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When the production manager finds the average life of her battery-lifetime data it is an example of what phase of inferential statistics?
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How do inferential statistics describe data differently than descriptive statistics?
The term "descriptive statistics" generally refers to such information as the mean (average), median (midpoint), mode (most frequently occurring value), standard deviation, highest value, lowest value, range, and etc. of a given data set. It is a loosely used term, and not always meant to contrast with inferential statistics as the question implies. But in the context of the question, descriptive statistics would be information that pertains only to the data that has actually been collected. In the case of an instructor calculating an average grade for a class, for example, the collected data would most likely be the only point of interest. Thus, descriptive statistics would be enough. However, it is more common for a researcher to use a sample of collected data to make inferences and draw conclusions about a larger group (or "population") that the sample represents. For example, if you wanted to know the average age of users of this site, it would be unrealistic to question every singe user. So you might question a small sample and then extend that information to all users. But if you found the average age in your sample to be 40, you could not immediately assume that 40 is the average for all users. You would need to use inferential statistics to calculate an estimate of how accurately your data represents the larger group. The most common way to do this is to calculate a standard error, which will produce a range within which the population average most likely (but not definitively) lies. Therefore, in the simplest description (inferential statistics are also a part of much more powerful tests outside of this answer), descriptive statistics refer only to a sample while inferential statistics refer to the larger population from which the sample was drawn.
What are the 2 branches of statistics?
The Branches of StatisticsTwo branches, descriptive statistics and inferential statistics, comprise the field of statistics.Descriptive StatisticsCONCEPT The branch of statistics that focuses on collecting, summarizing, and presenting a set of data.EXAMPLES The average age of citizens who voted for the winning candidate in the last presidential election, the average length of all books about statistics, the variation in the weight of 100 boxes of cereal selected from a factory's production line.INTERPRETATION You are most likely to be familiar with this branch of statistics, because many examples arise in everyday life. Descriptive statistics forms the basis for analysis and discussion in such diverse fields as securities trading, the social sciences, government, the health sciences, and professional sports. A general familiarity and widespread availability of descriptive methods in many calculating devices and business software can often make using this branch of statistics seem deceptively easy. (Chapters 2 and 3 warn you of the common pitfalls of using descriptive methods.)Inferential StatisticsCONCEPT The branch of statistics that analyzes sample data to draw conclusions about a population.EXAMPLE A survey that sampled 2,001 full-or part-time workers ages 50 to 70, conducted by the American Association of Retired Persons (AARP), discovered that 70% of those polled planned to work past the traditional mid-60s retirement age. By using methods discussed in Section 6.4, this statistic could be used to draw conclusions about the population of all workers ages 50 to 70.INTERPRETATION When you use inferential statistics, you start with a hypothesis and look to see whether the data are consistent with that hypothesis. Inferential statistical methods can be easily misapplied or misconstrued, and many inferential methods require the use of a calculator or computer. (A full explanation of common inferential methods appears in Chapters 6 through 9.)
When the production manager finds the average life of her battery-lifetime data, it is an example of what phase of inferential statisticsA. Data OrganizationB. Probability-based Inference C. Data-AnalysisD. Data Gathering?
DATA GATHERING
Distinguish between parameteric statistics and non - parameteric statistics?
The simplest answer is that parametric statistics are based on numerical data from which descriptive statistics can be calculated, while non-parametric statistics are based on categorical data. Takes two example questions: 1) Do men live longer than women, and 2), are men or women more likely to be statisticians. In the first example, you can calculate the average life span of both men and women and then compare the two averages. This is a parametric test. But in the second, you cannot calculate an average between "man" and "woman" or between "statistician" or "non-statistician." As there is no numerical data to work with, this would be a non-parametric test. The difference is vitally important. Because inferential statistics require numerical data, it is possible to estimate how accurate a parametric test on a sample is compared to the relevant population. However, it is not possible to make this estimation with non-parametric statistics. So while non-parametric tests are still used in many studies, they are often regarded as less conclusive than parametric statistics. However, the ability to generalize sample results to a population is based on more than just inferential statistics. With careful adherence to accepted random sampling, sample size, and data collection conventions, non-parametric results can still be generalizable. It is just that the accuracy of that generalization can not be statistically verified.
What does mean mean in statistics?
Mean is the average.
Compare and contrast descriptive statistics and inferential statistics?
Descriptive statistics are meant to describe the situation such as the average or the range. Inferential statistics is used to differentiate between a couple of groups.
The estimation of the population average family expenditure on food based on the sample average expenditure of 1000 families is an example of what?
Inferential Statistics!
How do inferential statistics describe data differently than descriptive statistics?
The term "descriptive statistics" generally refers to such information as the mean (average), median (midpoint), mode (most frequently occurring value), standard deviation, highest value, lowest value, range, and etc. of a given data set. It is a loosely used term, and not always meant to contrast with inferential statistics as the question implies. But in the context of the question, descriptive statistics would be information that pertains only to the data that has actually been collected. In the case of an instructor calculating an average grade for a class, for example, the collected data would most likely be the only point of interest. Thus, descriptive statistics would be enough. However, it is more common for a researcher to use a sample of collected data to make inferences and draw conclusions about a larger group (or "population") that the sample represents. For example, if you wanted to know the average age of users of this site, it would be unrealistic to question every singe user. So you might question a small sample and then extend that information to all users. But if you found the average age in your sample to be 40, you could not immediately assume that 40 is the average for all users. You would need to use inferential statistics to calculate an estimate of how accurately your data represents the larger group. The most common way to do this is to calculate a standard error, which will produce a range within which the population average most likely (but not definitively) lies. Therefore, in the simplest description (inferential statistics are also a part of much more powerful tests outside of this answer), descriptive statistics refer only to a sample while inferential statistics refer to the larger population from which the sample was drawn.
What are the 2 branches of statistics?
The Branches of StatisticsTwo branches, descriptive statistics and inferential statistics, comprise the field of statistics.Descriptive StatisticsCONCEPT The branch of statistics that focuses on collecting, summarizing, and presenting a set of data.EXAMPLES The average age of citizens who voted for the winning candidate in the last presidential election, the average length of all books about statistics, the variation in the weight of 100 boxes of cereal selected from a factory's production line.INTERPRETATION You are most likely to be familiar with this branch of statistics, because many examples arise in everyday life. Descriptive statistics forms the basis for analysis and discussion in such diverse fields as securities trading, the social sciences, government, the health sciences, and professional sports. A general familiarity and widespread availability of descriptive methods in many calculating devices and business software can often make using this branch of statistics seem deceptively easy. (Chapters 2 and 3 warn you of the common pitfalls of using descriptive methods.)Inferential StatisticsCONCEPT The branch of statistics that analyzes sample data to draw conclusions about a population.EXAMPLE A survey that sampled 2,001 full-or part-time workers ages 50 to 70, conducted by the American Association of Retired Persons (AARP), discovered that 70% of those polled planned to work past the traditional mid-60s retirement age. By using methods discussed in Section 6.4, this statistic could be used to draw conclusions about the population of all workers ages 50 to 70.INTERPRETATION When you use inferential statistics, you start with a hypothesis and look to see whether the data are consistent with that hypothesis. Inferential statistical methods can be easily misapplied or misconstrued, and many inferential methods require the use of a calculator or computer. (A full explanation of common inferential methods appears in Chapters 6 through 9.)
How can manager use statistics and what benefits he can get from the statistical methods?
Managers can apply some statistical technique to virtually every branch of public and private enterprise. These techniques are commonly separated into two broad categories: descriptive statistics and inferential statistics. Descriptive statistics are typically simple summary figures calculated from a set of observations. Suppose a professor computes an average grade for one accounting class. If the professor uses the statistic simply to describe the performance of that class, the result is a descriptive statistic of overall performance. Inferential statistics are used to apply conclusions about one set of observations to reach a broader conclusion or an inference about something that has not been directly observed. In this case, a professor might use the average grade from a series of previous accounting classes to estimate, or infer, the average grade for future accounting classes. Any conclusion made about future accounting classes is based solely on the inferential statistics derived from previous accounting classes. See the related link below for more information.
What is the difference between descriptive and inferential?
Both descriptive and inferential statistics look at a sample from some population.The difference between descriptive and inferential statistics is in what they do with that sample:Descriptive statistics aims to summarize the sample using statistical measures, such as average, median, standard deviation etc. For example, if we look at a basketball team's game scores over a year, we can calculate the average score, variance etc. and get a description (a statistical profile) for that team.Inferential statistics aims to draw conclusions about the population from the sample at hand. For example, it may try to infer the success rate of a drug in treating high temperature, by taking a sample of patients, giving them the drug, and estimating the rate of effectiveness in the population using the rate of effectiveness in the sample.Please see the related links for more details.All statistical tests are part of Inferential analysis; there are no tests conducted in Descriptive analysis· Descriptive analysis- describes the sample's characteristics using…o Metric- ex. sample mean, standard deviation or varianceo Non-metric variables- ex. median, mode, frequencies & elaborate on zero-order relationshipso Use Excel to help determine these sample characteristics· Inferential Analysis- draws conclusions about populationo Types of errorso Issues related to null and alternate hypotheseso Steps in the Hypothesis Testing Procedureo Specific statistical tests
What are the benefit of inferential statistics in Psychology?
Populations, parameters, and samples in inferential statistics. Inferential statistics lets you draw conclusions about populations using small samples. Consequently, inferential statistics provide enormous benefits because typically you can not measure and entirepopulation.Roll no: 18-237
When the production manager finds the average life of her battery-lifetime data, it is an example of what phase of inferential statisticsA. Data OrganizationB. Probability-based Inference C. Data-AnalysisD. Data Gathering?
DATA GATHERING
Distinguish between parameteric statistics and non - parameteric statistics?
The simplest answer is that parametric statistics are based on numerical data from which descriptive statistics can be calculated, while non-parametric statistics are based on categorical data. Takes two example questions: 1) Do men live longer than women, and 2), are men or women more likely to be statisticians. In the first example, you can calculate the average life span of both men and women and then compare the two averages. This is a parametric test. But in the second, you cannot calculate an average between "man" and "woman" or between "statistician" or "non-statistician." As there is no numerical data to work with, this would be a non-parametric test. The difference is vitally important. Because inferential statistics require numerical data, it is possible to estimate how accurate a parametric test on a sample is compared to the relevant population. However, it is not possible to make this estimation with non-parametric statistics. So while non-parametric tests are still used in many studies, they are often regarded as less conclusive than parametric statistics. However, the ability to generalize sample results to a population is based on more than just inferential statistics. With careful adherence to accepted random sampling, sample size, and data collection conventions, non-parametric results can still be generalizable. It is just that the accuracy of that generalization can not be statistically verified.
What does mean mean in statistics?
Mean is the average.
What are the importance of the mean in statistics?
the mean is important in statistics because you will find out your average and can compare that mean to other things..
What is the formula for x-bar in statistics?
(x value) - average
