# LL2 - Part 2 Sampling 1 Statistical Inference 2 Sampling...

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BUS1200-2 1 Part 2 Sampling 1. Statistical Inference 2. Sampling Plans 3. Simple Random Sampling 4. Distributions of Sample Means Section 2.1 Statistical Inference Statistical inference : using data obtained from a sample to make decisions or predictions about the population from which the sample is drawn Parameter : a numerical characteristic of a population (for example, population mean and population variance). The sample results provide estimates of the values of the population parameters. With proper sampling methods, the sample results will provide good estimates of the population parameters. Suppose we want to know the mean income BUS1200-2 2 of all families in a city. The population is therefore the incomes of all families in the city. We then select randomly a family from a city and know its income. In this experiment, the sample space is the city and the sample points are families in the city. The income corresponds to the outcome (a family randomly selected) of the experiment. Therefore we say that the income of a family randomly selected is a random variable. For convenience, we define the distribution of the population as the distribution of this random variable. Section 2.2 Sampling Plans We introduce three different sampling plans. Simple random sampling : all the samples with the same size are equally likely to be chosen. A simple random sample is a sample selected using a simple random sampling plan. BUS1200-2 3 To conduct random sampling, assign a number to each element of the chosen population (or use already given numbers), randomly select the sample numbers using a random number table or a software package. Stratified random sampling

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LL2 - Part 2 Sampling 1 Statistical Inference 2 Sampling...

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