What did crocodile do assure Golu of his being crocodile? Answer: The crocodile winked at first and later shed tears to assure Golu of his being a crocodile.
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Oases (plural of oasis) form when the wind cuts out deep channels in low-lying areas of the desert and uncovers underground water that fell as rain long ago. The water in some oases was rain that fell there about 20,000 years ago.
12 locations were defined as the Cultural Landscape of Al-Hasa Oasis (the World Heritage site): Eastern Oasis (ا”واحة ا”شر,ية) Northern Oasis (ا”واحة ا”ش…ا”ية)
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Egypt has seven Oases Located in Western & eastern Deserts of Egypt; the weather is so harsh all the days of the year. Oases are inhabited by Bedouin Tribes for years. The Egyptian desert has different oasis such as Dakhla, Kharga, Siwa, Bahariya, Fayoum and Farafra.
Oases in Ancient Egypt Wadi Natrun was crucial because it contained the salt natron, a central ingredient in Egyptian embalming. The only oasis not under ancient Egyptian control (not until the 26th Dynasty: 600″500 BC) was Siwa, an isolated settlement in Western Egypt in the Libyan Desert.
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Hieroglyphics means sacred carvings. Sometimes called the word of the god. Scribes and Pharaohs.
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Descriptive statistics summarize the characteristics of a data set. Inferential statistics allow you to test a hypothesis or assess whether your data is generalizable to the broader population.
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When analysing data, such as the grades earned by 100 students, it is possible to use both descriptive and inferential statistics in your analysis. Typically, in most research conducted on groups of people, you will use both descriptive and inferential statistics to analyse your results and draw conclusions.
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Descriptive statistics are used to describe or summarize the characteristics of a sample or data set, such as a variable’s mean, standard deviation, or frequency. Inferential statistics can help us understand the collective properties of the elements of a data sample.
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Both descriptive and inferential statistics help make sense out of row after row of data! Use descriptive statistics to summarize and graph the data for a group that you choose. This process allows you to understand that specific set of observations.
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Descriptive statistics are used to summarize the data and inferential statistics are used to generalize the results from the sample to the population. In turn, inferential statistics are used to make conclusions about whether or not a theory has been supported, refuted, or requires modification.
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The goal of inferential statistics is to discover some property or general pattern about a large group by studying a smaller group of people in the hopes that the results will generalize to the larger group.
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For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study.
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Descriptive statistics are used to summarize data in an organized manner by describing the relationship between variables in a sample or population. Calculating descriptive statistics represents a vital first step when conducting research and should always occur before making inferential statistical comparisons.
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What are the similarities between descriptive and inferential statistics? Both descriptive and inferential statistics rely on the same set of data. Descriptive statistics rely solely on this set of data, whilst inferential statistics also rely on this data in order to make generalisations about a larger population.
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Properties of samples, such as the mean or standard deviation, are not called parameters, but statistics. Inferential statistics are techniques that allow us to use these samples to make generalizations about the populations from which the samples were drawn.
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The objective of descriptive statistics is to summarize data. To provide a method to convey impressions about the data.
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Descriptive statistics employs a set of procedures that make it possible to meaningfully and accurately summarize and describe samples of data. In order for one to make meaningful statements about psychological events, the variable or variables involved must be organized, measured, and then expressed as quantities.
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Descriptive Statistics is that branch of statistics which is concerned with describing the population under study. Inferential Statistics is a type of statistics, that focuses on drawing conclusions about the population, on the basis of sample analysis and observation.
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Descriptive statistics describes data (for example, a chart or graph) and inferential statistics allows you to make predictions (“inferences”) from that data. With inferential statistics, you take data from samples and make generalizations about a population.
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Descriptive statistics are used to display and communicate information regarding the life of the product, asset or processes. While Inferential statistics are used to make predictions of how like products, assets or processes will act based on the actions observed on a sample of the products, assets or processes.
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In qualitative research, descriptive statistics are typically observed in mixed method, action research, or other qualitative designs. More important, description lays the foundation for later analyses and interpretation of collected data.
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Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data.
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Statistics can describe markets, inform advertising, set prices and respond to changes in consumer demand. Descriptive analytics look at what has happened and helps explain why. By using historical data, managers can analyze past successes and failures.
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In a nutshell, descriptive statistics focus on describing the visible characteristics of a dataset (a population or sample). Meanwhile, inferential statistics focus on making predictions or generalizations about a larger dataset, based on a sample of those data.
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Inferential statistics are powerful tools for making inference that rely on frequencies and probabilities. Consequently, an understanding of inferential statistics can improve one’s ability to make decisions, form predictions, and conduct research.
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We use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study.
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A precise measure of dispersion, all the exact values are taken into account. Not difficult to calculate if you have a calculator . Easy to calculate. A precise measure of dispersion, all the exact values are taken into account.
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Inferential statistics use a random sample of data taken from a population to describe and make inferences about the population. Inferential statistics are valuable when examination of each member of an entire population is not convenient or possible.
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Descriptive statistics uses the data to provide descriptions of the population, either through numerical calculations or graphs or tables. Inferential statistics makes inferences and predictions about a population based on a sample of data taken from the population in question.
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Inferential statistics are often used to compare the differences between the treatment groups. Inferential statistics use measurements from the sample of subjects in the experiment to compare the treatment groups and make generalizations about the larger population of subjects.
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Descriptive statistics describes sets of data. Inferential statistics draws conclusions about the sets of data based on sampling.
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Descriptive statistics help you to simplify large amounts of data in a meaningful way. It reduces lots of data into a summary. Example 2: You’ve performed a survey to 40 respondents about their favorite car color.
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Descriptive statistics are used to summarize and describe total numbers. Looking at statistical numbers such as mean, or the average number, mode, or the most frequent number, or median, or the middle number, helps managers monitor business activities and make decisions.
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Also if you have percentages then you don’t have (purely) qualitative data set (in the more modern sense). It is quite possible that various simple inferential statistics such as chi-square could be used depending on sample size. Whether you think that is appropriate for your data is another matter.
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Qualitative research is not part of statistical analysis. That’s because the results can’t be tested to see if they are statistically significant (i.e. to see if the results could have occurred by chance). As a result, findings can’t be extended to a wider population.
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Inferential statistics: By making inferences about quantitative data from a sample, estimates or projections for the total population can be produced. Quantitative data can be used to inform broader understandings of a population, or to consider how that population may change or progress into the future.