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what example for decriptive statistic
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How does Mugenda and Mugenda define sample size?
According to research made at multiple websites; Mugenda and Mugenda, which is a company that focuses on research methods for various areas, define sample size by using statistics data and probability.
In Statistics define the 4 ARMC criteria?
1. variable is an antecedent 2. random assignment 3. values are maniipulated 4. controls
Why parameter should not change?
In statistics the parameters of the distributions of populations are the fundamental values of interest. In a way the randomness just gets in the way of learning about the parameters. They are considered constant because they define or characterise the distributions of populations.
Difference between statistics and statistic?
Statistics is the name of the subject. It is the general definition which deals with the collection, presentation and interpretation of data. On the other hand, statistic is a test used to test a hypothesis. It is commonly applied to sample cases. A statistic is a description of some measure, such as your height, weight, or age. Such measures may be collected in large numbers to be analyzed statistically to determine some characteristic of a population. For example, the collection of a population's ages make up a data base that may be analyzed by statistical methods to determine the proportion of the population that will retire and start drawing pension funds during the next decade.
How do you define a variable?
I define an variable by saying x- an value
Name and define the two areas of statistics?
Descriptive and Inferential:Descriptive statistics describe the data set.Inferential statistics use the data to draw conclusions about the population.
How can you define descriptive syntax?
those rules and principles which we use to produce the language
What is a definitive essay?
definitive means to define , so probably a descriptive paper
How would one define the word described?
It is difficult to define the word described as it is a descriptive word itself. The best way to try to define it is to give an account of words that include all the relevant qualities, events and characteristics.
Define the human poverty index?
The human poverty index is a collection of statistics set to measure the human condition. The different statistics are combined to make the index.
Define Phat in statistics?
p-hat is the 'proportion in your sample.' It may be given as a percentage, a proportion or you will have to figure it out as a fraction (proportion).
How does Mugenda and Mugenda define sample size?
According to research made at multiple websites; Mugenda and Mugenda, which is a company that focuses on research methods for various areas, define sample size by using statistics data and probability.
In Statistics define the 4 ARMC criteria?
1. variable is an antecedent 2. random assignment 3. values are maniipulated 4. controls
What percentage of workers were employed in Africa and Asia in 1900?
No statistics were kept back then. Moreover, in Asia and Africa the concept of 'unemployment' as we now define it, was practically unknown.
What is the best way to define imagery?
Imagery is the use of vivid and descriptive language that appeals to the senses to create a mental picture or sensory experience for the reader. It helps to enhance the reader's understanding by making the text more engaging and evocative. Good imagery allows the reader to connect with the text on a deeper level.
How many people in the US not working?
Everybody throws the phrase "working age Americans" around, but I can't find anyone who will define the term. What does this mean? Everyone between 16 and 64?.....Everyone between 18 and 60?.....They will toss all kinds of statistics around, but don't define their terms!
What are the major steps in social research?
By the time you get to the analysis of your data, most of the really difficult work has been done. It's much more difficult to: define the research problem; develop and implement a sampling plan; conceptualize, operationalize and test your measures; and develop a design structure. If you have done this work well, the analysis of the data is usually a fairly straightforward affair.In most social research the data analysis involves three major steps, done in roughly this order:Cleaning and organizing the data for analysis (Data Preparation)Describing the data (Descriptive Statistics)Testing Hypotheses and Models (Inferential Statistics)Data Preparation involves checking or logging the data in; checking the data for accuracy; entering the data into the computer; transforming the data; and developing and documenting a database structure that integrates the various measures.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. With descriptive statistics you are simply describing what is, what the data shows.Inferential Statistics investigate questions, models and hypotheses. In many cases, the conclusions from inferential statistics extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population thinks. 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. Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what's going on in our data.In most research studies, the analysis section follows these three phases of analysis. Descriptions of how the data were prepared tend to be brief and to focus on only the more unique aspects to your study, such as specific data transformations that are performed. The descriptive statistics that you actually look at can be voluminous. In most write-ups, these are carefully selected and organized into summary tables and graphs that only show the most relevant or important information. Usually, the researcher links each of the inferential analyses to specific research questions or hypotheses that were raised in the introduction, or notes any models that were tested that emerged as part of the analysis. In most analysis write-ups it's especially critical to not "miss the forest for the trees." If you present too much detail, the reader may not be able to follow the central line of the results. Often extensive analysis details are appropriately relegated to appendices, reserving only the most critical analysis summaries for the body of the report itself.
