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Response to the posting below(other students) up to 150 words each.
Task #1:The First Step of Data Analysis
Descriptive statistics are ways of organizing, describing and presenting quantitative which is numerical data in a matter that is concise, manageable and understandable. Descriptive statistics usually deal with variable individually and established, patterns found in each individual variable and describing a sample in terms of individual variables such as sex, race, salary and occupation through measures such as tools and percentages gives us a picture of some of the characteristics of a sample. Descriptive analysis is helpful for understanding the extent of an event or occurrence, such as the number of rapes on the college or the typical salary of Chow where for workers. This method can be used with both quantitative and qualitative methods and while numerical descriptions of the sample and or the, responses can be using both methods of research, they're not always using qualitative methods but must be used in quantitative methods. (page 170)
Descriptive Analysis
Descriptive statistics help us make sense of a large amount of data by finding both what is common or typical for variable what expectations this for variable. If an individual conducted a study that involves 100 people and you ask him to complete a survey that consists of 20 questions, this would result in 2000 answers and is considered a large amount of material for anyone to read through an attempt to understand. Descriptive statistics allow us to compile information in a concise, manageable and understandable manner so that the data can be examined relatively quickly. Many of us have use descriptive statistics for much of our lives without knowing it, for instance people can describe what their average weight is it within what range the way generally fluctuates. When Collating the average, also known as the mean and the range of the variable which is the weight, we are using one type of descriptive statistics call univariate analysis. Univariate analysis involves the examination across cases of one variable at a time and there are three major characteristics of a single variable that we tend to look at which is distribution, central tendencies and dispersion. (page 172)
Measures of Distribution
Distribution of data is a summary of the frequency of individual values or ranges of values for a very. The simplest distribution list every value of a variable and the number of people who had each value frequency is the number of times a day response occurs. An example of frequency is that we may discuss the frequency of metastasis for members of the class, which are the number of students who fail to attend the class and a frequency can be reported as a total meaning two of the 20 students were absent today and as a percentage which is 10% of the 20 students were absent today. Using distribution, one can also group responses so that they can report that information and more manageable terms, for instance one my group annual income of families of incoming freshmen into five categories, rather than a report in the list of the 100 responses. (page 172)
Measures of Central Tendency
Measures of central tendency are a number that is used to represent a set of responses for a variable. A central tendency is an estimate of the center of a distribution of values. One can think of central tendencies as a means to determine what is most typical, common and routine. The three types of estimates of central tendency are mean, median and mode and the mean is the most often used measure of central tendency.
A mean is a statistical average one can use for any variable that is collected through interval level or ratio level responses such as weight, miles driving, grade point average and age. The main is a precise way to detect what is typical because tackle is the middle score by taking all scores into account and if one wanted to find typical or average age of people in your class, one can simply add the ages of each person together and divide the sum by the number of people in your class. The second measure of central tendency is the median. The median is simply the midpoint of a set of numbers and to get an accurate median one must first order the data from low to high than find a number that falls in the middle. The third measure of central tendency is the mole and mode is the most frequently occur in response found for a variable. If one is seeking the mean for your classmates ages, the number 21 Occurring more than any other number, which means 21 will be the mole or the most frequently occurring number in the variable of age. Molds are most commonly reported when the researcher has used a nominal measure of a variable such as the number of Catholics Protestants and Muslims in a study. Measures of central tendency attempt to represent how data is grouped around what are the average for variable and it is highly probable that you use these methods without realizing. One needs to make sure that when choosing a statistical measure they are using the appropriate level of measurement. It is also important that a statistical mean is very sensitive to outliers. An outlier is an anomaly or result that is far different from most of the results in the group and is a number or variable that has extreme values that can score skew the overall results. Central tendency statistics report how much our data is similar or like. (pages 173-174)
Summary
Researchers should take advantage of the everyday ways one is able to obtain statistics. The median, mean and mode is used in a professional's daily activities and can be used in future situations. The statistics are a very important part of the process in analyzing research.
Reference:
Faulkner, Samuel S., & Faulkner, Cynthia A. (2014). Research Methods for Social Workers A Practiced-Based Approach Morehead State University (2nd ed.)
Task#2:Quantitative data analysis is defined as the process of utilizing a variety of statistical procedures to analyze numerical data.
Data analysis is conducted in steps:
The descriptive analysis of the individual variables is conducted
Inferential statistics are used to analyze the association between variables
Data analysis involves counting responses and using other numerical procedures to discover identifying characteristics of individual variables in a sample and the relationships between two or more variables in the sample (p.170).
The First Step of Data Analysis
The first two steps to analyzing numerical data are using descriptive statistics and then inferential statistics.
Descriptive statistics are ways of organizing, describing, and presenting quantitative data in a manner that is concise, manageable, and understandable.
Often helpful for understanding the extent of an event or occurrence
This method is used with both quantitative and qualitative methods.
Descriptive statistics involves all of the data from a given set, which is also known as a population. With this form of statistics, you don’t make any conclusions beyond what you’re given in the set of data. For example, if you have a data set that involves 20 students in class, you can find the average of that data set for those 20 students, but you can’t find what the possible average is for all the students in the school using just that data.
Descriptive Analysis
Helps to make sense of a large amount of data by finding what is common or typical for a variable and what exceptions exist for a variable (p.172).
Allows us to aggregate data so that data can be examined relatively quickly.
For example, most people can describe what their average weight is and within what range their weight generally varies.
Univariate analysis involves the examination across cases of one variable at a time. There are three major characteristics of a single variable that people tend to focus on: distribution, central tendencies, and dispersion.
Measures of Distribution
Distribution of data is a summary of the frequency of individual values or ranges of values for a variable.
Frequency is the number of times that a response occurs. For example, one can discuss how often a student is absent from class within a given week.
I think that data analysis is very important because it supports the researcher to reach to a conclusion. Therefore, simply stating that data analysis is important for a research will be an understatement rather no research can survive without data analysis. One of the most important uses of data analysis is that it helps in keeping human favoritism away from research conclusion with the help of proper numerical treatment. With the help of data analysis a researcher can filter both qualitative and quantitative data for an assignment writing project.

Response to the posting below(other students) up to 150 words each.
Task #1:The First Step of Data Analysis
Descriptive statistics are ways of organizing, describing and presenting quantitative which is numerical data in a matter that is concise, manageable and understandable.

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