Statistics
can be explained as science which deals with the collection, analysis,
interpretation as well as representation of data (Ayyub and McCuen, 2016).
Effected by many causes:
Statistics is associated with data as well
as it is stated that it should be based on certain logical facts.
Numerically expressed:
According to Zhang (2016), it can be stated
that investigating the effects of one factor by neglecting the effects of other
factors is not easy. Here, the effects of all factors must be examined individually
and in combination as their effects may change with the change of location,
time or situation.
Numerically expressed:
Data, called statistics, must be expressed
in the number form to measure data. This means that data must appear in
quantitative form.
Enumerated or estimated accurately:
Statements must be accurate and concise.
The enquiry should not be very large for reasonable accuracy. Even if the data
is infinite or very large, it is not even possible to count the data and may
not reach a reasonable level of accuracy (Zhang, 2016).
Collected in a systematic manner:
Another characteristic of statistics is
that data should be collected systematically. Randomly collected data leads to
difficulties and inaccurate conclusions in the analysis process. In order to
collect statistical data, researchers must develop an appropriate plan to
collect as well as analyse the data (Rees, 2018). If this is not the case, the
reliability of the data will decrease. Therefore, the data must be collected
correctly to get the correct results.
Collected for a predetermined purpose:
Before collecting data, it is essential for
the researcher to understand that why they are collecting data and what they
are gathering (Rees, 2018). Collecting data without any specified information
regarding the goal the researcher cannot gather sufficient and reliable
details.
Capable of being placed in relation to each
other:
Normally, data is collected for comparison.
If the numbers collected cannot be compared, they will lose much of their
meaning.
There
are different methods that can be used to collect the most suitable and
reliable information regarding a certain research objective. Different types of
methods are as follows;
The
primary method of data collection can be explained as the researchers collect data
by themselves, such as by observations and conducting surveys. Additionally,
researchers can gather both qualitative and quantitative data by the help of
primary research technique (Sen and Singer, 2017).
The
secondary research method of data collection can be explained as retrieving
data from online research engine or already published data on the official
website (Sen and Singer, 2017).
Sources and types of data and information
businesses can access:
It
enables researchers to get the most reliable and relevant information quickly.
The significance of using questionnaire survey can be given as it enables the
researchers to complete the research anonymously; it is inexpensive, enables
the researchers to analyse as well as compare information easily (Bell et al.,
2018). However, there are certain challenges that are associated with the
questionnaire survey such as researchers may not get reliable responses, can
get biased information and it does not enable the researcher to get a complete
story regarding a certain research phenomenon.
Interviews
are frequently used in the research where the researchers aim to view the perspective
of others regarding a certain research phenomenon. It enables the researchers
to understand the experiences of others. The interview method is preferred
because it enables the researchers to get indepth and details information.
However, data collection with this technique requires a lot of time as well as
it is quite difficult to compare and analyse the information obtained by the
interviews (Bell et al., 2018). In addition, qualitative research methods also
include the collection of data from already published articles and journals
available it enables the researchers to access comprehensive as well as
historical information regarding research phenomenon.
The
population can be explained as a set of data which involves all elements while the
sample comprises one or more observations of the population. Moreover, based on
the sampling methods, it can be stated that the sample comprises on few numbers
of observations as compared to the population. Parameters of the population are
mean and standard deviation, while the measurable characteristic of the sample
is known as a statistic (Maronna et al., 2019).
Value of employing statistical methods when
meeting business objectives and achieving competitive advantage
Lei
et al., (2017, p.59) stated that statistical methods play an important role in
business management to achieve competitive advantage. One of the functions of
statistics in business organisations is to inform the manager about the
performance as well as the productivity of employees and managers within the
organisation. The manager collects data
on employee productivity, such as the number of jobs done or the number of
units produced. Business organisations must analyse the data to find ways that can
be used by the employee to maximise productivity. Many companies also collect
data and analyse it with statistical techniques on the commitment of employees
at work, which can be monitored to reduce the rate of employee turnover.
Statistical
techniques of descriptive statistics, as well as inferential statistics, are
frequently used in the business researches to analyse the gathered data. The statistical technique of descriptive
statistics is used to summarise a large set of data and to get meaningful
information. Descriptive statistics enables business organisations to understand
a specific set of data. Descriptive statistics describe a sample as well as
there is no uncertainty because it only describes data (Lei et al., 2017, p.59).
However, inferential statistical enables the researchers to analyse data and
make effective decisions. The goal of using inferential statistics is to draw a
conclusion based on the collected facts and figures.
Statistical techniques in business
intelligence enable to analyse past performance, predict future business
prospects, and manage the business effectively. Statistics can describe markets
which can help business organisations to announce advertising, set prices and
respond to changes in consumer demand. A descriptive analysis examines what
happened and helps explain why (Laursen and Thorlund, 2016). Using historical
data, managers can analyse past performance and mistakes. Also known as
"cause and effect analysis". Common applications for descriptive
analysis include sales, marketing, finance and operations. The forecasting
techniques use a variety of statistical methods (such as calculation models and
mining) to predict the likelihood and future development based on past or
historical data. The best possible assessment of what is happening is going
over the records of what happened (Laursen and Thorlund, 2016).
Different
statistical techniques can be used by the business organisations to analyse the
gathered data such as for the descriptive statistics and summary of data researchers
can calculate mean, median, mode, standard deviation, variance and range of
data which can help the researchers to determine the rate of fluctuations in
their productivity based on the gathered data. In addition, the technique of
graphical representation can also be used by the researchers to visualise the
fluctuation as well as the trend of the data. Based on the analysis researcher
can demonstrate that either a certain strategy which is being used in the
organisation is efficient or productive, or they need to change their
strategies (Mertler and Reinhart, 2016. As an illustration, an organisation can
gather the data of employee performance and productivity before implementing a
certain strategy and analyse the trend of the overall productivity which can
help the management to decide either to change a certain strategy to enhance
the productivity or the implemented strategy is already helping the
organisation to achieve the specified goals.
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Find Out MoreThe
data for monthly adjusted shared price for Apple, Microsoft, Amazon and Netflix
for the last five years and for analysis of the data Apple and Microsoft have
been selected. The provided data has been analysed with the help of inferential
and descriptive statistics.
As discussed above, descriptive statistics are used to summarise data and get meaningful values from a large set of data. From table 1, it can be observed that average adjusted price for the Apple is 133.72632 with a standard deviation of 38.58 and Microsoft is 66393 with a standard deviation of 25.99 and average NASDAQIXIC is 5844.1046 with the standard deviation of 1178.917. standard deviation enables the researchers to determine how much data is far from the central values, and high standard deviation indicates high fluctuation in the data.
Apple 

Microsoft 

NASDAQIXIC 

Mean 
133.7262 
Mean 
66.39398102 
Mean 
5844.588 
Standard Error 
4.97412 
Standard Error 
3.355728871 
Standard Error 
152.1046 
Median 
117.294 
Median 
57.145624 
Median 
5317.84 
Mode 
#N/A 
Mode 
#N/A 
Mode 
#N/A 
Standard Deviation 
38.52937 
Standard Deviation 
25.99336407 
Standard Deviation 
1178.197 
Sample Variance 
1484.512 
Sample Variance 
675.6549755 
Sample Variance 
1388149 
Kurtosis 
0.53538 
Kurtosis 
0.892740606 
Kurtosis 
1.24886 
Skewness 
0.681033 
Skewness 
0.685135537 
Skewness 
0.478652 
Range 
145.1161 
Range 
87.339714 
Range 
3866.92 
Minimum 
79.96438 
Minimum 
36.420288 
Minimum 
4242.62 
Maximum 
225.0804 
Maximum 
123.760002 
Maximum 
8109.54 
Sum 
8023.575 
Sum 
3983.638861 
Sum 
350675.3 
Count 
60 
Count 
60 
Count 
60 
With the help of descriptive statistics,
the data can be just summaries; however, the trend and fluctuations in the
shared price for Apple, Microsoft can be visualised with the help of a line
graph. There are different types of graphical representations such as
histogram, line graph and scatter plot histogram enables the researchers to
visualise the frequency distribution, line graph enables the researchers to
determine the fluctuations within the data set over a period of time while scatter
plot enables the researchers to determine the existence of different data points around the mean (Mertler
and Reinhart, 2016). The data which has been used in the study is timeseries
data such as shared price in last five years for every month therefore, in
order to visualise the fluctuations in the data set line graph has been
used.
Figure
1, determines the trend for the shared price of Apple for last five years and
it can be visualised that the shared price of Apple indicates a continuous
fluctuating trend which indicates uncertainty in the data set. Additionally, it
can be visualised that the fluctuations with the shared price of Apple are in
increasing pattern as well as a significant increase, then decline can be
observed during 2108.
Figure 1: share price trend of Apple
From figure 2, the shared price of Microsoft can be observed and it is clear that from the last five years, the shared price for Microsoft is continuously increasing. Moreover, it can be observed that eh standard deviation for the shared rice of Microsoft is also small indicating a low rate of fluctuation in the data.
Figure 2: the share price of Microsoft
Correlation can be explained as an inferential statistic that is frequently used to demonstrate the strength of association between two or more variables. Additionally, according to the range of correlation is1 to +1 as well as the value less than 0.5 enables the researchers to state that there is weak association between variables while if ha value is greater than 0.5 than the researchers can state that there is a strong association between the variables (Stephens, 2017, p. 195). In this report eh association of the shared price with the NASDAQIXIC has been analysed and the results are mentioned in table 2. Table2 indicates that shared price of Apple, as well as Microsoft, has a positive as well as a strong association between shared price and NASDAQIXIC.

Apple 
Microsoft 
NASDAQIXIC 
Apple 
1  
Microsoft 
0.929817 
1  
NASDAQIXIC 
0.967538 
0.972825 
1 
In addition, regression analyses can also be used for the inferential statistics such as to demonstrate that either NASDAQIXIC has a significant impact on the shared price of Apple and Microsoft or not regression analysis can be used. Table 3, indicates that there is a strong relationship between the shared price of organisation and NASDAQIXIC as value for eh coefficient of correlation is obtained as 0.98 as well as value for the coefficient of determination is obtained as 0.97 indicating that model is fine fitted.
Regression Statistics  
Multiple R 
0.987766 
R Square 
0.975682 
Adjusted R Square 
0.974829 
Standard Error 
186.9265 
Observations 
60 
ANOVA enables the researchers to make
inference such as either to accept a certain hypothesis or not. According to Stephens,
(2017, p. 195) null hypothesis of the study can be accepted if pvalue would be
greater than 005 as well as if the value of fstatistics calculated would be
less than the value of Fstatistics tabulated. From table 4, it is evident that
the Pvalue is less than 0.05 which leads the researchers to state that there
is a significant impact of NASDAQIXIC on shared price.
ANOVA 


df 
SS 
MS 
F 
Significance F 
Regression 
2 
79909128 
39954564 
1143.469 
9.98E47 
Residual 
57 
1991667 
34941.53 

Total 
59 
81900796 



Forecasting is used by the researchers to make an inference that either with the help of current strategies, they would be able to increase their productivity or not. Exponential smoothing is a statistical technique that is used by the researchers for forecasting. It enables the researchers to determine the expected future trend based on the previously available data (Kong et al., 2017, p.897). In this study, exponential smoothing has been used to forecast the shared price of Apple and it can be observed from the figure that if the organisation will work on the similar strategies and will not look for innovation than there is a possibility that the share price of organisation would be decreased.
Figure 3: exponential smoothing for Apple
Figure 4, indicates forecasting for the
shared price of the Microsoft and based on the results it can be stated that
the if the organization will continue working with similar strategies then
there is a possibility that the shared price of Microsoft will decrease.
Figure 4: exponential smoothing
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Find Out MoreThis
assignment was aimed to explore the effectiveness of the statistical technique
for business management. Based on the discussion it can be concluded that with
the help of descriptive statistics organisations can get meaningful values from a large set of
data, while with the help of graphical representation researchers can visualise
the trend of data however with the help of inference statistics researchers can
make inference regarding certain area of interest such as productivity of organisation based on current strategies.
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