Module 1 Flashcards
Descriptive Statitistics
Descriptive statistics emphasizes simply describing the characteristics of a set of data. In other words, descriptive statistics is the tabular, graphical, and numerical summaries of data. (OpenStax, 2019)
Inferential Statistics
Inferential statistics allows us to make generalizations, estimates, forecasts, or other types of findings based on the data. For example, what if we were to try and summarize data on all residents of the United States?
While this wouldn’t be impossible, there are often time and cost limitations that don’t allow for data to be collected. In this case, instead of analyzing the entire population (the set of all elements of interest in a particular study), we look at a subset of the population known as a sample. This process of using data obtained from a sample to make estimates or test hypotheses about the characteristics of a population is called a statistical inference. (OpenStax, 2019)
Statistic
which is a number that represents a property of that sample
Parameter
which is a numerical characteristic of the whole population (OpenStax, 2019)
Random Sample
has the same characteristics as that population. When selecting a random sample, it is done so that every member of the population has an equal chance of being selected (OpenStax, 2019).
Simple Random Sampling
is a straightforward method for selecting a random sample; assign each member of the population a number. Use a random number generator to select a set of labels. These randomly selected labels identify the members of your sample (OpenStax, 2019).
Stratified Sampling
is a method for selecting a random sample used to ensure that subgroups of the population are represented adequately; divide the population into groups (strata). Use simple random sampling to identify a proportionate number of individuals from each stratum (OpenStax, 2019).
Cluster Sampling
is a method for selecting a random sample and dividing the population into groups (clusters); use simple random sampling to select a set of clusters. Every individual in the chosen clusters is included in the sample (OpenStax, 2019).
Systematic sampling
is a method for selecting a random sample. First, list the members of the population. Use simple random sampling to select a starting point in the population. Let k = (number of individuals in the population)/(number of individuals needed in the sample). Choose every kth individual in the list starting with the one that was randomly selected. If necessary, return to the beginning of the population list to complete selecting your sample (OpenStax, 2019).
Convenience Sampling
which is used to select individuals that are easily accessible and may result in biased data
Data
the facts and figures collected, analyzed, and summarized for presentation and interpretation
Data Set
All of the data collected in a particular study
Elements
are the entities on which data are collected.
Variable
is a characteristic of interest for the elements
Observation
The set of measurements obtained for a particular element
Categorical (qualitative) Variables
can be summarized by counting the number of people or objects that fall into a specific category (OpenStax, 2019). When someone places a person or object into a category, they are using a categorical measure.
Quantitative Variables
Make it possible to determine how much of something is present, not the category to which it belongs (OpenStax, 2019).
Discrete Quantitative Variables
can be only certain values in an interval with possible gaps in the interval that are not available. For instance, if you were to go to the store to buy tea, you would only be able to buy full boxes of tea. So, you could buy 1, 2, or 3 boxes, but you could not buy 1.5 boxes, since it does not come in half boxes
Continuous Quantitative Variables
can be any value within an interval. Here you can think about how much fuel is in your car’s gas tank. Depending on the accuracy of your measurement, it could be 12.1 gallons, 12.14 gallons, 12.142 gallons, or even 12.1424 gallons at any given moment. All values can be used in the measurement (OpenStax, 2019).
Nominal Level
consists of category names or numbers only to identify membership in a group or category (OpenStax, 2019).
Ordinal Level
consists of numbers to represent “greater than” or “less than” measurements in things such as rankings or preferences (OpenStax, 2019).
Interval Level
contains the ordinal level along with a unit of measurement that allows one to describe how much more or less one object has than another. In an interval level, there is no “true or absolute” 0 value. Temperature in degrees Fahrenheit or Celsius is an example of an interval level
Ratio Level
contains the interval level, but it also includes an absolute zero, and multiples have a meaning (OpenStax, 2019).
Frequency Distribution
is a table that shows classes of data and the number of cases in each class.