Reflection Paper
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Reflection Paper
In the reflective essay, I intend to analyze ways in which statistics, as well as the course material in it, has helped in enhancing a comprehensive understanding of the workplace as well as ways in which statistical analysis helps in the decision-making process by the managerial process. Most of the workers of firms fail to realize ways of using statistics is prevalently used within the workplace in determining the outcomes. Even if a person is designing products or evaluating employees and customers, the managers of today face significant complications compared to what they have ever experienced. Statistics enable the managers with increased confidence in dealing with uncertainty while helping managers in making significant decisions promptly. I think that managers can be able to provide firmer leadership due to the usage of statistics within the workplace.
Statistics can back up the statements. A person can be able to conclude that a statistical tool is a relevant tool. Most of the time, the leaders end up finding themselves within a corner backed when they are involved in persuading individuals into making moves towards a particular direction or taking a risk. The statistics can provide an objective goal accompanies with hard evidence as well as objective figures. Additionally, statistics can pinpoint the existing relationships between different types of variables. For instance, a variable can be specific offers on sales, expenditure, or changes in revenue, among others. Conducting a comprehensive investigation into data can further reveal the hidden concepts that can lead to enhanced control over purchases, sales, as well as customer satisfaction.
In addition to the relevance to statistics, it ensures quality. Upon conducting an evaluation of the quality assurance (which the firm I am working for is doing continuously) or continuous improvements, a person can notice the essence of the statistics. Statistics can provide the means of controlling as well as measuring processes for lessening the differences that can develop errors which in turn can derail organizational performance. A manager can decide to conduct surveys to find out if there is sufficient demand for targeted consumers. Survey results can be used in justifying the spending (or not) upon the company engaging in product development.
Descriptive statistics can be defined as numerical, graphical, as well as tubular research data summaries. We usually cannot measure the entire population that we are interested in (for example, all women, or people with depression), so we measure a subset and then use statistics to make guesses about the population as a whole. Descriptive statistics is essential in the statistical field since it can facilitate the presentation as well as the interpretation of data. Through the usage of the inferential statistics, one can be able to conclude the extent beyond such immediate data. We can utilize such data in making judgments concerning the profitability in making inferences from the data to moving general conditions. Inferential statistics allows us to use what we have learned from descriptive statistics. Using data from the past and the resulting descriptive statistics, mean, mode, median, standard deviation, etc. inferential statistics extend beyond the immediate data. Inferential stats are used to infer from the sample data what the population might think. Inferential statistics is also used to make judgments about the probability that an observation is dependable or one that happened by chance in the study. For instance, assuming that the statistical method suggested that, on average, the payment issued towards men is more significant in comparison to the one issued to women after working full-time. Even though there may be various explanations, the inferential statistics can provide data as evidence to substantiate its claims. Inferential differ from descriptive in that inferential stats make guesses about the population, whereas descriptive describes the subset.
The following introduces some of the standard inferential statistics techniques briefly as well as it is intended to act as a guide for determining the appropriateness of certain techniques. The inferential statistics techniques are generally used depending on the types of variables that are involved in the process, be it interval, ordinal, or nominal. The tests are usually used with the contingency tables that the group observations are based on the common statistics. For instance, Analysis of variance (ANOVA) splits an observed aggregate variability that is found inside the data set. Then separate the data into systematic factors and random factors. In the systematic factor, that data set has statistical influence. On the other hand, random factors don’t have this feature. The analyst uses the ANOVA to determine the influence that the independent variable has on the dependent variable. With the use of Analysis of Variance (ANOVA), we test the differences between two or more means. Most of the statisticians have an opinion that it should be known as “Analysis of Means.” We use it to it test the general rather than to find the difference among means. With the help of this tool, the researchers can able to conduct many tests simultaneously.
After reflecting on the last couple of weeks in class, I eliminated the fear of pursuing the statistics class. When I started the syllabus, I became nervous about the complicated statistical terms. However, I came to learn them and found out they are not that complicated as I thought. I can apply such knowledge in the workplace to improve productivity.