Question 1:
Explain what is statistics, what are the key characteristics and what are the benefits of statistical data for meeting business objectives.
Statistics
Statistics is the science of understanding the relationship between the overall quantity and quantity of objective phenomena, also is a branch of mathematics that studies randomness and uncertainty regarding variables.
Use of probability theory to create mathematical models, collect data of the observed system, quantify the analysis, summarize and carry out the inference and predictions to provide the reference and basis for relevant decisions.
Applicable to the analysis of natural, economic, social, scientific and technical fields.
Statistics is divided into inferential statistics and descriptive statistics.
Inferential statistics is a statistical method for investigating how to use sample data to infer overall characteristics.
Descriptive statistics is a statistical method for studying data collection, processing and description.
Learning to find useful information from cluttered data to make a conclusions and further decision making.
The key characteristics of statistical data
It should be composed by facts.
Statistics is referring to data and the data must consist of aggregates of facts.
It easily effected by many factor.
The factor include time, place, family, phenomenon or situation.
Statistics should perform in numeral.
The data should relate to quantitative information. Hence, the measurement of data can be made possible.
Estimate and measure it accuracy
For acquire reasonable standard of accuracy the field of enquiry should minimize until suitable scope.
The benefit of statistical data for meeting business objectives
Price statistics
Commodity circulation is the exchange of goods through currency.
Studying the value of a commodity is expressed as the price of a commodity in exchange, whether the price level of a variety of commodity circulation, the changes is it reasonable will directly affects the smooth progress of commodity circulation.
Frequently, systematic collection, organization data and cumulative price level data, accounting for commodity price differences and parity and so on will helping achievement business objective.
Business statistics forecast.
Business statistics forecasting are not being negligence in business statistics. Only by making a scientific prediction on market conditions that can ensure the business decision and planning are based on a reliable basis.
Only this way can ensure the smooth flow of goods and attain good economic benefits.
For example: forecast of market price, commercial economic benefits and commodity demand.
Maximum the probability of prediction for doing effective decision making.
Collect data
May collect data about employee productivity, analyse the data to get solution in which an employee should improve their skill for maximum productivity.
Use data comparative analysis, which improves managerial efficiency.
Helps manager compare alternative situation and choose the best alternative option…
Can be used and reused for checking vary variables.
Explain the source and types of data and information business can be access.
Qualitative data:
Qualitative data are research materials which that are presented in non-numerical forms.
For example, the attitude of a person, gender, knowledge and clever or stupid; all these cannot be stated as a number to analyse it.
Quantitative data:
Quantitative data is the data which is countable or expressed in numerical values such as height or weight of a person, time spending of customer in waiting for goods or service and so on.
Primary data:
A primary data refers to the raw data that collected for some certain purpose and the data is directly collected from original source.
This type of data always will be concern by those who has their own research purpose or interested on something, so they will do the research by collecting data themselves.
Secondary data:
Secondary data is relative to primary data. The names of secondary data have already clearly indicated the characteristics of this data.
It is a data that obtained from the existing data results from other resources such as internet or website. In the simple word, secondary data is ready-made data.
Population:
Population is that all participants are involving in the survey and all of them will be taken consideration.
It has been done overall by involving all the target population.
Sample:
Sample is sampling a group of participant from population for the survey purpose.
As population is too large, some of the researcher may choose some of them as a sample to do the survey; this is easy for them to complete it more accurately and successfully.
The value of statistical methods when meeting business objectives and achieving competitive advantages.
Report:
The source of sleeping duration questionnaires is provided by students of IPK College.
From scenario one stated that two third of US high school students report would getting less than 8 hours of sleep on school night, and almost female students are likely to report not getting enough sleep than male student.
We through Google Form to get the result of the sleeping duration for IPK College student. We choose Google Form to make online questionnaire because it’s easily to distribute and may immediately make the summaries of data. The question we state has which class student from, gender, age, and sleeping hours on average per day.
Above 300 Students which is included degree student and diploma student in IPK College. On behalf to simplify the survey, we using sample data as selected 50 students out of above 300 students in IPK College.
In the research, there are 8 persons from DIA/16-2P, 3 persons from class DIA/17-1A, 21 persons from class DIA/17-2A, 5 persons from class DIA/6, 1 person from class DIB/17-2, 7 persons from class Degree2, 2 persons from class Degree 3, 1 person from diploma in accounting and 2 persons from class Degree 3.
In this questionnaire we applying quantitative method to collect the sleeping duration result.
The information data getting from the result were express as numerical and simply to understand. At the same time, the data will allow us to measure and analyse data more succinctly.
The collection of this data is primary data because the result is generated originally
On the result we getting from Google form stated that, within 50 students of IPK College has 74 % participants is female and 26 % is male.
Information data from Google form stated that, in 50 of IPK College student have 42% of female student and 10% of male student average sleeping hours within 6 to 8 hours per day, 14% of female student and 12% of male student sleeping hours were less than 6 hours, 14% of female student and 4% of male student average sleeping hours within 8 to 10 hours and have 4% of female student sleeping hours more than 10 hours per day.
Comparison:
For result we are getting is have 56% of female student and 22% of male student in IPK College sleeping hours less than 8 hours.
While for scenario are given by the question have 65 % of female participant and 73% of male participant sleeping hours are less than 8 hours.
14% of female student and 12% of male student sleeping hours are not up to 6 to 8 hours and are locate at unhealthy sleeping hours set by Ministry of Health.
In conclusion that, female student in IPK College is not enough sleeping than male student while for scenario 1 male not enough sleep than female.
Question 2:
Analyse and evaluate qualitative and quantitative raw business data from a range of examples using appropriate statistical method.
The different between qualitative and quantitative data
Qualitative data Quantitative data
Ordinal Discrete
Categorical Continuous
Subjective Objective
Deals with meaning Deals with numeral
Text, image Frequency
The data of statistical variables can be divided into two categories according to their nature.
Category data (Qualitative data)
Describing the quality or category, usually the non-numerical type of text or symbol, numerical pattern, but it only represents the code and no quantitative meaning
Quality data must be discrete data
Quantitative data
Describing how many and how much of the information, the information must be numeric.
Usually can divided into discrete data and continuous data
Category → . Nominal
↑ . Ordinal
DATA
↓
Quantitative → . Interval
. Ratio
Type of data
Nominal data
Data does not have logical and is basic classification data
E.g. service department employee, HR Administrator, attitude of the employees
Ordinal data
E.g. Job level of employees
Data which have a logical order, but the differences between values are not constant.
Interval data
E.g. age of employee, profit for the year
Data has logical order, continuous, has standardized differences between values, but no natural zero.
Ratio data
E.g. 50 employee of service department.
Data is ordered, continuous and standardized differences between value and a natural zero.
Three common use in sample statistics
Centralized location of data
Mean, median and mode
Mean is the most commonly used.
Degree of data dispersion
Use standard deviation to express
The ups and downs of the quality characteristic value reflects the process capability.
Distribution of data
Normal distribution for the statistics as a whole
Discuss mean and standard deviation of the normal distribution in the known context of general normal distribution.
Common descriptive used
Mean
One of the most effective measurement data core metrics.
Mode
Most common occurring value of a variable.
Median
Middle value that cut the distribution in half for a particular variable.
Some difficult to using excel perform the raw data analysis:
Excel has weird method of handing missing data
Programming analysis in excel is complicated
The mistake occurs during use the programming analysis are easily negligence.
Hard to know what excel is truly doing when applying a formula.
Critically evaluate the differences in application between methods of descriptive, exploratory and confirmatory analysis of business and economic data and critically evaluate the use of different types of chart and tables for communicating given variables.
Descriptive method:
Descriptive method means that the researcher needed to collect information on public and to present it.
Observation method:
Observation method is the researcher will focus on the participant actual behaviour.
For instance, the product analysis the investigator will concentrate for the product of participant’s behaviour.
Case study method:
Case study method it is takes for a long time to become a detailed research.
Case study method almost for this method the researcher gives based on the case document to the participant’s student studying or based on the case out with the participant comment.
Survey method:
For survey method is the investigator ask many people of question and in this method almost gathering by what most people are doing such as regarding with the behaviour, motivation, demographic and etc.
Exploratory method:
Secondary data:
The information data is already done by other investigator and only need taken out from book or magazine or newspaper to reinvestigate the data whether id true or not.
For example, almost this secondary data would provide by government as demographics, car accident rate and etc.
Experience survey:
During this investigation, involving the participant experience or knowledge in this particular situation.
Pilot studies:
This pilot study is already having or exist and it was a small-scale initial study lead in order to evaluate the time, cost and so on to improve the study design and to performance the full-scale project.
Confirmatory method:
This confirmatory method is used the test such as Z test, T test or quality control to whether measures of a construct are consistent with a researcher’s understanding of the nature of that construct.
Question 3:
Use the information above to determine the warning and action limit for the sample mean arrival time at a breakdown and construct a quality control chart to monitor the sample mean arrival time, on the graph paper provided within the answer book.
Mean: 38 minutes Standard deviation: 24 minutes n: 9
Warning limits:
x ̅-1.96 σ/√n to x ̅+1.96 σ/√n
= 38-1.96 24/√9 to 38+1.96 24/√9
= 38-15.68 to 38+15.68
= 22.32 to 53.68
Action limits:
x ̅-3.09 σ/√n to x ̅-3.09 σ/√n
= 38-3.09 24/√9 to 38+3.09 24/√9
= 38-24.72 to 38+24.72
= 13.28 to 62.72