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If there are three different samples of the same size from a set population is it possible to get three different values for the same statistics?
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How can descriptive statistics be used to understand a samples characteristics?
Will a sample descriptive statistics accurately estimate the underlying population's parameters?
Are inferential statistics different from descriptive statistics?
Which one of the following is true about statistics? A) Inferential statistics deals with collecting, summarizing, and simplifying data about given population B) Descriptive statistics is also known as inductive statistics C) Descriptive statistics goes beyond describing a given problem situation by means of collecting, summarizing and meaningfully presenting the related data D) Descriptive statistics is one which describes the population. On the other hand, inferential statistics issued to make the generalization about the population based on the samples.
Why wont a sample statistics change from sample to sample?
The sample mean may differ from the population mean, especially for small samples.
How many different simple random samples of 7 are possible from a pool of 25?
There are 25C7 different samples of seven from a pool of 25.25C7 = 25!/(7!(25-7)!) = 480 700 different samples of 7
If there are 3 different samples from a set population can you get 3 different values for the same statistic?
Yes you can.
Three reasons why samples are used in statistics?
There are a few reasons why samples are used in statistics. One reason is that the whole population cannot be used and a sample is a good representation of the whole.
How can descriptive statistics be used to understand a samples characteristics?
Will a sample descriptive statistics accurately estimate the underlying population's parameters?
What is inferential data?
There is no inferential data. There is inferential statistics which from samples, you infer or draw a conclusion about the population. Hypothesis testing is an example of inferential statistics.
Deffirence of discriptive statistics to inferential statistics?
Descriptive statistics is the term given to the analysis of data that helps describe, show, or summarize data in a meaningful way such that patterns might emerge from the data. Inferential statistics are techniques that allow us to use population samples to make generalizations about the populations from which the samples were drawn.
How many simple random samples of size 5 can be selected from a population of size 35?
There are 324,632 possible samples.
If repeating an experiment many times with different samples what values for the sample means would be possible?
The answer depends on the population and is described by the sampling distribution of the mean.
Are inferential statistics different from descriptive statistics?
Which one of the following is true about statistics? A) Inferential statistics deals with collecting, summarizing, and simplifying data about given population B) Descriptive statistics is also known as inductive statistics C) Descriptive statistics goes beyond describing a given problem situation by means of collecting, summarizing and meaningfully presenting the related data D) Descriptive statistics is one which describes the population. On the other hand, inferential statistics issued to make the generalization about the population based on the samples.
What is success independent samples design?
different samples of respondents from the population complete the survey over a time period
Why wont a sample statistics change from sample to sample?
The sample mean may differ from the population mean, especially for small samples.
How many different simple random samples of 7 are possible from a pool of 25?
There are 25C7 different samples of seven from a pool of 25.25C7 = 25!/(7!(25-7)!) = 480 700 different samples of 7
If there are 3 different samples from a set population can you get 3 different values for the same statistic?
Yes you can.
Describe the central limit theorem and give an example of how it can be used in statistics?
The Central Limit Theorem (CLT) is a theorem that describes the fact that if a number of samples are taken from a population, the distribution of the means of the samples will be normal. This is true for all different distributions, whether or not the population is normal or something else. The main exception to this is that the theorem does not work particularly well if the samples are small (
