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Random sampling of 100 women over the age of 35 showed 8 of the women were married 2 or more times based on the study how many women in a group of 5000 women over age of 35 would be married 2 or more?
Wiki User
∙ 14y ago
It looks like a proportion problem and not a statistics problem:
x/5000 = 8/100
x/50=8/1
x=400
Wiki User
∙ 14y ago
Assuming the proportion of women over 35 who are married 2 or more times is consistent, we can calculate the proportion found in the sample (8/100 = 0.08). Applying this proportion to the larger group of 5000 women would mean approximately 400 women over the age of 35 in a group of 5000 would be married 2 or more times.
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What is stratified sampling under research methodology?
Stratified sampling is a sampling method in research where the population is divided into subgroups or strata based on certain characteristics. Samples are then selected from each stratum in proportion to the population, to ensure representation of all groups. This method helps to reduce sampling errors and improves the accuracy of the research findings.
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Sampling is important because it allows researchers to make inferences about a larger population based on a smaller subset. It helps to reduce the time, cost, and resources needed for data collection. Sampling also helps to ensure the reliability and generalizability of research findings.
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Post stratification is a statistical technique used to improve the precision of estimates by adjusting sample weights based on known population characteristics. It involves dividing the sample into subgroups (strata) based on certain characteristics and then adjusting the weights of each subgroup to better reflect the overall population. This helps to reduce bias and improve the accuracy of estimates in survey sampling.
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Based on data analysis from the National Survey of Family Growth, around 5% of Americans were virgins on the day they got married.
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The marketing manager is applying a sampling technique to make inferences about the preferences, behaviors, or opinions of the entire customer population. This helps to gain insights and make decisions based on a subset of customers, rather than having to survey every customer individually.
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Simple random sampling.
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Sampling distribution in statistics works by providing the probability distribution of a statistic based on a random sample. An example of this is figuring out the probability of running out of water on a camping trip.
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You get a non-random sample and any analysis based on the assumption of randomly distributed variables is no longer valid. In particular, your estimates of any variables are likely to be biased and your error estimates (standard errors or sample variances) will be incorrect. Any inferences based on statistical tests will be less reliable and may be wrong.
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What is systematc random sampling?
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