithmetic mean, compute and interpret the weighted mean, determine the median, identify the mode, explain and apply measures of dispersion, compute and explain the variance and the standard deviation.

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  • When you have completed this chapter, you will be able to: Construct and interpret a dot plot, identify and compute measures of position, construct and analyze a box plot, compute and describe the coefficient of skewness, create and interpret a scatter diagram, develop and explain a contingency table.

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  • When you have completed this chapter, you will be able to: Explain the terms experiment, event, and outcome; identify and apply the appropriate approach to assigning probabilities; calculate probabilities using the rules of addition; define the term joint probability;...

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  • When you have completed this chapter, you will be able to: Identify the characteristics of a probability distribution, distinguish between a discrete and a continuous random variable, compute the mean of a probability distribution, compute the variance and standard deviation of a probability distribution,...

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  • The main goals of this chapter are to: List the characteristics of the uniform distribution, compute probabilities by using the uniform distribution, list the characteristics of the normal probability distribution, convert a normal distribution to the standard normal distribution,...

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  • Chapter 8 - Sampling methods and the central limit theorem. When you have completed this chapter, you will be able to: Explain why a sample is often the only feasible way to learn something about a population, describe methods to select a sample, define sampling error, describe the sampling distribution of the sample mean,...

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  • When you have completed this chapter, you will be able to: Define a point estimate, define confidence interval, compute a confidence interval for the population mean when the population standard deviation is known, compute a confidence interval for a population mean when the population standard deviation is unknown,...

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