Application Of Statistical Knowledge For Business

 Inferential statistics helps us to analyze predictions, inferences, or samples about a specific population from the observations that they make. “With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone” (Trochim, 2006). The goal for this type of data is to review the sample data to be able to infer what the test group may think. It does this by making judgment of the chance that a difference that is observed between the groups is indeed one that can be counted on that could have otherwise happened by coincidence. In order to help solve the issue of generalization, tests of significance are used. For example, a chi-square test or T-test provides a person with the probability that the analysis’ sample results may or may not represent the respective population. In other words, the tests of significance provides us the likelihood of how the analysis results might have happened by chance in a scenario that a relationship may not exist between the variables in regards to the population that is being studied.

At 5% level of significance, is there sufficient evidence to conclude that the proportions of errors in transactions assigned to each of the 5 memory locations are all different?

• Provide at least two examples or problem situations in which statistics was used or could be used.

Descriptive statistics are digits that are used to summarize and describe a given range of data (Klenke, 2008). Basic descriptive data includes, mean, median, mode, variance and standard deviation. The data can be rearranged in an ascending order as follows:

The major of statistics is used in everyday life whether it be through experiments or analyzing data for a company. With the use of statistics, it is not only important to know how to analyze data, but also how to communicate the information to others. Using the communication skills can help companies sell more products and save money on advertisements. With the advancement of digital and/or electronic communication technology, the field of statistics is growing rapidly and their findings are becoming more accurate. Post graduation I intend on working for a company doing business analytics for the products they sale. In this final paper, I will explain to you how the advancement of digital communication

The last few weeks we covered descriptive statistic: the central tendency, variability, correlation and Z-score. Today’s session is a little bit different, we will be talking about statistical significance. Statistical significance is the level of risk one is willing to take to reject or accept a null hypothesis while it is true and it separate random error from systematic error. When doing a study or research, the statistical significance shows that the difference obtained were not caused by chance. Inferential statistics, the T-test, partition noise from bias by studying a random sample than the population in which we are interested and from the results we infer. The advantage of using sample than a population, it is convenient, saves time, energy and money because n is smaller than population and above all it helps to control systematic and random errors. When we are making a conclusion, we should have a certain confidence or probability of being right and that is called the alpha level; which the risk you are willing to

When we look around us, we may not recognize that statistics is all around. Before I began to take this course “Statistics for Managers” I was not aware of how statistics actually worked. The first idea that came to my mind about statistics was probability. Not knowing statistics and probability are related because they both determine a possible outcome. Throughout this course I have learned what statistics is and how it works. In this paper, I will describe descriptive and inferential statistics, hypothesis developing and testing, the selection of statistical tests, and how to evaluate statistical results in analyzing data.

I believe that in the verge of realization, in everyday life’s undertaking we are facing and practicing statistics. I was being into a serious dedication of understanding the underlined concepts of statistics and the necessary usefulness in conducting a survey. In fact, throughout the course, I did learn many factors that became useful in contacting a survey research. according to Walker et al. (2011). However, my success could not be possible without the appreciation and support of my teachers, discussion groups, and class presentations.in addition, the misguided notion of statistics being a tough course was proven otherwise since I realized that statistics make a lot of sense not only in survey research in personal life, especially in budgeting and planning for individual routines. Furthermore, the following paper provides a vivid explanation of my reflection of the survey class, with more attention paid to the concepts of statistics.

The four levels of measurements are nominal, ordinal, interval, and ratio. I work on a pediatric hematology/ oncology floor where we have a bed capacity of 18. At times our pediatric census is low and we will have adults transferred to our unit as overflow. We use the nominal measurement to determine how many adult (A) patients we have and pediatric (P) patients we have. We use this measurement to classify the patients and the letters in this case are used to classify the data. It also helps the nurses know what our patient load is should an emergency arise. Ordinal level of measurement is used to determine the charge nurse on our floor. The most senior nurse on the floor working that day is in charge because she/ he have been there the longest. It is a ranking system that is not evenly spaced out but there is clear reasoning behind it. An interval measurement is the difference between two values and we use this measurement at work while taking temperatures on our patients. For example, if the temperature is 39.5 Celsius and 36.5 Celsius they have the same difference as between 39.0 Celsius and 36 Celsius. This measurement shows a scale from a low interval to a high interval. Ratios are also done when we weigh our patients. There is always an absolute zero when working with ratios, meaning that the can be no number that falls below zero. If we have a patient who weighs 15kg and weigh them again and they weigh

To determine the significance of the statistical data one must determine the relevance of the

3) The performance of personal and business investments is measured as a percentage, return on investment. What

When analyzing data, such as the marks achieved by 100 students for a piece of coursework, it is possible to use both descriptive and inferential statistics in your analysis of their marks. Typically, in most research conducted on groups of people, you will use both descriptive and inferential statistics to analyses your results and draw conclusions. So what are descriptive and inferential statistics? And what are their differences? Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data. Descriptive statistics do not, however, allow us to make conclusions beyond the data we have analyzed or reach conclusions regarding any hypotheses we might have made. They are simply a way to describe our data.

Quantitative data analysis is the process of presenting and interpreting numerical data using descriptive statistics and inferential statistics. The descriptive statistics include both measures of central tendency and measures of variability. Measure of central tendency includes mean, median and mode while measures of viability identify the average, which include range and standard deviation. Inferential statistics provide the outcome of statistical tests that assists a deduction from data collected. Since quantitative research collects data through survey, a protection of human participants is very critical to enhance ethical protocol in the research process. (Weisman, n.d.).

Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. It is applicable to a wide variety of academic disciplines, from the physical and social sciences to the humanities. Statistics are also used for making informed decisions – and misused for other reasons – in all areas of business and government. Statistical methods can be used to summarize or describe a collection of data; this is called descriptive statistics. In addition, patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations, and then used to draw inferences about the process or population being studied; this is called inferential statistics. Both

This video introduces new vocabulary terms. First of all, statistics is the study of variability. Secondly, statistics is broken down into two parts descriptive and inferential. Descriptive is when you collect data and talk about it. Inferential is when you take a sample and use it to make an inference about the population. Thirdly, the video talks about the difference between population and sample. Sample is a smaller part of the population. When you take a sample and come up with an average, that average is called a statistic. When you find the average of population, that average is called a parameter. Lastly, gathering information about the whole population is called a census. In conclusion, a statistic based on a sample helps make an inference about the parameter of the whole population.