## Reflection about Statistics – Essay Sample [New]

Introduction, statistics & probability, data analysis, future studies.

Learning statistics is viewed as an essential subject for every student. It is also crucial to do a reflection about statistics. The author will talk about their experience of learning the necessary skills for analyzing data and predicting possible outcomes. The statistics essay sample is going to speak on the author’s statistics and probability reflection.

I have extensively studied statistics and probability throughout this course. The probability course appeared to be a useful tool to apply in areas of statistical analysis. However, the part of learning statistics is much more prominent.

Statistical knowledge for both statisticians and non-statisticians is essential (Broers, 2006). So it is recommended that people from all fields be given the necessary statistical skills.

For that kind of reason, to gain quantitative skills to be applied and worked on in many ways, I followed this course. In this regard, I had hoped to acquire knowledge in designing experiments. I had wanted to grow in collecting and analyzing data, interpreting results, and drawing conclusions as well (Broers, 2006). I summarized and analyzed the results in my reflection about statistics and probability.

I can proudly say now that I have learned many useful things. I now understand math applications very clearly. I know how to collect, arrange, and explain the details. In data analysis, I may apply central tendency measurements such as mean, mode, and median.

I may also use dispersion measures to explain the data, such as standard deviation and variance. Furthermore, I have a detailed understanding of probability distribution. I can see what conditions are to be fulfilled for a normal distribution (Broers, 2006).

For example, I am aware of conditional probability and its applications. I also know how to use The Poisson process, Brownian motion process, Stochastic processes, Stationary processes, and Markovian processes. I learned the Ehrenfest model of diffusion, the symmetric random walk, queuing models, insurance risk theory, and Martingale theory.

Now, I can determine the relation between the two data sets. I can distinguish dependent and independent variables as well as the sort of relationship between them. I will tell you whether one random variable is causal to another. I am also able to determine positive, negative, and minimal correlations (Broers, 2006).

In most instances, data collection on an entire population is delicate (Chance, 2002). I got the necessary skills in sampling techniques in this respect. I have the skills to analyze sample data. Now I can draw inferences about the entire population using statistical probabilities and hypothesis testing.

In hypothesis testing, one seeks to determine whether the outcomes of a given sample are due to chance or known cause (Chance, 2002). Knowledge is used in the implementation of significance level, critical value, degrees of freedom, and p-value. One must be able to present the null hypothesis and the alternative hypotheses (Chance, 2002).

Now I’m able to use a t-test to assess if there are statistically significant variations between two data sets. In this regard, I understand the required assumptions for the t-test to be applied. I have a clear insight into the analysis of variance (ANOVA), both single and bidirectional. However, I feel that further practice would improve my knowledge of all of those applications (Chance, 2002).

This program has provided me with a good understanding of how statistics play a major part in life. Among other areas, statistics is the most commonly used research method in medicine, education, psychology, business, and economics (Rumsey, 2002). It helps to shape the choices people make in their everyday lives. Statistical studies may provide a clear picture of the consequences. For example, the results of such activities as smoking and contribute to corrective steps.

I was always keen to build a stable scientific career. I have now decided to major in statistics after taking this course. I would like to have advanced statistical skills that will help me to manage and evaluate complex research problems. The knowledge I’ve already obtained in this case will give me a strong foothold.

The goals I had hoped to accomplish by following this cause were well achieved. Now I can conduct experiments and collect, analyze, and interpret data. In real-life scenarios, I can apply that information and draw conclusions that will help develop answers to some issues.

Also, I have thoroughly studied and understood various causes of probability. I will be able to apply this knowledge where needed. Nevertheless, statistics will remain my main field of research.

This course, therefore, gave me the desire to seek additional statistical knowledge. For this reason, I intend to be in a better position in statistics to deal with more complex research issues.

- Broers, N. J. Learning goals: the primacy of statistical knowledge. 2006, Maastricht: Maastricht University.
- Chance, B. L. Components of statistical thinking and implications for instruction and assessment . 2002, Journal of Statistics Education, 10(3)
- Rumsey, D. J. Statistical literacy as a goal for introductory statistics courses. 2002, Journal of Statistics Education, 10(3)

## ❓ What should I include in my statistics reflection paper?

## ❓ Why are statistics so difficult?

## ❓ How do you start a reflection on statistics?

## ❓ What is the aim of statistics?

## ❓ What is the importance of statistics and probability?

## Cite this paper

StudyCorgi . (2020) 'Reflection about Statistics – Essay Sample [New]'. 5 November.

## Reflection on Statistics Learning Goals Essay

## 📌 How do you write a statistics essay?

Need a custom Essay sample written from scratch by professional specifically for you?

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## Reflective essay about your learning experience on using statistical techniques in data analysis

## New questions in Math

## STATISTIC 2011

Is there a positive realtionship between the armspan and the height.

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- 2. ANALYZING STATISTICS 2 Reflection of Analyzing Statistics Statistics is a word commonly used in daily discussion. The presence of statistics follows society in almost every decision a person can make. For example, what college to attend based on how well previous students have done, or betting on a particular horse during a race because he is said to win (Lind, D. A., Marchal, W. G., … 2011).. Even on Wall Street financial experts are constantly checking the numbers related to stocks such as the Dow Jones or Nasdaq. Reading the numbers and percentages off of a board is the easy part; converting raw data into a numerical order or placing a value to qualify the significance is where it gets tricky. Understanding what statistics do to impact decision making is extremely prevalent in daily life; however, deciding what values to assign to particular categories or what graph best suits the information provided is where the challenge begins. There are two types of primary research: quantitative data collection and qualitative data collection. Quantitative data collected is based on numbers. For example collecting data related to peoples age or income. The information collected can be analyzed after the collection using various statistical techniques. This analysis helps the researcher create meaningful patterns and take a deeper dive into the data. The most important use of quantitative data is in hypothesis testing and can help researchers in reaching conclusions. The main methods used to collect quantitative information are using questionnaires that require input of the user’s response. The survey can be distributed using different means, such as mail, phone, and websites. Qualitative data can be collected using personal interviews, focus groups, and observations carried out for the purpose of research (Siddharth, 2011). Although personal interviews can help researchers dig into greater details, they are time consuming and expensive.
- 3. ANALYZING STATISTICS 3 Available data from the Ballard Integrated Managed Systems (BIMS) employee survey afforded us the opportunity to quantify results and make an inference as to the possible reasons for the high turnover and low morale cited by management. As such, we formulated our initial hypothesis, that poor communication was a major contributing factor. To further test our hypothesis, our consulting firm made the recommendation to continue with the approach of using an employee survey instrument after removing or refining any ambiguous questions. A quarterly collection of data will allow us to reassess the data across consistent elements while attempts are made to increase response rate, thus giving us a better sample of data to derive conclusions from. Ordinal data is a scale of measurement in which any given class is higher or lower in rank than the other. For example in the given questionnaire, five is better than four, four is better than three, three is better than two, and two is better than one. Question A, C, and D represents nominal level data. This data can be represented in any order. There is no natural order for these. For instance, for the gender question one can either report males first or the females. Question B is quantitative in nature and can be classed as ratio level data. In this level of data the zero point is meaningful because the time spent being employed at BIMS cannot be a negative number. A person could have worked for a year but not negative one year. Team C consultants have already delivered two responses based on the survey. The first response was the sample size was too small. BMIS need a much higher participation rate to gain meaningful evidence of the root causes of the problem. The second response addressed the low scores on communication. While BMIS continues administering the survey, Team C consultants can test the hypothesis of communication being a critical problem within the company. Team C consultants have compared the means of the answers to question nine on the BMIS survey. Of all 78 responses only one could not be identified as food, housekeeping, or
- 4. ANALYZING STATISTICS 4 maintenance. Of the remaining responses 32 came from food, 36 came from housekeeping, and nine came from maintenance. The mean responses were 2.03, 2.33, and 2.33 respectively. This shows the food division has the lowest scores on communication, and has the most room for improvement. There are several different kinds of relationships between variables. Before drawing a conclusion, you should first understand how one variable changes with the other. This means you need to establish how the variables are related - is the relationship linear or quadratic or inverse or logarithmic or something else? Relationships between variables need to be studied and analyzed before drawing conclusions based on it. In natural science and engineering, this is usually more straightforward as you can keep all parameters except one constant and study how this one parameter affects the result under study. One variable that must be considered and compared is the response rate among the divisions. The overall response rate of 17% translates to each division where no division carried a response rate above 30%. This also supports the response of needing a larger sample size. A larger sample will help draw more accurate and complete hypotheses and conclusions.
- 5. ANALYZING STATISTICS 5 References Siddharth, K. (2011). Relationship between variables. Retrieved 13 Sep. 2012 from Experiment Resources: http://www.experiment-resources.com/relationship-between-variables.html Lind, D. A., Marchal, W. G., & Wathen, S. A. (2011). Basic statistics for business and economics (7th ed.). New York, NY: McGraw-Hill/Irwin. McClave, J. T., Benson, P. G., & Sincich, T. (2011). Statistics for business and economics (11th ed.). Boston, MA: Pearson-Prentice Hall.

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## Basic statistical tools in research and data analysis

## S Bala Bhaskar

## INTRODUCTION

## Quantitative variables

## STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS

Example of descriptive and inferential statistics

## Descriptive statistics

## Measures of central tendency

Example of mean, variance, standard deviation

## Normal distribution or Gaussian distribution

## Skewed distribution

Curves showing negatively skewed and positively skewed distribution

## Inferential statistics

Illustration for null hypothesis

## PARAMETRIC AND NON-PARAMETRIC TESTS

Two most basic prerequisites for parametric statistical analysis are:

- The assumption of normality which specifies that the means of the sample group are normally distributed
- The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

## Parametric tests

where X = sample mean, u = population mean and SE = standard error of mean

The formula for paired t -test is:

where d is the mean difference and SE denotes the standard error of this difference.

A simplified formula for the F statistic is:

where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

## Non-parametric tests

Analogue of parametric and non-parametric tests

Median test for one sample: The sign test and Wilcoxon's signed rank test

If the null hypothesis is true, there will be an equal number of + signs and − signs.

## Tests to analyse the categorical data

## SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS

There are a number of web resources which are related to statistical power analyses. A few are:

- StatPages.net – provides links to a number of online power calculators
- G-Power – provides a downloadable power analysis program that runs under DOS
- Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
- SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

## Financial support and sponsorship

There are no conflicts of interest.

## Statistical analysis essay

The arguments are discussed and justified with values in the body of the essay.

## Writing a Commentary Essay

## What is a statistical analysis essay?

## Where do you start writing a statistical analysis essay?

The most popular methods of data collection are;

## How to write a statistical analysis paper – step by step

Step 2: Get an outline of statistics paper

- Introduction : This part discusses what background information is needed on the given topic and why this study has been conducted (problem statement).
- Body : The main body gives details on how the problem was analyzed.
- Discussion : This part draws out certain conclusions based on methods used in data collection and analysis.
- Conclusion : Conclusions are drawn together based on the findings of this research and a call to future research is given.
- References : The sources used in this paper should be listed here.

Step 3: Collect Data and Analyze it with SPSS or any statistical analysis software

Take care of following points when you collect and analyze the data:

- The sample size you have taken must be big enough to represent your whole population properly; otherwise your data would be considered as inadequate;
- Approach to analysis should be clear and consistent. You must have a reason for choosing one over other alternatives of statistical techniques like simple regression, multiple regression etc.;
- You should describe your data by giving different characteristics including nominal, ordinal, interval and ratio scale; what type of variables you used in data collection (qualitative or quantitative); if the variables are qualitative then their classifications i.e., how many classes/groups they consist of would be given too;
- The same discipline must also be followed while presenting the outputs from SPSS such as tables (in reports), graphs, figures and diagrams (in magazines). These represent your data graphically so that they can understand it easily.

Find out more about our spss assignment help services.

Step 4: Drafting the report (or statistical analysis essay)

Step 6: Proofreading the report

Step 7: Style & Formatting of your statistical analysis report

## Let’s look at each of the components of a statistical analysis essay.

- Methods – The data collection method used describes how the data you will present was collected. Also, give an account of why you settled on that method.
- Data presentation – You represented the data collected in the form of graphs, tables, or charts. Pick the best one to present your data best.
- Data analysis – Here, you break down your data into simpler forms for the reader.
- Discussion – In this section, you bring out your arguments pulled from the data. It is here where you express your views about that topic, as shown by the data. You discuss the analysis and the results of the research.

## How to write a data analysis paper

While doing any kind of data analysis, there are three important steps that should be followed:

What is descriptive statistics?

What should be considered before conducting descriptive statistics analysis?

- objective of descriptive analysis (what is the main purpose of conducting this procedure);
- type of data used for descriptive analysis i.e., what kind of collected information are going to be analyzed using statistical techniques/methods (quantitative or qualitative etc.).

What are the tools most commonly used by descriptive statistician?

How to analyze Quantitative Data?

How to analyze Qualitative Data?

## Statistics analysis paper writing tips

- Make sure your statistical analysis paper is well-written, and has proper grammar.
- Be creative with word choice! You may also choose to add quotes from the literature that support your hypotheses and claims.
- Using quotes from reputable sources will strengthen the impact of your work. Remember, one must cite their work when using information taken from another source (i.e., a book/journal article).
- When referencing a quote, make sure you include page numbers!
- Your paper should be a minimum of 3 pages long.
- A statistics analysis research paper should have an intro section which includes problem statement and research question; methods used; hypothesis development; results conclusion; discussion section regarding finding relevance to real life problems, any limitations of study, future studies that could be done; reference list should be at end.
- Avoid grammar mistakes when writing your statistics essay!

## Tips on Writing a Statistical Analysis Essay

- Ensure you interpret statistical data correctly
- Support calculation of the statistics with the procedure used to reach the final figures
- Include visuals such as tables and graphs in your essay.
- Avoid grammatical and spelling errors in your essay.
- If necessary, include as much information as possible to enable the reader to interpret and reconstruct your argument.

## 5 steps if writing a good statistical analysis essay

Following steps will guide you through the process of writing a good statistics analysis paper:

Step 1 : Exploring your topic and gathering information .

Step 2 : Making outline for statistical analysis paper (or report).

Step 4 : Editing and proofreading your draft.

## Statistical Analysis Paper Example

Title : Statistical Analysis in Psychology – A Case Study

Authors’ names and affiliation : Student Name, University of New York State School of Psychology

## Statistics essay writing help

- Business Statistics assignments – deals with assessment of past events and comparisons between objects within certain data, market analysis or trend predictions for the future;
- Research Statistics – covers conducting statistical surveys and experiments which are required while working on qualitative research projects as well as evaluating collected material according to predetermined criteria. Some require advanced calculations such as using chi-squared test or t-test;
- Political Statistics papers – deals with presenting political trends from past election years in comparison with current or upcoming elections and reports the results.
- Legal Statistics papers – covers analyzing past court decisions to determine if they are followed by current legislations or not;
- Epidemiology assignment – deals with research of living organisms, their distribution and way of life through statistical surveys in order to establish trends which can be used for better understanding of certain phenomena within natural environment.
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## COMMENTS

In most instances, data collection on an entire population is delicate (Chance, 2002). I got the necessary skills in sampling techniques in this

What I Have LearnedUsing the space below, write a reflective essay about your learning experience onusing statistical techniques in data analysis.

I have skills to analyze data from a sample and make conclusions regarding the entire population by using statistical probabilities and test of

Reflective essay about your learning experience on using statistical techniques in data analysis - 11947352.

Overall, this project has been a good learning experience for me. I become more knowledgeable towards satistics and it will definetly aid me in future research

All three methods allowed me to learn the importance of comparing data to one another, understand probability, and test the significance of data. Overall, the

The most important use of quantitative data is in hypothesis testing and can help researchers in reaching conclusions. The main methods used to

Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data.

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and

It is a requirement from many universities that student should show some evidences collected during their data analysis process as well they