tailieunhanh - Lecture Quantitative investment analysis: Chapter 3 – CFA Institute
Chapter 3 – Statistical concepts and market returns. This chapter include objectives: Differentiate between a population and a sample, explain the concepts of a parameter and a sample statistic; explain the differences among the types of measurement scales; define, calculate, and interpret a holding period return (total return); | Statistical concepts and market returns 1 Populations and samples The subset of data used in statistical inference is known as a sample and the larger body of data is known as the population. The population is defined as all members of the group in which we are interested. 2 Sample Population LOS: Differentiate between a population and a sample. Page 62 Even if we could observe all the members of a population, it is often costly to do so in terms of time, money, and/or computing power. The additional benefit gained is not typically worth the additional cost. The field of statistics is specifically designed to alleviate the need to observe an entire population. In order for a sample drawn from a population to be useful in statistical inference, it needs to have been created with some specific attention to its properties as detailed in Chapter 6. We refer to a sample with this desirable property as being “characteristic” of the population. 2 Parameters and Sample Statistics A population | Statistical concepts and market returns 1 Populations and samples The subset of data used in statistical inference is known as a sample and the larger body of data is known as the population. The population is defined as all members of the group in which we are interested. 2 Sample Population LOS: Differentiate between a population and a sample. Page 62 Even if we could observe all the members of a population, it is often costly to do so in terms of time, money, and/or computing power. The additional benefit gained is not typically worth the additional cost. The field of statistics is specifically designed to alleviate the need to observe an entire population. In order for a sample drawn from a population to be useful in statistical inference, it needs to have been created with some specific attention to its properties as detailed in Chapter 6. We refer to a sample with this desirable property as being “characteristic” of the population. 2 Parameters and Sample Statistics A population has parameters, and a sample has statistics. Descriptive statistics that characterize population values are called parameters. Examples: mean, median, mode, variance, skewness, kurtosis Descriptive statistics that characterize samples are known as sample statistics. Examples: sample mean, sample median, sample variance By convention, we often omit the term “sample” in front of sample statistics, a practice that can lead to confusion when discussing both the sample and the population. 3 LOS: Explain the concepts of a parameter and a sample statistic. Pages 62–63 Although it is possible to know parameters, it is most often the case that we don’t know them with certainty. The field of inferential statistics is devoted to inferring population parameters from sample characteristics. 3 Measurement Scales Statistical inference is affected by the type of data we are trying to analyze. Nominal scales categorize data but do not rank them. Examples: fund style, country of origin, manager gender
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