Ap Statistics Sampling Method
Sampling methods
There are 4 basic sampling methods we have learned do far: simple random sampling, stratified sampling, clusters and systematic sampling. When we do experiments we need to use the right sampling method in order to make the experiment useful and successful. First, simple random sampling; it gives a sample selected in a way that gives every different sample of size n an equal chance of being selected. Second, stratified sampling; it divides a population into subgroups and then takes a separate random sample from each stratum. Next, cluster sampling; it divides a population into subgroups and forms a sample by randomly selecting clusters and includes all individuals or objects in the selected clusters in the sample. The last one is systematic sampling; a sample selected from an ordered arrangement of a population by choosing a starting point at random from the first n individuals on the list and then selecting every nth individual thereafter. In an experiment we want to select and test the sex of a pond of fish. We cannot use census so we need to use sampling. So we use the simple random sampling.
Stem plot A stemplot (or stem-and-leaf display), in statistics, is a device for presenting quantitative data in a graphical format, similar to a histogram, to assist in visualizing the shape of a distribution. Unlike histograms, stemplots retain the original data to at least two significant digits, and put the data in order, thereby easing the move to order-based inference and non-parametric statistics. To construct a stem plot, the observations must first be sorted in ascending order: this can be done most easily if working by hand by constructing a draft of the stem and leaf plot with the leaves unsorted, then sorting the leaves to produce the final stem and leaf plot. Here is the sorted set of data values that will be used in the following example: 44 46 47 49 63 64 66 68 68 72 72 75 76 81 84 88 106 Next, it must be determined what the stems will
There are 4 basic sampling methods we have learned do far: simple random sampling, stratified sampling, clusters and systematic sampling. When we do experiments we need to use the right sampling method in order to make the experiment useful and successful. First, simple random sampling; it gives a sample selected in a way that gives every different sample of size n an equal chance of being selected. Second, stratified sampling; it divides a population into subgroups and then takes a separate random sample from each stratum. Next, cluster sampling; it divides a population into subgroups and forms a sample by randomly selecting clusters and includes all individuals or objects in the selected clusters in the sample. The last one is systematic sampling; a sample selected from an ordered arrangement of a population by choosing a starting point at random from the first n individuals on the list and then selecting every nth individual thereafter. In an experiment we want to select and test the sex of a pond of fish. We cannot use census so we need to use sampling. So we use the simple random sampling.
Stem plot A stemplot (or stem-and-leaf display), in statistics, is a device for presenting quantitative data in a graphical format, similar to a histogram, to assist in visualizing the shape of a distribution. Unlike histograms, stemplots retain the original data to at least two significant digits, and put the data in order, thereby easing the move to order-based inference and non-parametric statistics. To construct a stem plot, the observations must first be sorted in ascending order: this can be done most easily if working by hand by constructing a draft of the stem and leaf plot with the leaves unsorted, then sorting the leaves to produce the final stem and leaf plot. Here is the sorted set of data values that will be used in the following example: 44 46 47 49 63 64 66 68 68 72 72 75 76 81 84 88 106 Next, it must be determined what the stems will