The scenario of this report determines different data analysis techniques and its importance in order to resolve business and management related issues easily. This will enable learner to develop their knowledge in the context of statistics and data analysis approaches.
- A review published sources in order to gather economic and business-related information.
- Critically analyze sourced or atomic data by implementing different methods of statistics.
- Provide a clear application of different methods for a business planning process.
- Discuss findings on the basis of identified outcomes.
Business and economic data analysis is basically collection and organization of data so that a researcher can come to a conclusion (Anderson and et. al., 2014). Data analysis allows the person to answer various questions, derive important information and solve problems to come at a conclusion.
P1. Methods of data analysis:
Qualitative Research: This research involves the data and focuses on describing characteristics. This research does not use numbers. A better way to remember it is that it uses quality.
Quantitative Research: This research involves numerical data. It focuses on study of financial statements such as balance sheet, profit and loss account, cash flow statement etc to derive on some results.
Interpreting data from a variety of sourcesusing different methods of analysis: descriptive, exploratory and confirmatory:
Descriptive analysis: This is a preliminary stage of processing the data that uses summary of historical data to find useful information and possibly prepare the data for further analysis. Descriptive analysis of data is necessary as it helps to determine the normality of the distribution. Descriptive analysis is used to analyse the basic features of the data in the study.
Exploratory data Analysis: This is an approach for the analysis of the data that employs a variety of techniques to maximize insight into a data set, uncover the underlying structure, extract important variables, detection of outliers and anomalies, testing underlying assumptions etc. EDA is precisely an approach and not a technique on how to carry the analysis of data. EDA is a philosophy as to how we dissect the data, what we look, how we look, and how to interpret the data (Dey, MüIler and Sinha, 2012).
TECHNIQUES OF EXPLORATORY DATA ANALYSIS:
EDA techniques are graphical in nature which also includes quantitative techniques. EDA is heavily reliant because it gives graphical insight into the data which allows the analysts to open minded explore and entice the data to reveal structural secrets.
The particular graphical techniques involved in EDA are quite simple, which consists various techniques:
- 1. Plotting the raw data: 1) data traces 2) histograms 3) Bi-histograms 4) Lag plots
- 2. plotting simple statistics such as mean plots, standard deviation etc.
- Positioning such plots to maximize our natural pattern-recognition abilities.
Confirmatory data Analysis: Confirmatory data analysis and exploratory data analysis is somewhat similar techniques but in confirmatory analysis researchers can specify the number of factors that is required in the data and which measures are related to which latent variable. Confirmatory data analysis is a tool that is used for the confirmation or rejection of measurement theory. The advantage of confirmatory research is that results are more meaningful, the reason behind it is that in confirmatory research, one ideally strives to reduce the probability of falsely reporting a coincidental result as meaningful. This is called the probability of a type 1 error.
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Descriptive statistics is used to describe the basic features of the data in a study. It provides simple summaries about the sample and the measures. Descriptive statistics is the brief of coefficients that summarises a given data set. Descriptive statistics is broken down into measures of central tendencies such as (mean, median and mode) and measures of variability such as standard deviation and variance (Horton, Baumer and Wickham, 2015).
MEASURES OF CENTRAL TENDENCIES:
Mean: The mean or average is the most common of the central tendencies that is used to describe the data. The mean is calculated by adding all the values in a population or mean and dividing it by the total number of the values. For example, the mean or average of numbers scored by the students in an examination is calculated by adding all the marks scored by the students and then dividing it by the number of students. For example, consider the following test numbers scored by the students:
15, 21, 34, 24, 19, 32, 27, 38
The mean will be calculated by adding all the values (i.e. 210) which will be divided by 8 (210/8=26.25)
Median: The median is the number which is at middle of the given data. Median can be calculated by re-arranging the data in ascending or descending order and then taking the value in the middle. If the data is in odd counting, we will simply take the mid-value (i.e. N/2) if the data is in even count then the formula that is used will be (n+1)/2.
15, 15, 15, 20, 20, 21, 25, 36
There are 8 scores and score #4 and #5 comes in the halfway point. Since both of these scores are 20 the median is 20. If the two digits had different values, then we would have to interpolate to find out the median.
MODE: The mode is the number which has the highest frequency in the data set which have been set operation.
MEASURES OF VARIABILITY:
Standard deviation: This is measure which is used to calculate the amount of variation or dispersion of the data. Standard deviation which is low in value determines that the data points are close to mean of the set, while high standard deviation indicates that the data points are spread out over a wide range (Linoff and Berry, 2011).
Variance: This is used in statistics for probability distribution. It is a measurement of the spread between numbers in a data set. Variance has a central role in statistics, where some ideas that use it includes descriptive statistics, statistical inference, hypothesis testing and Monte Carlo sampling.
P2 Evaluation of data from qualitative and quantitative
Qualitative data analysis: This is non-statistical methodological approach which is mainly suggested by the concrete material at hand. While in quantitative research, the main approach to data is the statistical and make that data in the tabular form. Findings are normally descriptive in nature. However, in nature conclusive only throughout the numerical framework.
This is normally assist prejudice which quantitative research which is objectives vs subjective. This is normally a gross over simplification. Instead of comparing two approaches which are as follows. Quantitative research aims at explanatory laws. On the other hand, qualitative research could be elaborated as descriptive. Qualitative research calculates the hope of emerging universal laws while qualitative research could be elaborated as the investigated of what is presumed to be the changing reality. Qualitative research does not claim about what is elaborated in the universal process and henceforth, replicable. The main differences normally cited between these kinds of research covers (Neave, 2013).
Normally, qualitative research is the vast, investigated and reliable process which contribute to detailed understanding of the context. Quantitative research emerges valid population related and common data which is fixed to forming cause and effect connection.
Quantitative data analysis: This is simply the mean of implemented of quantitative and statistical tools to assess investment opportunities and make decisions. The quantitative work also assists to elaborates distribution of third sector organisations, assess their contribution to the community and economy and assess their dynamics. This research is totally aimed to enhance the outstanding of the third sector via huge scale programme of quantitative work. The data not just on third sector firm and their resources, but likewise on both financial inputs to the sector and human inputs.
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Difference between Qualitative and quantitative research analysis have been mentioned as under:
Assess and evaluate social interactions
Test hypothesis, check cause and effect. Emerge forecasting for the future time period.
Assess selected intentionally.
More and selected randomly.
Words, image, aims
Numbers and statistics