570 Statics for management ASSIGNMENT 2

As a Research Analyst. My company is planning to improve information systems and decisionmaking process by applying some statistical methods. More specifically I am required to show my understanding by evaluating and analyzing business data (financial information, stock markets) or microeconomics or near macroeconomic issues. Here, future trends plans, etc. related to the research topic. All variables can be nominal or order, interval or rate. All methods to be explored including information about data, concept of information and knowledge, including converting data and information into knowledge, how data is collected and discovery transformation steps and using descriptive analysis, discovery, and validation techniques.

ASSIGNMENT 02 FRONT SHEET

Qualification BTEC Level 5 HND Diploma in Business

Unit number and title Unit 31: Statistics for management

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Contents

I Introduction

In this article, I choose the issue of how many dental implants are exported every day to various dental hospitals and private clinics The goal and scope

of this study was to examine the amount of dental equipment that our business has supplied to the hospital as well as the income that we haveearned I use Excel to summarize goods, sales, buyers, and the dates on which we deliver our products When we sell a unit of a commodity, wesummarize it in Excel and store it in our database on a daily basis

II Main Content: 1 Analysing and evaluating qualitative and quantitative raw business data from a range of

examples using appropriate statistical methods

Qualitative data

Approximate data and characteristic data are examples of qualitative data Capable of observing and recording qualitative data This data is numerical in nature This data was gathered by assessment, informal interviews, focus group polls, and other related means In statistical data,qualitative data is also known as categorized data because it can be classified and arranged based on the properties and features of objects orphenomena

non-Advantages of Qualitative research:

• Understanding behaviors is possible when consumption habits shift often Companies can be perplexed if this occurs unexpectedly The mechanismcreated by qualitative analysis has the power to explain why behaviors can be altered You may also include a detailed description, which can cause theorganization to react to shifts of perception Since qualitative studies will help us to truly consider behaviors It is now easier to develop relationshipswith customers

• This is a content generator: even for seasoned advertisers, discovering new ways to show old content can be challenging Qualitative analysisapproaches gather real statistics from detailed socioeconomic demographic data These concepts are then translated into data that can be used to

generate useful content that represents the recommended brand knowledge If this procedure is followed properly, everybodywill benefit from a refined and advantageous value proposition

• It offers predictability: individuals who have similar beliefs would have similar thought habits They can also purchase comparable goods Sincequalitative analysis evidence is dependent on perspective, it is predictive of nature It will gather the trademarks that distinguish an individual and usethem to recognise individuals who have common tastes or ways of thinking, allowing the company to produce more useful content, products, andservices

Disadvantages of Qualitative research:

• It may loss data: Before data can be obtained in qualitative analysis, it must be accepted by the researcher This means that other types of analysis donot necessitate a high level of trust in the data collection process Researchers who are unable to see the required data during observation will missthis data, limiting the precision of qualitative analysis findings It can also lead to incorrect findings in some scientific studies

• This can be time-consuming: since researchers take several detours while gathering data, the processing time will be extended It also takes time to gothrough all of this extra information Each data point is measured subjectively, so its validity is often debatable Other analysis types have stringentcriteria and standards for the obtained data, which can be analyzed and used more easily than qualitative research data

• This may not be acknowledged because, while qualitative analysis has a degree of validity, it also has a degree of subjectivity Because of this, theobtained data could not be approved If equivalent qualitative analysis work fails to yield comparable findings, the data obtained initially could bediscarded

Quantitative data

Quantitative data is classified as data values in the form of counts or numbers, with each collection of data having a distinct value Data is quantifiableknowledge that can be used for quantitative equations and statistical analyses in order to make sound assumptions based on these mathematicalderivations Quantitative evidence were used to address questions like "How many?" and "How many times?" Using statistical methods, these data can

be conveniently checked or analyzed

Advantages of Quantitative research:

• Can be validated and verified: Quantitative analysis necessitates meticulous laboratory design, and the experiments and findingsshould be reproducible by all As a result, the data you gather would be more accurate and non-controversial

• Simple Analysis: When you gather quantitative results, the form of outcome will inform you which statistical measure to use As a result, reading thedata and presenting these findings is easy, but subject to errors and subjectivity

• Since many people do not appreciate the math involved, research involving abstract numbers and data processing is regarded as important andspectacular Computer simulation, security selection, portfolio assessment, and other data-driven business assessments are also examples ofquantitative testing The connection between credibility and value in quantitative research could well apply to your small business

Disadvantages of Quantitative research:

• Focusing on the numbers incorrectly: Quantitative analysis can be constrained in its ability to identify precise mathematical associations, causingresearchers to overlook larger trends and relationships If you just look at the numbers, you can miss out on surprising or extensive details that mighthelp your company

• Difficulty in establishing research models: When doing quantitative research, theories must be carefully formulated and models for data collection andinterpretation must be developed Any configuration bug, researcher bias, or implementation error will render the whole set of results null

• Many people believe that because quantitative analysis is dependent on data, it is more reliable or empirical than analytical and qualitative research.Both findings, however, have the potential to be arbitrary and deceptive

The differences of Quanlitative and Qualitative:

The below are some of the features of quantitative data: It has a standardized order ratio, uses numerical values for numerical properties, and can bevisualized using scatter plots and dot plots Qualitative data, on the other hand, can use numerical values but no numerical properties, no uniformorder scale, and is visualized using bar and pie charts Furthermore, when gathering qualitative evidence, researchers use techniques such as polls,interviews, focus groups, and conclusions In most cases, qualitative data is gathered by surveys and interviews For example, when measuring theaverage height of a class of students, you can query the height of the students instead of measuring height again

Measures of Central Tendency:

Mean, median, and mode are all useful indicators of central tendency; but, depending on the circumstances, certain measures ofcentral tendency are more appropriate to use than others In the following pages, we will look at the average, mode, and median, aswell as how to measure them and when to use them

The Mean (or mean) is the most well-known and widely used measure of central inclination While it is most widely used for continuous data (for data

types, see our variable type guide), it may also be used for discrete and continuous data The average is calculated by dividing the sum of all values inthe data set by the number of values in the data set The average value is calculated by taking into account all values The average value can be affected

by small or high values Statistics

Products Customers

The median : The number of centers in the data set is represented by the median Find the middle number by organizing the data points from smallest

to highest This is the average Where two numbers are in the centre, the median is the sum of these two numbers The median Very high or very smallvalues have little impact on the median

The Mode is the most commonly occurring number in the data set Count the number of occurrences of each number in the data collection The

number with the highest count is the mode It makes no difference if there are several modes There is no trend if all figures occur the same number oftimes Moderate If the data set is not a number (for example, the color of a car in a parking lot), then this mode is the only option

Measures of Variability Standard Devition :

The standard deviation could be a measurement that employments the square root of the fluctuation to see the remove between a set of numbers andthe normal The change calculation employments square since it is bigger than the weighted exception than the information closer to the cruel Thiscalculation can too anticipate above-average differences from compensating for below-average contrasts, which is able result in zero fluctuation Bydeciding the alter within the cruel between each information point, the standard deviation is calculated as the square root of the change On the offchance that these focuses are more distant from the cruel, the contrast inside this date will be more prominent; in the event that they are closer to thecruel, the distinction will be littler Hence, the more the number bunches are dispersed, the more prominent the standard deviation

It necessitates the use of the average value as an indicator of core inclination Since ordinal and nominal data do not have an average value, it can only

be used for interval data This is a flaw since the standard deviation does not include all forms of data in use, so its use is restricted Statistics

Customers Products

Mean

6.25 6.19 Std Deviation 3.089 3.408

Variance

The variance is the mean of the mean's squared deviations To measure the variance, first compute the difference between each point and the mean;then grid the result and average it The variance is no longer measured in the same unit as the original numbers Finding the source of the differenceinvolves restoring the standard deviation to the initial measuring unit, making it easy to understand

Advantages:

Variance analysis is a critical control technique since it will call attention to situations where real activity differs from scheduled operations Anotherbenefit of variance analysis is that it will help you find situations where assets are not being used correctly and where changes are needed As a result,difference analysis will boost business management productivity Variance analysis may also be used to ascertain the extent of cost overruns andwhether existing standard rates are appropriate

Disadvantages:

The biggest drawback of variance is that it takes a long time to verify the effect of variance, which means that corrective steps are deferred Monitoringtools can trigger a significant lag period, delaying the implementation of control steps Sensitivity is another performance metric that decides whenvariations in output can be due to changes in input If the actual findings vary from those predicted, a sensitivity analysis may be performed to assessthe effect of the actual results Statistics

Range

Range in statistics refers to the distribution of data from the lowest to the highest value in the distribution It is a common measure

of variation Tests of uncertainty, in addition to measures of core inclination, include descriptive statistics to summarize data sets The spectrum iscomputed by subtracting the minimum and maximum values A wide range indicates high variability, while a narrow range indicates low variability indistribution

Advantages:

The advantage of using this range as a calculation of dispersion is that it is fast and simple to compute As previously mentioned, the range isdetermined by subtracting the minimum value from the maximum value in the dataset This is advantageous because it will speed up data processing,allowing psychologists and analysts to reach conclusions on their findings more quickly It also means that researchers will be able to devote moreeffort to describing and drawing conclusions from evidence rather than calculations and interpretation

Disadvantages:

Extreme scores have a big impact on this range For eg, assume the final score for group A is 134 rather than 40, which raises the range for group A to

100, giving the impression that the scores for group A are still dispersed This is a flaw since it can be claimed that the spectrum does not necessarilyreflect a dataset's dissemination

Inferential statistics illustrating the differences between population and sample based on different sampling techniques and methods

Write the comparison between population and sample:

BASIS FOR

Meaning Population refers t o the collection of all

elements possessing common characteristics, that comprises universe

Sample means a subgroup of the members of population chosen for participation in the study.

Includes Each and every unit of the group Only a handful of units of

population

Data collection Complete enumeration or census Sample survey or sampling

Focus on Identifying the characteristics Making inferences about

population

One sample T-test

The sample T-test is a statistical hypothesis test that is used to assess if the mean of an uncertain population differs from a given value The test should

be used to collect continuous data Your data should come from a random survey of a healthy population Normality can not be measured if the samplesize is limited You will need to focus on your knowledge of the results If you can't reliably presume standard, you can run a nonparametric test thatdoesn't

One-Sample Test

Test Value = 0

t

df Sig (2tailed) Difference Mean

95% Confidence Interval ofthe Difference

Two samples T-test

A two-sample t-test (also known as an independent-sample t-test) is a tool for determining if the uncertain means of two populations are identical.When the data values are independent, randomly drawn from two normal samples, and the variances of two independent groups are similar, youshould use this test Use a variety of comparative techniques One such approach is analysis of variance (ANOVA) The Tukey-Kramer test for allpairwise disparities, measurement of means (ANOM) (used to equate group means with overall mean), and Dunnett's test (used to compare groupmeans) are among the other comparative tools And keep an eye on the overall value

Measuring the association between two variables (from the dataset) by using SPSS software or Excel for raw data analysis Correlations :

Correlation is a mathematical indicator that determines how closely two variables are connected linearly (meaning that they change together at aconstant rate) It is a commonly used method for explaining basic relationships without specifying causality

Total

20264496524433784.000 150

a Dependent Variable: Total Price

b Predictors: (Constant), Total, Customers, Products

Coefficients a

Model

UnstandardizedCoefficients

Standardize

d Coefficients

t Sig

Collinearity StatisticsToleranc

2 Applying a range of statistical methods used in business planning for quality, inventory and capacity management

Normal distribution

The regular distribution (also known as the Gaussian distribution) is the most often used distribution function for randomly generated independentvariables The bell curve is very popular in statistical reports, from inquiry and analysis to quality management and resource distribution For eg, IQ,blood pressure, and a person's height