btec level 4 hnd diploma in business unit 42 statistics for management

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<b>ASSIGNMENT 2 </b>

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rSummative Feedbacks: rResubmission F<b>eedbacks:</b>

<b>Internal Verifier’s Comments:</b>

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<b>Signature & Date:</b>

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<i><b>1. Introduction ... 4 </b></i>

<i><b>2. Theoretical basis of satisfaction and related variables ... 4 </b></i>

<b>2.1 The definition of basic probability distribution, normal distribution, Poisson distribution, binomial distribution, inference statistics and regression ... 4 </b>

<b><small>2.2.2Service and quality of service ... 12</small></b>

<b><small>2.2.3The relationship between service quality and customer satisfaction ... 12</small></b>

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<b>4.3 Multivariate regression analysis ... 25 </b>

<i><b>5. Conclusion ... 27 REFERENCES ... 27 </b></i>

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<b>1. Introduction </b>

<b>Background and reasons for choosing the topic </b>

In the era of industrial revolution 4.0, e-commerce technology platform is growing strongly, businesses do not necessarily have a strong staff, but instead have the presence and support. of modern technology platform. The model of e-banking is gradually expanded, in which mobile banking is a service that cannot be ignored. But in fact, the process of developing and improving the quality of mobile banking services of MB-Bank branch still has some difficulties and limitations. With the desire to propose to Military Commercial Joint Stock Bank with specific and practical solutions to improve service quality and satisfy customers who are using services at MB-Bank, Da Nang branch. Therefore, I have chosen the topic: "Customer satisfaction about the quality of banking services MB-Bank Da Nang branch".

<b>Objectives, scope of research, methodology and report structure </b>

Inference about mathematical statistics is carried out within the framework of probability theory, which is concerned with the analysis of random phenomena. In business, business analysis and research the plan is basic. As a research analyst for MB-Bank Da Nang branch, I was tasked with understanding the factors affecting customer satisfaction when using services at MB-Bank branch. Da Nang and created a plan to improve customer satisfaction for the bank. To do this, I conducted a survey on the level of satisfaction with the quality of banking services MB-Bank Da Nang branch in Da Nang city. With the scale in Da Nang city, but I only surveyed 50 of them and all are customers who have used the service at this bank. Through this survey, I will take raw data and then apply statistical techniques to analyze the factors that make customers satisfied when using services at MB-Bank Da Nang branch. And the main purpose of this report is to analyze and evaluate customer satisfaction when using services at MB-bank, Da Nang branch with causal statistics. Specifically, the research method used in the study is quantitative. This report is divided into 3 main parts: the first part analyzes the theoretical basis of the survey topic and the statistical methods. Specifically, analysis of research theories and basic probability distributions, normal distributions, poisson distributions, binomial distributions, inferential statistics and regression. The next section analyzes the study designs. The final section reviews and analyzes the results of the study. From there, I can assess customer satisfaction

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when using services at MB-Bank Da Nang branch.

<b>binomial distribution, inference statistics and regression 2.1.1 Probability distribution </b>

A random variable can have any number of alternative values and likelihoods within a particular range, and a probability distribution is a statistical function that captures all of these possibilities. This range will be constrained by the minimum and maximum possible values, but the location of the possible value on the probability distribution will rely on a number of other variables. These elements include the mean (average), standard deviation, skewness, and kurtosis of the distribution.

• Probability Distributions Work

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Several extensively used probability distributions exist, but the normal distribution, also known as the "bell curve," is arguably the most used. Usually, the probability distribution of a phenomenon is determined by the method used to collect the data. The probability density function is the name given to this process.

Cumulative distribution functions (CDFs), which sum up the probability of occurrences cumulatively and always start at zero and end at 100%, can also be made using probability distributions. The probability distribution of a given stock can be calculated by academics, financial analysts, and fund managers to assess the potential expected returns that the stock may provide in the future. The analysis will be prone to sampling error since the stock's history of returns, which can be measured over any time period, is probably only made up of a small portion of the stock's returns. The size of the sample can be increased, significantly lowering this inaccuracy.

(Investopedia)

Example: Probability distributions are idealized frequency distributions

Imagine that an egg farmer wants to know the probability of an egg from her farm being a certain size. The farmer weighs 100 random eggs and describes their frequency distribution using a histogram

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Figure 1: Frequency distribution histogram of 100 random eggs (scribbr)

<b>2.1.2 Normal distribution </b>

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The most typical distribution function for independent, randomly produced data is the normal distribution, commonly known as the Gaussian distribution. Every statistical report uses this well-known bell-shaped curve, from survey analysis and quality control to resource allocation. The mean, or average, which is the maximum of the graph and about which the graph is always symmetric, and the standard deviation, which indicates the degree of dispersion from the mean, are the two parameters that define the graph of the normal distribution. A graph with a small standard deviation (relative to the mean) will be steep, whereas one with a big standard deviation (again relative to the mean) will be flat.

The normal distribution is produced by the normal density function, p(x) = e−(x − μ)2/2σ2/σSquare root of√2π. In this exponential function e is the constant 2.71828…, is the mean, and σ is the standard deviation. The fraction of the region contained inside the function's graph between the supplied values and above the x-axis determines the likelihood that a random variable will fall within any particular range of values. Probabilities may be calculated directly from the corresponding area since the denominator (sometimes referred to as the "normalizing coefficient") makes the total area enclosed by the graph precisely equal to unity, i.e., an area of 0.5 corresponds to a probability of 0.5. Even though these areas can be calculated using calculus, tables for the special case of = 0 and = 1 were created in the 19th century and can be used for any normal distribution once the variables are suitably rescaled by subtracting their mean and dividing by their standard deviation, (x − μ)/σ. The use of such tables has now almost entirely been replaced by calculators.

(Britannica)

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Figure 2: Percent of Population Between 0 and 0.45 (mathsisfun)

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<b>2.1.3 Poisson distribution </b>

The Poisson distribution is a discrete probability distribution. It provides the possibility that an event will occur a specific number of times (k) in a predetermined period or area. The mean number of occurrences, denoted by the letter λ "lambda," is the only parameter of the Poisson distribution. An example of a Poisson distribution with different values of λ is shown in the graph below. The chance of a discrete (i.e., countable) outcome is provided by the Poisson distribution, which is a discrete probability distribution. The discrete result for the Poisson distribution is k, which stands for the frequency of an event.

A Poisson distribution can be used to forecast or explain how many events will take place over a specific period of time or space. The term "events" can refer to anything from the occurrence of a sickness to client purchases to meteor strikes. Any defined period of time or area, such as 10 days or 5 square inches, can be used as the interval. Independently and at random, individual occurrences take place. In other words, the likelihood of one event has no bearing on the likelihood of another. Aware of the average amount of occurrences that take place throughout a specific period of time or space. It is believed that this quantity, known as (lambda), is constant.

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Figure 3: Chart examples of Poisson distributions with different values of λ (Scribbr)

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<b>2.1.4 Binomial distribution </b>

The binomial distribution is the discrete probability distribution used in probability theory and statistics that only allows for Success or Failure as the possible outcomes of an experiment. For instance, if we flip a coin, there are only two conceivable results: heads or tails, and if we take a test, there are only two possible outcomes: pass or fail. A binomial probability distribution is another name for this distribution. There are two parameters n and p used here in a binomial distribution. The variable ‘n’ states the number of times the experiment runs and the variable ‘p’ tells the probability of any one outcome. Suppose a die is thrown randomly 10 times, then the probability of getting 2 for anyone throw is ⅙. When you throw the dice 10 times, you have a binomial distribution of n = 10 and p = ⅙. Learn the formula to calculate the two-outcome distribution among multiple experiments along with solved examples here in this article. In binomial probability distribution, the number of ‘Success’ in a sequence of n experiments, where each time a question is asked for yes-no, then the boolean-valued outcome is represented either with success/yes/true/one (probability p) or failure/no/false/zero (probability q = 1 − p). A single success/failure test is also called a Bernoulli trial or Bernoulli experiment, and a series of outcomes is called a Bernoulli process. For n = 1, i.e., a single experiment, the binomial distribution is a Bernoulli distribution. The binomial distribution is the base for the famous binomial test of statistical importance.

The binomial distribution formula is for any random variable X

There,

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n = the number of experiments x = 0, 1, 2, 3, 4, …

p = Probability of Success in a single experiment q = Probability of Failure in a single experiment = 1 – p

The binomial distribution formula can also be written in the form of n-Bernoulli trials, where nCx = n! /x! (n-x)!.Hence,

P(x:n,p) = n!/[x!(n-x)!].px.(q)n-x (BYJU’S)

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Figure 4: The graph shows that the mean is 10 (expected value) and the chance of getting six heads is on the red left tail. (Investopedia)

<b>2.1.5 Inference statistics </b>

The technique of analyzing the outcome and drawing conclusions from data with random variation is known as statistical inference. Additionally known as inferential statistics. Applications of statistical inference include hypothesis testing and confidence intervals. Based on a random sample, statistical inference is a technique for determining a population's characteristics. Analyzing the correlation between the dependent and independent variables is helpful. Estimating uncertainty or sample to sample variation is the goal of statistical inference. It enables us to offer a likely range of values for an

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p g y g

item's true values in the population. The following elements are included in statistical inference: Sample size, variability in the sample and size of the observed differences.

Types of Statistical Inference

There are different types of statistical inferences that are extensively used for making conclusions. They are:

• One sample hypothesis testing • Confidence Interval

• Pearson Correlation • Bi-variate regression • Multi-variate regression

• Chi-square statistics and contingency table

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• The observed sample is made up of independent observations from a population type like Poisson or normal.

• The parameters of the anticipated model, such as the normal mean or binomial proportion, are evaluated using a statistical inference solution.

• Testing the fit of the model to test the fit of the multiple linear regression model, we use the F value in the ANOVA analysis table. If the sig of the F value < the significance level, then we reject the population's Cheap coefficient as 0 and conclude that the model fits the data set and can be generalized to the population. The sig value in the Coefficients table for t regression parameters is

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significant or not (with 95% confidence, Sig. <5% is significant). (Investopedia)

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Figure 5: The regression line graph will show the relationship between the independent variable (precipitation) and the dependent variable (umbrella sales) (ablebits)

<b>2.2.1 Customer satisfaction </b>

Customer satisfaction has become an important reason to increase competitiveness and it is considered a very important factor in determining the competitive factor of a bank. Customer satisfaction is the repetition of the customer's experience to purchase goods or services and also create new customers by word of mouth to others. Customers' feelings and beliefs also influence the level of satisfaction. If a company's service makes customers happy, then customers will be loyal to that company and thanks to that, the company will keep its customers, which is positive for the company

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because it means that the company earns more money. get more profit, have more market share and also increase base customer. Customer satisfaction is the key to long term business success. And to protect or increase market share,

organizations need to outperform their competitors by providing high quality products or services to satisfy customers. In a nutshell, customer satisfaction is “the customer feels satisfied with his or her expectations about a product or a service that the company provides them”. However, there have been many conceptual studies on customer satisfaction, however, these concepts are abstract and quite ambiguous because customer satisfaction is considered to be the satisfaction of needs and wants. their wishes. Some concepts of customer satisfaction are conceptualized by researchers as follows:

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According to Hunt (1977) (according to Ashim, 2011) satisfaction is consumer evaluation after purchase and experience service. It is the customer's perception of what they wish and expectations have been met or exceeded expected.

According to Parasuraman et al (1988), customer satisfaction customers are their wishes about the perceived difference between the known experience and expectations. That is, experience has known by customers when using a service and the following results when the service is provided.

<b>2.2.2 Service and quality of service </b>

Standing in many aspects, many different angles that people our definition of service is also different. However, the point common to most concepts that service is intangibility, inseparability, heterogeneity, and cannot be stored. Philip Kotler (according to Ashim, 2011) is definition: “A service is an activity or benefit provided intended for exchange, which is essentially intangible and does not result in transfer of ownership. The performance of a service may or may not be tied to a physical product. Gronross (1984) argues that a service is an activity or series of activities that more or less carry normally invisible nature, taking place in the interactions between customers and employees or the supply system service response as solutions to problems arising from client. Service quality is the gap of expectations customer expectations and their perception after using services (According to Parasuraman (1988)). According to the American Society for Quality (ASQ) (according to Tran Hong Hai, 2014) quality represents the superiority of goods and services, in particular is the degree to which people are able to satisfy all needs and customer satisfaction. According to the author group Bui Nguyen Hung and Nguyen Thuy Quynh Loan (2010), each customer often has different perceptions of quality, so participating in customer involvement in the development and evaluation of quality. Service quality is very important. In the service sector, quality is a function of customer perception. Say a way on the other hand, service quality is determined based on customers' perceptions, or feelings, related to individual needs there. In short, service quality is an intangible product. Customers perceive service quality through comparing your expectations with your activities of the business – the supplier organization to meet the expectations of that hope. Customer perception and evaluation of quality service quality not only compares the results achieved but the perception and evaluation of service

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quality is carried out during use and provision of the service.

<b>2.2.3 The relationship between service quality and customer satisfaction </b>

Customer satisfaction and service quality of businesses - organizations have a close, reciprocal relationship together. There have been many studies examining the relationship of these two concepts, they argue that service quality leads to customer satisfaction (according to Oliver, 1993; Corin &Taylor, 1992), service quality is the premise that is the basis for assess customer satisfaction. Therefore, to enhance the customer satisfaction, demanding service providers to further improve its service quality. Two factors these have a close relationship, interacting with each other, in which service quality factors preceded the decision of customer satisfaction client. Because service quality is perceived by customers

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received during and after using the service. Both of these factors are shown by researchers that they have a close relationship with each other. However, according to Oliver (1993) still has a specific difference between these two factors that is:

- Specific service quality measurement criteria possible while customer satisfaction is related to many factors other than service quality such as price, customer relationship, service time, etc.

again a comparison between the received values and the values expectations for the performance of that service.

- Perception of service quality is less dependent on business experience with service providers, business environment while customer satisfaction is highly dependent on more on these factors. There have been many theoretical models developed by researchers applied to analyze customer satisfaction in terms of quality service volume. Among them are some outstanding models as the service quality gap five model

(Parasuraman et al, 1985; 1988), SERVQUAL model, model SERVERF model (Cronin and Taylor, 1992), harmonic index model customer satisfaction (CSI Model).

The research process is a series of thinking steps, applying knowledge about PPNC and specialized knowledge. Specifically, from defining the research problem to finding the answer to the research problem posed.

Identifying hidden variables, building links between latent variables and why relationships exist are important in building research models. After the research model has been built, the next step is to build a research scale to help collect data to test the relationships and hypotheses in the proposed research model.

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Figure 6: Diagram showing the research process

and analysis Make conclusions and

analysis in the report

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and attempting to provide evidence for or against a predetermined hypothesis. In contrast, qualitative research is subjective and inductive, starting with observation and trying to find suitable patterns and processes (Cavana et al., 2001). This study aims to assess the level of customer satisfaction through the quality of services provided by MB-Bank. Survey research by small sample survey method (N=50) to adjust and supplement observed variables so that the scale is more suitable to the reality of business lines.

The study was carried out using a quantitative research method through a questionnaire with a sample size of 50 samples. After being collected, the questionnaire will be analyzed by measurement method and structural modeling through SPSS software. Because this study determines the relationship between service quality and customer satisfaction, descriptive research method is chosen to explain this study.

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