Question

Answer each question in More than two sentences with your own Example? ( wright this by word )
1. Describe how Descriptive statistics is helpful in our real life? Give examples.
2. Describe the important applications on Health Statistics with an example?
3. What makes the difference on Discrete and Continuous data to make action statistically?
4. How will you explain discrete and Continuous data with descriptive Statistics?

Answer 1

1. Descriptive statistics helps to simplify large data amount in a simple summary. Chart or graph can be used for this. It is broken down into measures of central tendency and measures of variability (spread). Measures of central tendency include the mean, median and mode, while measures of variability include standard deviation, variance, minimum and maximum variables.It plays a big role in the medical field, quality testings, and even weather forecasting. It answers the question, “what happened?” which makes it the simplest form among other types.

An example for descriptive statistics is When you came to a doctor for a check-up, the first question that he often asks is, “What do you feel?”. The next thing he would do is to either ask you more questions or perform some tests to help him understand your condition better. This will also help him determine what is likely to happen based on the test results. Finally, he’ll give you the prescriptions that you need.

Take whatever can be classified and you can do descriptive stats on it. In real life we need data to make it easy, we can enter it ourself.We can just use Excel sheet for that. Go ahead and make columns of our workout - walk, run, yoga, cycle, weightlifting, swimming, whatever we expend energy on - our rows can be “minutes spent.” The more data we have, the better we can change up our routine to suit our goals, endurance, strength, flexibility, balancent. If you’ll but enter these figures daily, then once you have 50 days you’ve got a statistical amount.

2. Health statistics are numbers that summarize information related to health.Researchers and experts from government, private, and non-profit agencies and organizations collect health statistics. Health statistics include both empirical data and estimates related to health, such as mortality, morbidity, risk factors, health service coverage, and health systems. They use the statistics to learn about public health and health care. Some of the types of statistics include,

• How many people in the country have a disease or how many people got the disease within a certain period of time
• How many people of a certain group have a disease. The groups could be based on location, race, ethnic group, sex, age, profession, income level, level of education. This can help identify health disparities.
• Whether a treatment is safe and effective
• How many people were born and died.
• How many people have access to and use health care
• The quality and efficiency of our health care system
• Health care costs, including how much the government, employers, and individuals pay for health care. It could include how poor health can affect the country economically
• The impact of government programs and policies on health
• Risk factors for different diseases. An example would be how air pollution can raise your risk of lung diseases
• Ways to lower risk for diseases, such as exercise and weight loss to lower the risk of getting type 2 diabetes

By identifying statistical trends and trails, health care providers can monitor local conditions and compare them to state, national, and international trends. Health statistics provide empirical data to assist in the allocation of public and private funds and help to determine how research efforts should be focused.Health statistics are records of births, marriages, divorces, and deaths. Demographics describe a specific population group, often defined by geographic region. Health statistics, also called mortality and/or morbidity statistics, detail the incidence of certain diseases and conditions.

3. If the data can have an infinite number of possible values, then it's continuous, and if it can only have a finite or limited number of possible values, then it's discrete. One way we can identify continuous data is if we can calculate it using a function.Discrete data is information that can only take certain values. Continuous data is data that can take any value. Height, weight, temperature and length are all examples of continuous data.These are the main difference between discrete and continuous data to make actions statistically.

Examples of discrete data include the number of people in a class, test questions answered correctly, and home runs hit.A variable is a quantity whose value changes. A discrete variable is a variable whose value is obtained by counting. A discrete random variable X has a countable number of possible values. A continuous variable is a variable whose value is obtained by measuring.

4.Descriptive statistics e.g. the mean, mode, median, and standard deviation. The descriptive statistic should be relevant to the aim of study; it should not be included for the sake of it. If you are not going to use the mode anywhere, don't include it. Identify the level or data.But discrete statistics uses counting and continuous statistics uses measuring .