3. Which of the following quantities is likely to show more temporal autocorrelation: daily rainfall or daily temperature? Why? 4. Distinguish between noise and outliers. Be sure to consider the following questions. Is noise ever interesting or desirable? Outliers? b. Can noise objects be outliers? C. Are noise objects always outliers? d. Are outliers always noise objects? Can noise make a typical value into an unusual one, or vice versa? e. 5. Discuss the advantages and disadvantages of using sampling to reduce the number of data objects that need to be displayed. Would simple random sampling (without replacement) be a good approach to sampling? Why or why not? 6. How might you address the problem that a histogram depends on the numberand location of the bins? 7. Show that the entropy of a node never increases after splitting it into smaller successor nodes.
3. Autocorrelation means relations of the values of the attributes. If the values of feature are closer to each other, we can say that feature is temporal auto correlated. Daily temperatures are more closely located as compare to daily rainfall. Because daily rainfall is scattered and not continuous all over the location. So, it is very difficult to predict the autocorrelation of it. Hence Daily temperature is likely to show more temporal autocorrelation.(由留学作业帮www.homeworkhelp.cc整理编辑)
4.
(a). Is noise ever interesting or desirable? Outliers? Answer: No. Noise will never be interesting because by definition, it is the unwanted part of the data. Outlier on the other hand is a piece of data which falls far outside the typically expected variation. It can be desirable
(b). Can noise objects be outliers?
Answer: Yes (
c). Are noise objects always outliers?
Answer: No
(d). Are outliers always noise objects?
Answer: No
(e). Can noise make a typical value into an unusual one, or vice versa?
Answer: Ye
5)
Sampling may be defined as the procedure in which a sample is selected from an individual or a group of people of certain kind for research purpose. In sampling, the population is divided into a number of parts called sampling units
(Advantages and Disadvantages of Sampling.
Sampling also assists in reducing the amount of data objects that need to be displayed.
There are however, both advantages and disadvantages that are associated with sampling.
Some of the advantages of sampling include.
Sampling can lower cost. Collecting data from the whole population can end up causing a lot of money. Taking samples of population and collecting data from them can assist in lowering the cost.
Sampling also takes less time. Collecting data from the whole population can be time consuming.
In increases the sampling scope. Sampling makes it possible to arrive at generalizations by studying the variables within a relatively small proportion of the population
(Advantages and Disadvantages of Sampling.
The accuracy of data collected from sampling tends to be higher. The higher degree of accuracy can be accomplished by collecting data from a limited area of operations
(Advantages and Disadvantages of Sampling)
There are also disadvantages that come from using sampling to reduce the number of data objects. Some of the disadvantages include but are not limited to.
A chance of bias is a big disadvantage of sampling. This can be caused by a bias selection of the sample of the population.
Selecting a sample that is a good representation of the population is also a disadvantage as it is something that could be difficult to represent.
Regarding my opinion in whether random sampling is a good approach to sample, I would have to say yes. Within a simple random sample the statistical population all have an equal chance to be chosen to collect data from. A simple random sample is meant to be an unbiased representation of a group. It is considered a fair way to select a sample from a larger population, since every member of the population has an equal chance of getting selected (Simple Random Sample.
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