BUS308 Statistical Methods discussion

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Question Description

Below are the initial requirements for the Final paper I hired a tutor to help me with. The paper I received was poorly written, did not cite any of the references and came back with a high % on the trunit in site for plagiarism. I am in need of serious help as my passing grade depends upon this paper and it was due yesterday thanks to that tutor messing me up. I have lectures with the course material and the paper I was given if you'd like to re work it or build from it. If you do decide you can help me I will send you some additional info after you accept as I can only upload a couple files and I want to attach the paper and checker report .

Thank you!



Final Paper

The Final Paper provides you with an opportunity to integrate and reflect on what you have learned during the class.

The question to address is: “What have you learned about statistics?” In developing your responses, consider—at a minimum—and discuss the application of each of the course elements in analyzing and making decisions about data (counts and/or measurements).

In your paper,

  • Discuss the following course elements:
    • Descriptive statistics
    • Inferential statistics
    • Hypothesis development and testing
    • Selection of appropriate statistical tests
    • Evaluating statistical results.

The Final Paper

  • Must be three to five double-spaced pages in length (not including title and references pages) and formatted according to APA style as outlined
  • Must include a separate title page with the following:
    • Title of paper
    • Student’s name
    • Course name and number
    • Instructor’s name
    • Date submitted
  • Must begin with an introductory paragraph that has a succinct thesis statement.
  • Must address the topic of the paper with critical thought.
  • Must end with a conclusion that reaffirms your thesis.
  • Must use at least three scholarly sources in addition to the course text.
  • Must document all sources in APA style as outlined in the Ashford Writing Center
  • Must include a separate references page that is formatted according to APA style

Unformatted Attachment Preview

Running head: FINAL PAPER 1 Final Paper Brittany Grace Instructor: Christina Waddell BUS 308 February 4, 2019 FINAL PAPER 2 Final Paper Introduction Statistics is the collection, organization, presentation, analysis and interpretation of data. Throughout this course we examined a great deal of raw data and learned how to turn into information we can use. Not only were we able to use the data but also make decisions, identify patterns, and identify existing relationships by working with these tools:descriptive statistics, inferential statistics, hypothesis development, selection of appropriate statistical tests and evaluating statistical results. Descriptive Statistics As we learned early on, “the first action in any analysis involves collecting the data” (WK 1, Lecture 2). Descriptive statistics focuses on analyzing a particular data set and focusing our attention only on that group when examining the information. However, if we decide to look outside of our group and make inferences, claims and or conclusions based on a larger population we will need to use inferential statistics. Descriptive statistics recognizes the inferential insights, graphic insights intend to outline the example rather than expand into information from a larger population. It basically provides a summary of the sample or the entire population, encompassing the indicators of central tendency and indicators of dispersion, such as the mean, median, mode, and others to understand the data better. Measures of variation represent the contrast of measures of center, because they show the dispersion of values in the data set, and they include the standard deviation, variance and range. As an example, we can have two sets of data with the same mean, but with very different values. Graphics such as histogram, scatterplot, boxplot help to have a clearer picture. Scatter plot shows the linear relationship between two variables, while boxplot tells about the minimum, first quartile, FINAL PAPER 3 median, third quartile and maximum values. Boxplot also helps in the detection of outliers in the data. Descriptive statistics describe the data set and does not make any inferences about data. This type of statistics describes a given sample or population by adding up the features of certain data about the same sample. Hence the descriptive statistics is very helpful to get the basic idea of the data set or sample. For example, descriptive statistics methods can help a network manager for household appliances stores to compare last week's weekly sales, different sales points, as follows: weekly sales are summarized (possibly grouped on types of household appliances) in several numerical levels: average sales level weekly, the degree of variation in sales versus their average. Tables and charts help to clearify and easier retrieve the information you get so you can quickly spot the key differences in sales between the two points of sale. The manager might also want to open another sales outlet in an area of a city; he can organize a selective statistical survey in which to find out if the citizens included in a sample appreciate this initiative as positive and if it is interested in shopping at the new point of sale. The manager will expand the results sample research across the population, and so he makes an inference statistics. Inferential Statistics Inferential statistics is a subset of mathematics used in the analysis and interpretation of data. It is used to test hypotheses to find out if the outcome of a study has significance in statistics. This type of statistics is also a family of tools that support sociologist and allows them to draw conclusions from data and is used to test whether a certain behavior is due to chance or certain underlying phenomenon. A wide range of statistical methods is available for testing hypotheses. Each of this technique is appropriate to a different type of experimental design. Statistical tools include z statistic, t-test, and ANOVA. We can take the results of an analysis FINAL PAPER 4 using a sample and can generalize it to the larger population that the sample represents. In order to do this, however, it is important that the sample is representative of the group to which it is being generalized. In other words, inferential helps to achieve one of the main purposes of statistics, which is to draw conclusions and inferences from a sample representation of a population. The two main problems, the estimation of a population parameter and hypothesis testing are solved with the help of inferential statistics. Hypothesis Development and Testing When you conduct a piece of quantitative research, you are inevitably attempting to answer a research question or hypothesis that you have set. One method of evaluating this research question is via a process called hypothesis testing, which is sometimes referred to as significance testing. One of the main steps in statistical analysis is the hypothesis testing. The hypothesis is an empirically declarative statement that depicts the relationship between independent and dependent variables. Hypothesis development always begins with the identification of a research question. In hypothesis testing, the purpose is usually to reject the null hypothesis. The null hypothesis is the condition of null, where no difference between means no relationship between variables is detected. Data is collected that allows us to decide if we can reject the null hypothesis, and do so with some confidence that we're not making a mistake. The alternative hypothesis is the opposite condition of the null hypothesis. After collecting the data, it is analyzed, and a conclusion is made whether to accept or reject the null hypothesis. Accepting the null hypothesis means that if the data are normally distributed the results might be due to chance. Selection of Appropriate Test and Evaluation of Results FINAL PAPER 5 Selection of the appropriate statistical test is a vital process in the statistical analysis of data. To select the ideal statistical test one has to consider the type of data involved and whether it is dealing with normal distribution or not and also in consideration with the objectives of the study. There are many tests such as parametric and non-parametric tests. We have to consider if there is one sample, two samples or more than two samples in our data set. The sample size is also important when choosing between a t-test and a z-test. However, it is important to recognize whether the data is collected through experimental design or observational. After selecting the appropriate test for the sample this is the main thing of the whole testing procedure when we test there is a test statistics, we will calculate the test statistics from the sample based on the data under the null hypothesis. To evaluate data all relevant information for the given sample must be pooled. This implies that all data obtained must be used in a calculation of statistical parameters. Conclusion In conclusion, statistic testing is essential in every research based on evidence, which offers every detail needed to make conclusions. As found above, the statistic process involves some major steps that cannot be omitted by the researcher or detective as we portrayed throughout this course. The vital steps comprise of: descriptive statistics, inferential statistics, hypothesis development, selection of appropriate statistical tests and evaluating statistical results. Without considering all of them, the research process is not complete. FINAL PAPER 6 References Berenson, M., Levine, D., Szabat, K. A., & Krehbiel, T. C. (2012). Basic business statistics: Concepts and applications. Pearson Higher Education AU. Heiman, G. (2015). Behavioral Sciences STAT 2. Stamford, CT: Cengage. Lowry, R. (2014). Concepts and Applications of Inferential Statistics. Peck, R., Olsen, C., & Devore, J. (2011). Introduction to statistics and data analysis. Nelson Education. GraceBUS308Final.doc by Brittany Grace FILE GRACE_BUS308_FINAL.DOC (50K) T IME SUBMIT T ED 05-FEB-2019 06:50PM (UT C-0800) WORD COUNT 1241 SUBMISSION ID 1073706496 CHARACT ER COUNT 6738 GraceBUS308Final.doc ORIGINALITY REPORT 52 12 3 52 % % % % SIMILARIT Y INDEX INT ERNET SOURCES PUBLICAT IONS ST UDENT PAPERS PRIMARY SOURCES 1 2 3 4 5 6 43 Submitted to Bridgepoint Education % St udent Paper 4 www.studymode.com % Int ernet Source 2 Submitted to King's Own Institute % St udent Paper 1 cte.drupal.ku.edu % Int ernet Source 1 Submitted to Kenyatta University % St udent Paper 1 Submitted to Westcliff University % St udent Paper EXCLUDE QUOT ES ON EXCLUDE BIBLIOGRAPHY OFF EXCLUDE MAT CHES OFF ...
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Attached.

Statistical Methods - Outline
Thesis Statement: Through the lectures on statistics, I gained knowledge on the basic
approaches to statistics, especially the process of developing and testing a hypothesis through
statistical methods.
I. Introduction
II. Descriptive statistics
III. Inferential statistics
IV. Hypothesis development and testing
V. Selection of appropriate statistical tests
VI. Evaluating statistical results
VII.

Conclusion


Running head: STATISTICAL METHODS

1

Statistical Methods
Name
Institution

STATISTICAL METHODS

2
Statistical Methods

Many scholars are constantly presenting the idea that statistical literacy if going to be an
essential aspect to citizenship. Lessons in statistics have increased my understanding of the
field’s importance in different applications in the world. Statistics was portrayed as a field in
mathematics that deals with data. Dealing with data involves more than just collecting but also
manipulating it to make sense and be more useful in the field. While looking at descriptive and
inferential data, the course highlighted the uses of statistics and when handling hypotheses and
tests, the course was looking into the ways of manipulating data using statistical and research
approaches to come up with meaningful results and apply them. Through the lectures on
statistics, I gained knowledge on the basic approaches to statistics, especially the process of
developing and testing a hypothesis through statistical methods.
Descriptive Statistics
The two main branches of statistics based on the uses are descriptive and inferential.
Descriptive statistics is the basic approach to the subject and it refers to the use of data to
describe a phenomenon or state of affairs as they are. According to the second lecture of week
one, we defined descriptive statistics as describing a fixed population (Class Notes). This
suggests that descriptive statistics provides an overview of the state of affairs or events in a
particular population and as they are, they are not much useful in describing a larger population.
The main role of descriptive statistics that I learned from this course, therefore, is to provide a
summary of the basic ‘wh’ questio...

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