Our Statistics and Probability experts always do their best to answer most of the questions posted on this platform. We have always provided free myMathlab Answers on our website. If we have not answered this question, then we'll be on it very soon. However, sometimes we get engaged, and we might not answer it within the time you expect. In any case, if you are looking to pay someone to help you with your online classes , or to answer this question, then we are at your service. Our experts can also help you complete your online statistics classes. You can contact us via WhatsApp or use MyMathLab Homework Help link.
Hi, Actually, this question is simple but it might complicate someone who has never heard of statistics before. To begin,
When we conduct a study we examine a group of items or people called the population. Unfortunately we are not always afforded the opportunity or the money to measure or survey each person or item in our population. Therefore we take a sample which is a subset of our population. If we do measure everything within our population we would call this a census. The measurement would be a parameter. From a sample of a population we get a statistic. For example if I were to take a sample of 15 people and weigh each person I could average the data. This average would be my statistic. If I were to measure each and every person in my population I would then have a census. Whenever we draw a sample we will have a statistic. Whenever we measure the entire population we get a parameter. Our sample must represent our population. That is we cannot draw a sample from something that is unrelated to our population. Example: If I am interested in the average height of the students at Middlesex Community College I would not take a sample of students from UMASS Lowell.
What are data types in statistics?
Quantitative Data: data that is measurable. It can be discrete or continuous. Discrete data can only take on certain values in an interval. Continuous data can take on any value in the interval. Ex: Number of children each person has. This would be discrete data since the data can only take on whole numbers. Time it takes to run a race is continuous data since time can take on any value.
Quantitative data is more difficult to argue with..................
Example: You walk into the classroom and you state "wow it's cold in here" but your friend thinks it is at the perfect temperature. Who is right? Actually, neither, you are simply categorizing the temperature in the room. This is qualitative data. Now, say you walk into the same room and read the thermostat and state "wow it's 65 degrees in here".This is measurable data and therefore quantitative data. There are less arguments with quantitative data. You and your friend can argue all day as to whether or not the room is cold or just right, but you cannot argue with the fact the room is at 65 degrees F.
What are data Levels?
There are different levels of measurement depending on whether you have qualitative data or quantitative data.
If the qualitative data does not have a ranking or ordering it is a nominal level of measurement. For example, if you categorized people by hair color, brown hair doesn't come before blonde or black hair. In other words there is no order.
If the qualitative data has an order then it is an ordinal level of measurement. An example would be if you were categorizing icecream as good better or best. In this case the order has a meaning.
2. Quantitative Data:
If the quantitative data has a true zero then ratios and intervals make sense. This type of data is at the ratio level of measurement. For example, if you were measuring the time it takes for runners to complete a race. There is a true zero, the start of the race. f you finished the race in 3 minutes and I finished the race in 6 minutes you could say you were twice as fast (ratio).
If the quantitative data does not have a true zero and ratios do not make sense that his data is at the interval level of measurement. For example, if you look at important dates from history, 1776, 1945, 2001. In makes sense to say that the difference between 1945 and 2001 is 56 years (interval), you would not say 1776 is twice as old as 888. This is because the choice of the year 0 is arbitrary.