Student’s Verifier’s Contents I.The statistical methods used in business planning for quality, inventory, and capacity management .1 Measuring the variability in business processes or quality management.The Coefficient of Variation. Probability distributions and application to business operations and processes. Discrete Probability Distribution .Continuous probability distributions .Inferential statistics illustrating the differences between population and sample based on different sampling techniques and methods .One sample T-test: Estimation and Hypotheses testing .Two sample T-test: Estimation and Hypotheses testing .Measuring the association between two variables (from the dataset) by regression technique.Create/draw different types of visual representations for variables in the dataset. Explain the advantages and disadvantages of each visual representation.
34 Figure Figure 1: Mean, mode, median, range and standard deviation of ROE. 6 Figure 2: The correlation between the variables. 9 Figure 3: Categorical ROE. 10 Figure 4: Bell-shaped distribution.
12 Figure 5: Example of Empirical rules. 14 Figure 6: Linear regression model of predicted variable (ROA) is affected by variables SAGR, FIXED, LEV, DAR27 Figure 7: Simple tables of my data set. 29 Figure 8: Frequency of ROE. 30 Figure 9: Histogram of ROE.
31 Figure 10: Bar chart of Return On Equity. 32 Figure 11: Scatter plot shows the correlation between SIZE and DAR. Background and the reasons why you choose the topic Title: Analysis of factors affecting telecommunications enterprises in Vietnam. Objectives, scope, and meaning of the study Objective: The objective of this study is to find out the factors affecting the business performance of enterprises in the telecommunications sector in Vietnam and to expand the scale of business activities with efficient use of capital.
The study in scope: All telecommunications companies operating in Vietnam listed on HNX, HOSE, and Upcom in the period 2014-2021. Meaning: The meaning of this research paper is to provide recommendations for telecommunications businesses so that they can offer methods to help businesses improve and improve business performance or capital efficiency in the following business periods to help the business operate better. Methodology (changed) Using quantitative analysis, financial ratio comparison, and descriptive research, we looked into the relationship between financial ratios and the financial performance of Vietnamese telecom firms. Vietnam as determined by the regression formula.
The application and interpretation of statistics is known as descriptive analysis. Unlike inferential or inductive statistics, which aim to make conclusions about the population the sample is supposed to represent, descriptive statistics focus on summarising a sample. This usually means that descriptive statistics are non-parametric statistics, in contrast to inferential statistics, which are based on probability theory. Even in cases where inferential statistics are utilized to derive meaningful inferences from data analysis, descriptive statistics are typically presented.
Structure of the report I will first employ a variety of statistical techniques used in business planning for capacity, inventory, and quality management in my ASME article. After that, I'll measure the variability in inferential statistics, q y g y , y , probability distributions and their application to business operations and procedures, and quality management. Subsequently, I use inferential statistics to show how the population and sample differ based on various sampling strategies and tactics. I'll be working on one sample T-test and two sample T-tests in it, using the regression technique to measure the relationship between two variables (from the dataset).
provide an explanation of regression and its use in the dataset (Stata), and construct or draw various visual representations for the variables in the dataset, such as frequency tables, basic tables, pie charts, and histograms. I'll be studying those sections in my ASM. The statistical methods used in business planning for quality, inventory, and capacity management 2.Measuring the variability in business processes or quality management. According to Bhandari, A(2023), variability describes how far apart data points lie from each other and from the center of a distribution.
Along with measures of central tendency, measures of variability give you descriptive statistics that summarize your data. Variability is also referred to as spread, scatter or dispersion. It is most commonly measured with the following: Range: the difference between the highest and lowest values Standard deviation: average distance from the mean Variance: average of squared distances from the mean Coefficient of Variation: a statistical measure of the dispersion of data points around the mean While the central tendency, or average, tells where most of the points lie, variability summarizes how far apart they are. This is important because the amount of variability determines how well can generalize results from the sample to the population.
Low variability is ideal because it means that can better predict information about the population based on sample data. High variability means that the values are less consistent, so it’s harder to make predictions. Data sets can have the same central tendency but different levels of variability or vice versa. If know only the central tendency or the variability, you can’t say anything about the other aspect.
Both of them together give a complete picture of your dataset. The Range According to Frost, A(2017), the range is the most straightforward measure of variability to calculate and the simplest to understand. The range of a dataset is the difference between the largest and smallest values in that dataset. While the range is easy to understand, it is based on only the two most extreme values in the dataset, which makes it very susceptible to outliers.
If one of those numbers is unusually high or low, it affects the entire range even if it is atypical. Formularies: Range = Highest value – The lowest value Figure 1: Mean, mode, median, range and standard deviation of ROE For example: In the dataset of Figure 1 above, the dataset of ROE Variable has a range of -8.5592108, which means ROE has Min= -8.5592108 or that means the total statistics of 368 observations of ROE show that the enterprise with the lowest ROE index is approximately -868.78% and enterprises have the highest ROE of index is approximately 55. So the range is 55. Variance Although the range and interquartile range measure the spread of data, both measures take into account only two of the data values.
We need a measure that would average the total (Σ) distan ce between each of the data values and the mean. But for all data sets, this sum will always equal zero because the mean is the center of the data. If the data value is less than the mean, the difference between the data value and the mean would be negative (and distance is not negative). If each of these differences is squared, then each observation (both above and below the mean) contributes to the sum of the squared terms.
The average of the sum of squared terms is called the variance. A measure of variability using all the data is called variance. It is based on the discrepancy between the mean (𝑥 for a sample, m for a population) and each observation's value (xi). When comparing the variability of two or more variables, the variance is helpful.
The average of the squared variances between each data value and the mean is the variance (Frost, 2023). Formular: With respect to variance, the population variance, σ2 , is the sum of the squared differences between each observation and the population mean divided by the population size, N 𝟐 : Population variance : Ith observation in the population : Population mean : Number of observations in population The sample variance, s2, is the sum of the squared differences between each observation and the sample mean divided by the sample size, n, minus 1: 𝟐 : Sample variance : Ith observation in the sample : Sample mean : Number of observations in a sample Example: I’ll work through an example practice in Stata for a sample on my dataset with 368 observations in Figure 1. The total statistics of 368 observations of ROE show that the Variance is 0. Standard Deviation The standard deviation is the standard or typical difference between each data point and the mean.
When the values in a dataset are grouped closer together, you have a smaller standard deviation. On the other hand, when the values are spread out more, the standard deviation is larger because the standard distance is greater. Conveniently, the standard deviation uses the original units of the data, which makes interpretation easier. Consequently, the standard deviation is the most widely used measure of variability.
The standard deviation is just the square root of the variance. Recall that the variance is in squared units. Hence, the square root returns the value to the natural units. The symbol for the standard deviation as a population parameter is σ while s represents it as a sample estimate.
To calculate the standard deviation, calculate the variance as shown above, and then take the square root of it (Frost, 2023). Formularies: The sample standard deviation 𝒔: Sample standard deviation The population standard deviation 𝝈 = √𝝈𝟐 𝝈: Population standard deviation Example: In the variance section, the total statistics of 368 observations of ROE show that the Variance is 0.2217484 or 22,17%, from that the standard deviation is the square root of the variance equal to 0. This is clearly shown in Figure 1 above. Standard Deviation is 47.09% shows an unequal distribution in the observed samples.
That is, businesses in the telecommunications sector are having a disparity in operating ability and profitability.The Coefficient of Variation The coefficient of variation (relative standard deviation) is a statistical measure of the dispersion of data points around the mean. The metric is commonly used to compare the data dispersion between distinct series of data. Unlike the standard deviation that must always be considered in the context of the mean of the data, the coefficient of variation provides a relatively simple and quick tool to compare different data series. In finance, the coefficient of variation is important in investment selection.
From a financial perspective, the financial metric represents the risk-to-reward ratio where the volatility shows the risk of an investment and the mean indicates the reward of an investment. By determining the coefficient of variation of different securities, an investor identifies the risk-to-reward ratio of each security and develops an investment decision. Generally, an investor seeks a security with a lower coefficient (of variation) because it provides the most optimal risk-to-reward ratio with low volatility but high returns. However, the low coefficient is not favorable when the average expected return is below zero ( Sebastian Taylor, 2023).
Formular of coeficient variance: The population coefficient of variation is: σ – the standard deviation μ – the mean The sample coefficient of variation is: Example: Figure 2: The correlation between the variables Getting significant at 5% means a confidence level of 95%. Comparing the correlation relationship between SIZE and DAR variables, we see that these two variables have a correlation value with a significant value is 0.05) with a correlation coefficient of 0. SIZE increases by 1, and DAR will increase to 0. Larger enterprises tend to expand, loosen trade credit policies, are less able to manage receivables, and slower the recovery rate of receivables.
Probability distributions and application to business operations and processes Definition: Probability is a quantitative representation of the likelihood of an event happening, expressed on a scale from 0 to 1. A probability close to 0 suggests a low likelihood, signifying that the event is improbable, while a probability near 1 indicates a high likelihood, implying that the event is highly probable or almost certain to occur (Turney, 2023). For example: Coin Toss: Experiment: Tossing a fair coin. Possible Outcomes: Head (H) or Tail (T).
Probability Assignment: Each outcome has a probability of 1/2. There are two types of probability distribution which are used for different purposes and various types of data generation processes. Discrete Probability Distribution Continuous Probability Distribution 2. Discrete Probability Distribution Discrete Probability distributions are applied for discrete random variables.