商业决策技术课件 07Quantitative Methods For Decision Makers Chapter 7 MARKET RESEARCH AND STATISTICAL INFERENCE.pptVIP

Quantitative Methods For Decision Makers3rd Edition Chapter 7 MARKET RESEARCH AND STATISTICAL INFERENCE Learning Objectives By the end of this chapter you should be able to: Understand the difference between a sample and a population Explain the principles of a sampling distribution Calculate and explain a confidence interval around a sample mean and a sample percentage Carry out a variety of common hypothesis tests Population and sample Two key assumptions about the sample: The sample is a properly representative subset of the statistical population. The data collected is reliable and accurate. Sampling distribution A sampling distribution is a distribution of all of the sample means selected from a population. Different samples taken from the same population would generate similar, but not necessarily arithmetically identical, sample means. The central limit theorem If we take random samples of size n from a population, the distribution of sample means will approach that of the Normal probability distribution. This approximation will become closer, the larger is n. Central Limit Theorem The sampling distribution If the data distribution is Normal, then the interval: contains about 68% of the values in the population contains about 95% of the values in the population contains about 99.7% of the values in the population Characteristics of the sampling distribution Mean SD Sample s Population μ σ Sampling distribution μ Point and Interval Estimates A point estimate is a single number, a confidence interval provides additional information about variability Point Estimates Confidence Intervals How much uncertainty is associated with a point estimate of a population parameter? An interval estimate provides more information about a population characteristic than does a point estimate Such interva