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 HND Diploma in Business Unit number and title Unit 31: Statistics for management Submission date Date received (1st Submission) Re-submission date Date received (2nd Submission) Student Name Student ID Class No Assessor Name Student declaration I certify that the assignment submission is entirely my own work and I fully understand the consequences of plagiarism I understand that making a false declaration is a form of malpractice Student Signature Grading grid P3 P4 P5 M2 M3 M4 D1 D2 D3 Description of activity undertaken Assessment & Grading criteria How the activity meets the requirements of the criteria Student Signature Date: Assessor Signature Date: Assessor name: Summative Feedbacks Grade: Assessor Signature: Resubmission Feedbacks Date: Internal Verifier’s Comments: Signature & Date: 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 have earned 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, we summarize it in Excel and store it in our database on a daily basis II Main Content: 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 nonnumerical 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 or phenomena Advantages of Qualitative research: • Understanding behaviors is possible when consumption habits shift often Companies can be perplexed if this occurs unexpectedly The mechanism created by qualitative analysis has the power to explain why behaviors can be altered You may also include a detailed description, which can cause the organization to react to shifts of perception Since qualitative studies will help us to truly consider behaviors It is now easier to develop relationships with customers • This is a content generator: even for seasoned advertisers, discovering new ways to show old content can be challenging Qualitative analysis approaches 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, everybody will 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 Since qualitative analysis evidence is dependent on perspective, it is predictive of nature It will gather the trademarks that distinguish an individual and use them to recognise individuals who have common tastes or ways of thinking, allowing the company to produce more useful content, products, and services 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 not necessitate a high level of trust in the data collection process Researchers who are unable to see the required data during observation will miss this 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 go through all of this extra information Each data point is measured subjectively, so its validity is often debatable Other analysis types have stringent criteria 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, the obtained data could not be approved If equivalent qualitative analysis work fails to yield comparable findings, the data obtained initially could be discarded 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 quantifiable knowledge that can be used for quantitative equations and statistical analyses in order to make sound assumptions based on these mathematical derivations 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 findings should 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 the data and presenting these findings is easy, but subject to errors and subjectivity • Since many people not appreciate the math involved, research involving abstract numbers and data processing is regarded as important and spectacular Computer simulation, security selection, portfolio assessment, and other data-driven business assessments are also examples of quantitative 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, causing researchers to overlook larger trends and relationships If you just look at the numbers, you can miss out on surprising or extensive details that might help your company • Difficulty in establishing research models: When doing quantitative research, theories must be carefully formulated and models for data collection and interpretation 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 be visualized using scatter plots and dot plots Qualitative data, on the other hand, can use numerical values but no numerical properties, no uniform order 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 the average 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 of central tendency are more appropriate to use than others In the following pages, we will look at the average, mode, and median, as well 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 in the 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 N Valid 161 161 0 6.19 6.25 Missing Mean 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 small values have little impact on the median Statistics Customers Products N Valid Missing Median 161 161 0 6.00 6.00 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 of times 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 Statistics Customers Products N Valid Missing Mode 161 161 0 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 and the normal The change calculation employments square since it is bigger than the weighted exception than the information closer to the cruel This calculation can too anticipate above-average differences from compensating for below-average contrasts, which is able result in zero fluctuation By deciding the alter within the cruel between each information point, the standard deviation is calculated as the square root of the change On the off chance 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 the cruel, the distinction will be littler Hence, the more the number bunches are dispersed, the more prominent the standard deviation Advantage: This is the most precise measurement of dispersion The standard deviation, for example, will take into account all possible scores in the data set, while the distribution will not This is a benefit because it ensures that standard deviation is the most representative way to consider all ratings, even though it can underestimate a number of days Disadvantages: It necessitates the use of the average value as an indicator of core inclination Since ordinal and nominal data 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 N Valid Missing 161 161 0 Most Absolute 126 Extreme Differences Positive 086 Negative -.126 Kolmogorov-Smirnov Z Asymp Sig (2-tailed) a Test distribution is Poisson b Calculated from data 1.542 017 Binomial distribution The chance of success or failure in replicated trials or surveys may be thought of clearly as a binomial distribution A binomial distribution has two potential outcomes (the prefix "bi" means two or two times) For example, completing an assignment may result in one of two outcomes: "Pass" or "Refer." Inference statistics Inferential statistics is a statistical method that can deduce the characteristics of a larger population from a small but representative sample In other words, it allows researchers to make assumptions about a larger group based on a smaller part of the group Inferential statistics is one of two statistical methods used to describe data and to analyze data The purpose of this tool is to provide a way to describe the entire research project by studying a small number of samples For example: A company called Pizza Palace Co is currently conducting market research on customer behavior when eating pizza The company tried to understand the preferences of its customers in order to rethink the menu They hired a marketing consulting company to convene a focus group to study the issue The company gathered 50 people of different ages and genders, all of whom were residents of the neighborhood where the store was located After the focus group was over, the marketing company began to make inferences and statistics on the sample to understand the most sought after taste among the general population The results showed that 75% of the people in the sample liked pepperoni pizza; they also found that 75% of the women in this group liked pizza and pineapple The probability distribution Probability distribution is a statistical function that describes all the possible values and possibilities that a random variable can take in a given range The range will be limited to the smallest and largest possible values, but the exact location of the possible values on the probability distribution depends on many factors These factors include the mean (mean) of the distribution, standard deviation, skewness, and kurtosis For example: The rolling dice have probability of each is 1/6 (from to 6), but the sum of the two dice will form a probability distribution, as shown in the figure below Seven endings are the most common endings (1 + 6, + 1, + 2, + 5, + 4, + 3) On the other hand, and 12 are much less likely (1 + and + 6) Regression Regression analysis is a method of finding trends in data For example, you might guess that there is a link between diet and weight; regression analysis can help you quantify Regression analysis will provide you with graphical equations so that you can make predictions on the data For example, if you have been losing weight for a few years, then if you continue to lose weight at the same rate, it can predict your weight in ten years It will also provide you with a lot of statistical information (including p-values and correlation coefficients) to tell you the accuracy of the model Most basic statistics courses cover very basic techniques such as point cloud drawing and linear regression However, you may come across more advanced techniques, such as multiple regression You are a social researcher interested in the relationship between income and happiness You surveyed 500 people with incomes between $ 15,000 and $ 75,000 and asked them to rank happiness from to 10 Your independent variable (income) and your dependent variable (happiness) are both quantitative, you So you can a regression analysis to see if there is a linear relationship between them Using appropriate charts/tables communicate findings for a number of given variables Frequency tables The frequency distribution is expressed in graph or table form and is used to display the number of observations in a given interval The size of the interval depends on the data to be analyzed and the goals of the analyst The intervals must be mutually exclusive and exhaustive Frequency distribution is usually used in statistical environments Generally, the frequency distribution can be associated with a graphical representation of the normal distribution For example, if four students have a math score of 80, a score of 80 is called a frequency of The frequency of the data value is usually denoted by f Simple tables The statistical table can be thought of as representing the subject and the predicate The subject is the phenomenon or group of phenomena treated in the table Predicates are composed of characteristics that describe the subject The statistical table consists of horizontal rows and vertical columns Usually enter the subject of the table in the row and enter the characteristics that make up the predicate in the column The intersection of rows and columns forms a unit in which numeric data is organized The meaning of each number is indicated by the header of the corresponding row and column Pie charts A pie chart (or pie chart) is a pie chart, which is divided into multiple slices to explain or illustrate the ratio of numbers In a pie chart, the center angle, area, and arc length of each slice are directly proportional to the number or percentage it represents The pie graph under demon the percentage of blood types in a group of 200 individuals How many people in this group have blood type AB? The solution for this question is 19%×200 = 19×200/100 = 38 people Histograms Histograms are another way to show the distribution of quantitative variables Histograms are particularly useful for large data sets The histogram divides the values of the variables into intervals of equal size We can see the number of individuals in each interval Frequency curves The frequency curve is obtained by connecting the points of the frequency polygon with a smooth curve drawn freehand To draw a frequency curve instead of using a histogram, we need to plot the frequency of a category relative to its category markers and connect these points with line segments Normal curve The bell curve is a common type of variable distribution, also known as a normal distribution The term "bell curve" comes from the fact that the graph used to describe the normal distribution consists of a symmetrical bell curve For example, if 100 test scores are collected and used for a normal conversion distribution, 68% of those test scores should be in the above or below average type the average should include 95% of the 100 test scores collected Scatter plot A scatter plot (also called a scatter chart) is similar to a line chart Line graphs use lines on the X-Y axis to draw continuous functions, while scatter graphs use points to represent individual data In statistics, these graphs are very useful to see if two variables are related to each other For example, scatter plots can show linear relationships Advantages Disadvantages Frequency table The frequency table can It is difficult to quickly find anomalies in understand the complex the data set, and even data set displayed on the find important life cycle frequency table patterns (such as unfinished learning Big data should be results displayed on the divided into time interval recursive table) to categories for easy quickly understand the viewing categories This allows different analysts to visualize the relative abundance of each type of information It seems that the number of information sets contained in the target information is not often mentioned as a recursive table Unless displayed on the histogram, the skewness and flatness of the data in the frequency table may not be obvious Simple table Columns can be Only a few columns can automatically resized so be compressed before that the table always fills the width of the table different screen widths causes horizontal Cell borders and background colors can improve readability Simple tables are not scrolling on a smaller screen Compared to the same content displayed compatible with without a table, the page nondigital open text size has increased content The page rendering speed becomes slow Pie chart It visualizes data as a part If you use too much data, of the whole, and can be the effect of the pie an effective chart will be reduced communication tool even for unfamiliar audiences If there is too much data, even if you add data tags and numbers, the data Using this table can eliminate the need for readers to check or measure basic numbers itself will become messy and difficult to read, which may not help by themselves To emphasize the points to be emphasized, you can manipulate the data in the pie chart Making decisions based on visual impact rather than data analysis may lead readers to draw wrong conclusions Histogram Histogram makes it easier The exact value cannot be for us to identify read because the data is different data aggregated This helps to visualize the distribution of data Can easily compare to normal curves It is more difficult to compare two sets of data Used only for continuous data Frequency Curve The frequency curve has The main disadvantage of the greatest advantage the curve is the inability of showing the skewness to show the exact value of the distribution (i.e of the distribution It is positive tilt, negative also difficult to compare skewness, and different sets of data symmetry) The downside of The advantage of cumulative frequency is that it can help us observe and find the number of data observations below the range for a specific data cumulative frequency is that it is difficult to compare the frequency of each set of data The huge gaps not set It also helps us to observe and understand how the value of a particular data set changes It also allows us to know the total frequency of all things at all times Normal curve Easily compare with histograms The probability distribution can be divided infinitely Strictly stable probability distribution show scatter, making it difficult to compare different sets of information Use a normal distribution starting from negative infinity This may cause some results to be negative Scatter Plot Even if the dependent Calculation errors can lead variable has many to incorrect graphical values, they are easy to representations, which in plot turn lead to incorrect It is easy to understand and understand The maximum value and the minimum value are easy to separate, so it has little effect on the graph data analysis They not always determine the exact correlation level Too many graphs are a big problem when using such graphs, because they can lead to arbitrary values The most effective way of communicating the results of your analysis and variables You should use all the charts and tables listed above and give reasons for your choice In the statistical methods learned In my opinion, the most effective way to convey information and data to others is Histogram Because the user can easily see the datas and compare them Furthermore, it can operate with a large amount of information III Conclusion The application of data analysis and statistical methods is indispensable for the organization and delivery of written materials During the implementation of the application, the discussion and development of each technology will be introduced, and the statistical results can provide information about decision-making and issues that need to be resolved within the organization Based on the resources available to the organization, they can choose to use it to achieve the organization's goals IV References Gaille, B., 2021 25 Advantages and Disadvantages of Qualitative Research [online] BrandonGaille.com Available at: [Accessed 10 May 2021] The Balance Small Business 2021 Here Are the Advantages and Disadvantages of Quantitative Research [online] Available at: [Accessed 10 May 2021] Base, K and set, H., 2021 How to Find the Range of a Data Set | Formula & Examples [online] Scribbr Available at: [Accessed 10 May 2021] Encyclopedia Britannica 2021 normal distribution | Definition, Examples, Graph, & Facts [online] Available at: [Accessed 10 May 2021] Statistics How To 2021 Binomial Distribution: Formula, What it is, and how to use it in simple steps [online] Available at: [Accessed 10 May 2021] My Accounting Course 2021 What is Inferential Statistics? - Definition | Meaning | Example [online] Available at: Definitions [online] Available at: [Accessed 10 May 2021] Statistics How To 2021 Regression Analysis: Step by Step Articles, Videos, Simple [Accessed 10 May 2021] TheFreeDictionary.com 2021 Statistical Table [online] Available at: [Accessed 10 May 2021] ... [Accessed 10 May 20 21] My Accounting Course 20 21 What is Inferential Statistics?... 578 -.0 42 -2. 993 003 986 1.014 014 954 341 986 1.014 985 70. 023 000 1.000 1.000 (Constant) 3446 42. 829 6181 42. 29 Products Customers Total -20 4613. 120 68353.711 75197.096 78785.116 124 70 024 .3 178083.73... TheFreeDictionary.com 20 21 Statistical Table [online] Available at: