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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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Table of Contents
- I. INTRODUCTION
- II. MAIN CONTENT
- A. Analysing and evaluating qualitative and quantitative raw business data
- B. Descriptive statistics
- C. Inferential Statistics
- The differences between population and sample........................................
- The association between two variables
- quality, inventory, and capacity management D. Applying a range of statistical methods used in business planning for
- E. Communicate findings using appropriate charts/tables..............................
- F. Recommendation and judgement
- III. CONCLUSION
- IV. REFERENCES
- Figure 1 The example of quanlitiative data (Author, 2022) Table of Figure
- Figure 2 The example of Quantitative data (Author, 2022)
- Figure 3 Mean
- Figure 4 Example for mean
- Figure 5 Histogram revenue product's samsung of 2014-
- Figure 6 The Product of Samsung in
- Figure 7 Statics Category Mode
- Figure 8 Category Mode Details
- Figure 9 Bar chart mode of category
- Figure 10 Example for Median odd & even number
- Figure 11 Statics median unit cost
- Figure 12 Median detail
- Figure 13 Unit cost range
- Figure 14 Varience
- Figure 15 Variance of unit cost
- Figure 16 Get Variance from excel
- Figure 17 Standard deviation
- Figure 18 Example for standard deviation
- Figure 19 Caculate standard deviation by excel............................................................................
- Figure 20 Population vs sample staticstics
- Figure 21 Example of population and sample
- Figure 22 Example to Estimation
- Figure 23 Hypothesis testing
- Figure 24 Example Hypothesis
- Figure 25 One-sample and statics test
- Figure 26 Some formular of two situations
- Figure 27 Comparison Estimate and hypothesis
- Figure 28 Comparison Estimate and hypothesis
- Figure 29 SPSS of independent Samples Test
- Figure 30 Assuming from SPSS....................................................................................................
- Figure 31 Paired Sample Correlattions
- Figure 32 Paired Sample Test
- Figure 33 Correlation
- Figure 34 Table Correlation
- Figure 35 Correlations
- Figure 36 Anova regression model
- Figure 37 Additional data
- Figure 38 Normal distribution
- Figure 39 Normal distributions with diffrent means
- Figure 40 Normal distributions with diffent standard deviations
- Figure 41 Normal distributions unit cost my excel
- Figure 42 Example Poisson distribution
- Figure 43 Poisson Distribute Graph
- Figure 44 fequecy category
- Figure 45 example of pie chart
- Figure 46 Example bar chart
- Figure 47 Example historygram
- Figure 48 Example for scatter diagram
- Figure 49 Example for box plot
I. INTRODUCTION
In human society, there are many miracles that happen randomly and miraculously, so the children have constantly researched and discovered to enrich their knowledge. For any hypothesis testing, researchers must conduct research projects. In projects, they have to use many different types of methods to analyze data from a specific level. In the previous project, the author explained most of the concepts related to receiving analytical data to confirm a hypothesis. Several analytical methods to obtain the overview data are also proposed in the last method. In this report, the author gives advice on professional statistical methods to analyze more specifically the significant changes and the interplay between factors. From the results of this project, they could see that some problem occurred in their business data system that they had to improve immediately. Samsung is dedicated to abiding by local laws and regulations and holds all employees to a strict global code of conduct. The corporation thinks that ethical management may be used to gain the trust of a wide range of audiences as well as respond to the swift changes in the global business environment. including clients, investors, staff, business partners, and the neighborhood. Samsung continues to train staff members, run a monitoring system, and engage in open and equitable corporate management in an effort to become one of the most moral businesses in the world. This is another factor, in my opinion, that makes Samsung unique. In order to better comprehend Samsung's business performance from 2014 to 2019, the author will undertake data and analysis of Samsung's sales in his capacity as an analytical researcher. Furthermore, the findings of this study will serve as the foundation for the company's future creation of business plans to improve the effectiveness of marketing operations and capture profitable sales from the devices. electrical apparatus. The information provided includes some fundamental details about the Samsung company's product categories. Based on that, some method analysis can be applied to statistics in the form of some practical metrics (profit, revenue, launched date, etc.). In order to improve the proposal in the future, Intel's existing environment will be assessed through these reports, including performance levels, trends, and more. To support and rectify the data story, some adjustments and applications with SPSS or Microsoft Excel are employed. The study used accepted statistical techniques to analyze the data in line with the theory. This report's goal is to successfully carry out the data analyst's translation of unreadable data into readable information. especially with data on sales and revenue for the Samsung Group.
II. MAIN CONTENT
A. Analysing and evaluating qualitative and quantitative raw business data Data analysis comes in two flavors: qualitative data and quantitative data. Particularly, qualitative research gathers the qualitative data, and quantitative research gathers the quantitative data. These sorts of data are analyzed using both qualitative and quantitative methods, depending on the corresponding type. Both of them are fundamental techniques for gathering and analyzing data in research. The two forms of analysis differ in the following ways. Criteria Qualitative Analysis Quantitative Analysis Definition The objective of qualitative research is to gather in-depth knowledge about the topic of the survey, investigation, or other type of research (hence collectively referred to as the "subject of study"). Simply put, it can be described as a research methodology that focuses more on the diversity, texture, and feeling of data representation since identifying and explaining phenomena is based on the shared perspective and expertise of researchers through the evolution of phenomena. for the purpose of analysis or comprehensive evaluation. Quantitative research is the redesign of quantitative observations about variables, methods of measurement, examples of analysis, and the interpretation of relationships between variables using quantitative relationships. The inference and interpretation of phenomena is based on the collection and analysis of previous data. Purpose Understand and interpret social interactions Test hypotheses, check the cause and effect. Develop predictions for future Sampling Small, had a specific choice purpose Big, random selection Focus Detailed descriptive data Detailed number data Analysis Inductive (by the researcher) Deductive (by the statistical)
pressure drug, quantitative research is helpful. (Dowd, 2018) Disadvantages For example, qualitative studies often take longer to complete due to the rigors of collecting and analyzing field notes, recording interviews, identifying topics, and researching topics. photo. Studies cannot be easily repeated or generalizable to the general population. Conscious or unconscious bias can influence a researcher's conclusions. Lacking rigorous scientific controls and numerical data, qualitative findings may be perceived by some researchers as anecdotal information. (Dowd, 2018) Measurement of phenomena like human behavior in real settings is more challenging, even though quantitative research approaches perform well in the laboratory under strictly regulated conditions. Survey instruments are susceptible to flaws including inaccurate measurement and poor sampling practices. Another drawback is that quantitative research relies on numbers, but some subjects are too complex to express quantitatively. For instance, it would be challenging to design a successful survey that included closed-ended questions about how individuals fall in love. (Dowd, 2018) Table 1 Qualitative and Quantitative raw business data The advantages of both quantitative and qualitative methods are combined in a mixed- methods approach, which ignores their respective shortcomings. An administrator who wants to improve connections with parents of students, for instance, might carry out a mixed-methods study. Parents will first get a survey from the school's principal asking them to rate their happiness with the school and the caliber of instruction. Principals will suggest areas for further investigation after examining the data, such as parent complaints regarding the school's handling of bullying events. The next step is to set up focus groups to get qualitative data from parents in order to better understand their issues.
Figure 1 The example of quanlitiative data (Author, 2022) Figure 2 The example of Quantitative data (Author, 2022) B. Descriptive statistics Measures of central tendency: is a single value that represents the central point of the data set. This value can also be thought of as the “central position” of the data set. Each of these measurements finds the central position of the data set using different methods. Depending on the type of data you are analyzing, one of these three measures may be better to use than the other two. (Zach, 2018)
1. Mean: The mean is the most widely used indicator of central tendency. Adding together all the individual values and dividing by the total number of values yields the dataset's mean. All 1. Consumer Electronics 2. IT Communications 3. Mobile Communications 4. Semicon 5. Display Panel
2. Mode: Mode is the value that occurs most often in the data set. The data set may have no mode (if no values repeat), single mode, or multiple modes. For example, with the data of a variable "Category" from the attached excel file, the author used SPSS software to calculate the mode value. There are results of the mode value of the variable "Category" from SPSS. My table is Figure 6 The Product of Samsung in 2019 ➢ 1. Consumer Electronics ➢ 2. IT Communications ➢ 3. Mobile Communications ➢ 4. Semicon ➢ 5. Device Panel Figure 7 Statics Category Mode
Figure 8 Category Mode Details From the above two tables, the finding presented that the mode value of the variable "Category" is Semicon. Shown in the bar chart below. Figure 9 Bar chart mode of category
3. Median : The median is the middle number in a data set of sorted numbers. To determine the median in a series of numbers, the numbers must first be sorted by value from lowest to highest or highest to lowest. The median may be used to determine the approximate mean or the mean, but the median should not be confused with the actual mean. For example, with the data of a variable "Unit cost" from the attached excel file, the author used SPSS software to calculate the mode value. There is a result of the mode value of the variable "Unit cost" from SPSS to calculate the median.
Figure 12 Median detail Measures of Variability: A measure of variability is a summary statistic that represents the amount of dispersion in a data set. How are the values spread? While the central tendency measure describes the typical value, the measure of variability determines how far the data points tend to fall off the center. We talk about the change in the context of the distribution of values. The low dispersion indicates that the data points tend to be tightly grouped around the center. The high dispersion indicates that they tend to fall further apart. (Frost, 2021)
1. Range: That is the easiest way to compute range, which is a measure of variance. the portion of the data set that lies between the highest and lowest values in that data set. Although this range is simple to comprehend, it is highly prone to outliers because it is based simply on the
two most extreme values in the data set. Even if one of those numbers is out of the ordinary, a high or low value has an impact on the entire range. Range = Highest – Lowest Example: Considering the list of numbers including 2, 4, 5, 7, 7, 12, 13, 14. In this case: Maximum value = 14 and Minimum value = 2 ➔ Range value = 14 – 2 = The application with the SPSS through some variable: Unit cost Figure 13 Unit cost range From the table above, the results clearly show that for 27 observations, the range value of the variable "Unit cost" is 249.16, this means the distance or range of variation between the minimum value and The maximum of this variable is 249. 16
2. Variance: Variance isthe average squared difference of the valuesfrom the mean. The variance includesall values in the calculation by comparing each value to the mean. To calculate this statistic, first, calculate a set of squared differences between the data points and the mean, sum them, and then divide bythe number of observations. Hence, it’s the average squared difference. The mean of the square of deviations from mean is also called variance. Figure 14 Varience Example: : Considering the following sample dataset including 8, 9, 7, 6, 5, to calculate the variance, the author had to use a sample variance formular.
Example : Considering the following sample dataset including 8, 9, 7, 6, 5, to calculate the standard deviation, the author had to use a sample standard deviation formula. Figure 18 Example for standard deviation Based on the below table, the standard deviation value of “Unit Cost” variable is 80.96332904. Apart from that, if a value of the dataset has a far distance from the mean, it is considered that the more spread out the data is, the higher its standard deviation is Figure 19 Caculate standard deviation by excel C. Inferential Statistics ➢ The differences between population and sample In statistics as well as in quantitative methodology, the set of data is collected and selected from a statistical population with the help of some defined procedures. There are two different types of data sets namely, population and sample. So basically, when we calculate the mean deviation, variance and standard deviation, it is necessary for us to know if we are referring to the entire population or to only sample data. Figure 20 Population vs sample staticstics
1. Population: "In simple terms, population denotes the aggregate of all elements under study having one or more common characteristics, for example, all persons living in India constitute the population," writes Surbhi S in her explanation of the term. The term "population" does not just refer to people; it can also refer to things like objects, events, structures, etc. It can be of any size, and the number of individuals that make up a population is known as population size; for example, if India has 100 million citizens, then N is the population size of India. The various population types are discussed as follows. Finite Population: The population is considered to be finite when the number of its components is fixed, making it possible to count it in its whole. Infinite Population: A population is said to have an unlimited number of units when this makes counting them impossible, making it impossible to observe all of the universe's constituent parts. Existent Population: The population that is made up of things that actually exist in the world is known as the existent population. 2. Hypothetical Population: The population that only exists in the mind is referred to as the hypothetical population. (S, 2017). A sample is a population subset that reflects the population of interest in the data. To avoid a population constraints, occasionally collecting data from subsets of the population, then consider the overall specification. Therefore, the sample is an unbiased subset of the population that best reflect the entire dataset. Results obtained by the groups involved in the study. Can be extended to generalize to the entire population (Ravikiran A S, 2022). Generally speaking, the interrelationship between population and data exists to support each other in statistics. The difference between them and the size of the context from which each other can be inferred. Pass if the population is too large, the population limit of the sample can be collected. Print Also, data from populations are more prestigious and reliable. More specifically, the table below will draw the difference. Table 2 The diffrence population and sample
