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When determining the necessary sample size for hypothesis testing of means for a specified level of confidence and margin of error the minimum sample size is given by which?
RobertHuntergp2893
Sample size determination is the act of choosing the number of observations or replicates to include in a statistical sample. The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample.
For Confidence level c, and the critical value of Zc is the number such that the area under the statndard normal curve between -Zc and Zc equals C.
n > (zcσ/E)2
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What are sampling distributions used for?
Sampling distribution are used to: a) Estimate the number of samples or surveys to make to obtain a specified confidence in a particular statistic. b) Determine the confidence interval and the margin of error of a particular statistic. c) Conduct a hypothesis test on a particular statistic. I note that common statistics are mean and variance. However, there are sampling distributions for many statistics, including proportion and coeficient of correlation. Hypothesis testing can be one tail or two tail, and there are different approaches.
How do you calculate the probability of observing a sample proportion of .32 or more?
You need a null hypothesis first. You then calculate the probability of the observation under the conditions specified by the null hypothesis.
One use of the chi-square goodness of fit test is to determine if specified multinomial probabilities in the null hypothesis is correct True False?
True.
Does the confidence interval always contain the true population parameter?
No, the confidence interval (CI) doesn't always contain the true population parameter. A 95% CI means that there is a 95% probability that the population parameter falls within the specified CI.
What are difference between uniformly most powerful test and most power test?
the mp test is only for a specified value of hypothesis and the UMP test is for a set of values
What is a Simple vs complex hypothesis?
A simple hypothesis is one in which all parameters of the distribution are specified. For example, if the heights of college students are normally distributed with, the hypothesis that its mean is, say,, that is , we have stated a simple hypothesis, as the mean and variance together specify a normal distribution completely. A simple hypothesis, in general, states that where is the specified value of a parameter, ( may represent etc). A hypothesis which is not simple (i.e. in which not all of the parameters are specified) is called a composite hypothesis.For instance, if we hypothesize that (and) or and, the hypothesis becomes a composite hypothesis because we cannot know the exact distribution of the population in either case. Obviously, the parameters and have more than one value and no specified values are being assigned. The general form of a composite hypothesis is or, that is the parameter does not exceed or does not fall short of a specified value. The concept of simple and composite hypotheses applies to both null hypothesis and alternative hypothesis.
What is composite hypothesis?
A hypothesis which is not simple (i.e. in which not all of the parameters are specified) is called a composite hypothesis.For instance, if we hypothesize that (and) or and, the hypothesis becomes a composite hypothesis because we cannot know the exact distribution of the population in either case. Obviously, the parameters and have more than one value and no specified values are being assigned. The general form of a composite hypothesis is or, that is the parameter does not exceed or does not fall short of a specified value. The concept of simple and composite hypotheses applies to both null hypothesis and alternative hypothesis.
What is a Simple vs complex?
A simple hypothesis is one in which all parameters of the distribution are specified. For example, if the heights of college students are normally distributed with, the hypothesis that its mean is, say,, that is , we have stated a simple hypothesis, as the mean and variance together specify a normal distribution completely. A simple hypothesis, in general, states that where is the specified value of a parameter, ( may represent etc). A hypothesis which is not simple (i.e. in which not all of the parameters are specified) is called a composite hypothesis.For instance, if we hypothesize that (and) or and, the hypothesis becomes a composite hypothesis because we cannot know the exact distribution of the population in either case. Obviously, the parameters and have more than one value and no specified values are being assigned. The general form of a composite hypothesis is or, that is the parameter does not exceed or does not fall short of a specified value. The concept of simple and composite hypotheses applies to both null hypothesis and alternative hypothesis.
What are sampling distributions used for?
Sampling distribution are used to: a) Estimate the number of samples or surveys to make to obtain a specified confidence in a particular statistic. b) Determine the confidence interval and the margin of error of a particular statistic. c) Conduct a hypothesis test on a particular statistic. I note that common statistics are mean and variance. However, there are sampling distributions for many statistics, including proportion and coeficient of correlation. Hypothesis testing can be one tail or two tail, and there are different approaches.
What is Scientific modeling?
I suspect you mean the scientific method. Briefly outlined, the scientific method consists of the following tasks: Specify a testable null and an alternative hypothesis based on preliminary tests and observations. Design tests that will determine the validity of the null hypothesis to a specified degree of confidence. Collect data and observations for the tests according to requirements of tests and data sample spaces. Test the hypothesis using collected data and observations. Make conclusions and report on the validity (or not) of the hypothesis. Make recommendation for further studies or applications of the experimental results.
What is the scientific model?
I suspect you mean the scientific method. Briefly outlined, the scientific method consists of the following tasks: Specify a testable null and an alternative hypothesis based on preliminary tests and observations. Design tests that will determine the validity of the null hypothesis to a specified degree of confidence. Collect data and observations for the tests according to requirements of tests and data sample spaces. Test the hypothesis using collected data and observations. Make conclusions and report on the validity (or not) of the hypothesis. Make recommendation for further studies or applications of the experimental results.
How do you calculate the probability of observing a sample proportion of .32 or more?
You need a null hypothesis first. You then calculate the probability of the observation under the conditions specified by the null hypothesis.
As standard deviation increases what happens to the sample size in order to achieve a specified level of confidence?
decreases
How do you use british European and international standards in determining if components and products are fit for purpose?
By comparing the components to the specified standards.
What values are specified by the null hypothesis for the chi square test for goodness or fit?
The value specified is usually the maximum value that the test statistic can take for a given level of statistical significance when the null hypothesis is true. This value will depend on the parameter of the chi-square distribution which is also known as its degrees of freedom.The value specified is usually the maximum value that the test statistic can take for a given level of statistical significance when the null hypothesis is true. This value will depend on the parameter of the chi-square distribution which is also known as its degrees of freedom.The value specified is usually the maximum value that the test statistic can take for a given level of statistical significance when the null hypothesis is true. This value will depend on the parameter of the chi-square distribution which is also known as its degrees of freedom.The value specified is usually the maximum value that the test statistic can take for a given level of statistical significance when the null hypothesis is true. This value will depend on the parameter of the chi-square distribution which is also known as its degrees of freedom.
One use of the chi-square goodness of fit test is to determine if specified multinomial probabilities in the null hypothesis is correct True False?
True.
Explain in detail the three possible outcomes that may result from this research?
The question is very poorly specified so this answer is simply a wild guess at what the questioner might want. Three possible outcomes of any research, designed to test some hypothesis, are: (a) evidence in support of the hypothesis; (b) evidence disproving the hypothesis; or (c) evidence that can neither prove (support) nor disprove the hypothesis.
