Selective Distribution

Examples of Selective Distribution in the following topics:

• Distribution Intensity

  • Product distribution intensity refers to the scale of the distribution network as well as the appropriate selection of location.
  • Product distribution intensity refers to the scale of the distribution network as well as the appropriate selection of location.
  • In selective distribution, the producer relies on a few intermediaries to carry their product.
  • In exclusive distribution,the producer selects only very few intermediaries.
  • Retailers such as Lowe's are commonly utilized in selective distribution for large appliances.

• Channel Member Characteristics

  • There are three basic types of distribution for a marketer to consider: Intensive, selective, and exclusive.
  • Selective distribution means that the producer relies on a few intermediaries to carry their product.
  • Exclusive distribution means that the producer selects only very few intermediaries, such as is often the case with luxury goods.
  • A marketer will consider the three types of distribution and select the one that most closely fits the overall marketing strategy.
  • Generally, as one moves from intensive to selective to exclusive distribution channels, the more that company can charge for its products.

• Stabilizing, Directional, and Diversifying Selection

  • If natural selection favors an average phenotype by selecting against extreme variation, the population will undergo stabilizing selection.
  • When the environment changes, populations will often undergo directional selection, which selects for phenotypes at one end of the spectrum of existing variation.
  • Sometimes natural selection can select for two or more distinct phenotypes that each have their advantages.
  • Diversifying selection occurs when extreme values for a trait are favored over the intermediate values.This type of selection often drives speciation.
  • Different types of natural selection can impact the distribution of phenotypes within a population.In (a) stabilizing selection, an average phenotype is favored.In (b) directional selection, a change in the environment shifts the spectrum of phenotypes observed.In (c) diversifying selection, two or more extreme phenotypes are selected for, while the average phenotype is selected against.

• Creating a Sampling Distribution

  • Two of the balls are selected randomly (with replacement), and the average of their numbers is computed.
  • The distribution shown in the above figure is called the sampling distribution of the mean.
  • For this simple example , the distribution of pool balls and the sampling distribution are both discrete distributions.
  • As the number of samples approaches infinity , the frequency distribution will approach the sampling distribution.
  • This table shows all the possible outcome of selecting two pool balls randomly from a population of three.

• Sampling Distributions and Statistic of a Sampling Distribution

  • You can think of a sampling distribution as a relative frequency distribution with a great many samples.
  • Suppose thirty randomly selected students were asked the number of movies they watched the previous week.
  • If you let the number of samples get very large (say, 300 million or more), the relative frequency table becomes a relative frequency distribution.

• Typical Shapes

  • Considerations of the shape of a distribution arise in statistical data analysis, where simple quantitative descriptive statistics and plotting techniques, such as histograms, can lead to the selection of a particular family of distributions for modelling purposes.
  • In a symmetrical distribution, the two sides of the distribution are mirror images of each other.
  • A normal distribution is an example of a truly symmetric distribution of data item values.
  • A uni-modal distribution occurs if there is only one "peak" (or highest point) in the distribution, as seen previously in the normal distribution.
  • This image shows a normal distribution.

• Frequency-Dependent Selection

  • In frequency-dependent selection, phenotypes that are either common or rare are favored through natural selection.
  • Another type of selection, called frequency-dependent selection, favors phenotypes that are either common (positive frequency-dependent selection) or rare (negative frequency-dependent selection).
  • An interesting example of this type of selection is seen in a unique group of lizards of the Pacific Northwest.
  • As a result, populations of side-blotched lizards cycle in the distribution of these phenotypes.
  • Negative frequency-dependent selection serves to increase the population's genetic variance by selecting for rare phenotypes, whereas positive frequency-dependent selection usually decreases genetic variance by selecting for common phenotypes.

• Summary

  • The distribution for the test is the F distribution with 2 different degrees of freedom.
  • The distribution for the hypothesis test is the F distribution with 2 different degrees of freedom.
  • The populations from which the two samples are drawn are normally distributed.

• Review

  • Suppose that one person from of the above was randomly selected.
  • The distribution for X is Uniform.
  • The distribution for $\bar{X}$ is still Uniform with the same mean and standard dev. as the distribution for X.
  • The distribution for X is uniform.
  • The distribution for ∑ Xis still uniform with the same mean and standard deviation as the distribution for X.

• Introduction to Sampling Distributions

  • Suppose two of the balls are selected randomly (with replacement) and the average of their numbers is computed.
  • The distribution shown in Figure 2 is called the sampling distribution of the mean.
  • For this simple example, the distribution of pool balls and the sampling distribution are both discrete distributions.
  • Now we will consider sampling distributions when the population distribution is continuous.
  • Therefore, it is more convenient to use our second conceptualization of sampling distributions which conceives of sampling distributions in terms of relative frequency distributions.