Describe various scales of measurements. Provide two examples of .pdf

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There are four scales of measurement: nominal, ordinal, interval, and ratio. Nominal measures classify data into categories without order, such as gender or race. Ordinal measures classify data into ranks where differences between ranks are unknown, like class rank or socioeconomic status. Interval measures classify ranked data where differences are meaningful but there is no true zero, for example temperature in Celsius or time. Ratio measures also classify ranked data where differences are meaningful and there is a true zero, allowing for ratios, such as with age or height.

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Describe various scales of measurements. Provide two examples of each scale.
Describe various scales of measurements. Provide two examples of each scale.
Solution
Nominal Level
Level of measurement which classifies data into mutually exclusive, all inclusive categories in
which no order or ranking can be imposed on the data.
Example: Gender, Race
Ordinal Level
Level of measurement which classifies data into categories that can be ranked. Differences
between the ranks do not exist.
Example: class Rank, socio economic status( low, medium,high)
Interval Level
Level of measurement which classifies data that can be ranked and differences are meaningful.
However, there is no meaningful zero, so ratios are meaningless.
Example: Temperature in Celsius, time
Ratio Level
Level of measurement which classifies data that can be ranked, differences are meaningful, and
there is a true zero. True ratios exist between the different units of measure.
Example: age, height

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Types of Statistical Analyses Matrix Carrington Sherman, Donna Crawford, Henry Izeke, Stella Crozier, Trish Gordon HCS 542 April 2, 2018 Lane Baggett Purpose Example of when it would be used provides information about significance of differences between groups provides information about significance of relationships between variables provides information about a single sample vs. two or more samples is parametric or nonparametric Descriptive Statistics: Mean The average Average grades in the class Mean is different from the median because it is the sum of the data set then divided by the total of the number of the data set. So, the average. Mean and median are both a type of average A single sample is getting average of the data set Two or more samples you are getting mean of all means or the average of all the samples Parametric Descriptive Statistics: Median Midpoint in a data set Median salary; Middle of a groups salary which different from the mean because low and high salaries could way the average. The median is different from the mean because it is the midpoint of the data set or sample being used. The mean and median can be the same but rarely Median and mean are both a type of average With one set or sample it is simply the midpoint such as 3, 5, 12; 5 is your midpoint. If there is two sets or samples put all the numbers together and then find the midpoint. Parametric Descriptive Statistics: Mode Value that appears most often Looking at bar chart of incomes that is mostly around minimum wage then you have a couple that make a million dollars. Mode shows the top number or numbers that are most used but it can affect both the mean and median. Mode can affect both the median and the mean There can be no mode One mode or numbers: unimodal Two modes or numbers: bimodal Three modes or numbers: trim (Smith, 2018)odal Parametric Descriptive Statistics: Ratio Variables Ratio variables are used to show comparisons between two or more samples. They are characterized by having answers that are numbers on a scale; the difference between two samples is expressed as having a numeric value which is significant and where a zero response indicates that there is none of that variable, also known as having a true zero. Used for measurements such as height, weight and BP. The significance of differences between groups is expressed as a ratio of two measurements that are being compared. For example, a weight of 4 grams is twice the weight of 2 grams, or a 2 to 1 ratio. Used to show comparison between two or more variables. Can be used to compute the following: how frequent one variable occurs (counts) compared to another, mean, median, mode, percentiles of variables, add or subtract, standard deviation, and ratio. The difference between variables can be quantified. There is an order to the values on the scale. One sample demonstrates what value is present. Two or more samples demonstrate comparison. Parametric Descriptive Stati ...

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Categorical Data Categorical data represents characteristics. Therefore it can represent things like a person’s gender, language etc. Categorical data can also take on numerical values (Example: 1 for female and 0 for male). Note that those numbers don’t have mathematical meaning. Nominal Data Nominal values represent discrete units and are used to label variables, that have no quantitative value. Just think of them as „labels“. Note that nominal data that has no order. Therefore if you would change the order of its values, the meaning would not change. You can see two examples of nominal features below: The left feature that describes a persons gender would be called „dichotomous“, which is a type of nominal scales that contains only two categories. Ordinal Data Ordinal values represent discrete and ordered units. It is therefore nearly the same as nominal data, except that it’s ordering matters. You can see an example below: Note that the difference between Elementary and High School is different than the difference between High School and College. This is the main limitation of ordinal data, the differences between the values is not really known. Because of that, ordinal scales are usually used to measure non-numeric features like happiness, customer satisfaction and so on. Numerical Data 1. Discrete Data We speak of discrete data if its values are distinct and separate. In other words: We speak of discrete data if the data can only take on certain values. This type of data can’t be measured but it can be counted. It basically represents information that can be categorized into a classification. An example is the number of heads in 100 coin flips. You can check by asking the following two questions whether you are dealing with discrete data or not: Can you count it and can it be divided up into smaller and smaller parts? 2. Continuous Data Continuous Data represents measurements and therefore their values can’t be counted but they can be measured. An example would be the height of a person, which you can describe by using intervals on the real number line. Interval Data Interval values represent ordered units that have the same difference. Therefore we speak of interval data when we have a variable that contains numeric values that are ordered and where we know the exact differences between the values. An example would be a feature that contains temperature of a given place like you can see below: The problem with interval values data is that they don’t have a „true zero“. That means in regards to our example, that there is no such thing as no temperature. With interval data, we can add and subtract, but we cannot multiply, divide or calculate ratios. Because there is no true zero, a lot of descriptive and inferential statistics can’t be applied. Ratio Data Ratio values are also ordered units that have the same difference. Ratio values are the same as interval values, with the difference that they do have an absolute zero. Good e ...

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Many programming languages, especially older ones, provide no language support for concurrency. C and C++ are examples of such languages. Is it essential that a language include syntax for concurrency to be able to write concurrent programs in that language? If not, how is it accomplished? What are the advantages and disadvantages of including support for concurrency in a language? Solution Advantages: 1).Although the tasks are running and the user can relate with the applications. 2).It has the availability of services. means long running the tasks will not delay tiny running ones. 3). complex programs make better use of many resources. 4).tasks are requiring certain pre conditions can hang and wait untill the pre conditions are seize and then restart implementation properly. Disadvantages: 1). a chance of corrupting consistent state of program by concurrent tasks. 2).Deadlock may occur. 3).It would be masters exponential number of interleavings due to different schedules..

Many programming languages, especially older ones, provide no langua.pdf

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Inferior nasal concha Lacrimal bone Mandible Maxilla Nasal bone Palatine bone Vomer bone Zygomatic bone Forms bridge of nose Only movable bone in the facial skeleton Contains coronoid process Creates prominence of cheek inferior and lateral to the eye Contains sockets of upper teeth Forms inferior portion of nasal septum Forms anterior portion of zygomatic arch Scroll-shaped bone in nasal passage Forms anterior roof of mouth Contains mental foramen Forms posterior roof of mouth Small bone in medial wall of orbit Forms a very small portion of inferior orbit Contains large sinus inferior to orbit Solution.

Inferior nasal concha Lacrimal bone Mandible Maxilla Nasal bone .pdf

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