Question one
The analysis of data through the description, and summary of records in a meaningful manner so that the patterns may emerge from such information is known as a descriptive statistics (Investopedia, 2007). However, in certain circumstances, it does not allow data specialists to make conclusions beyond the information to be analyzed regarding any hypotheses that are being prepared.
The descriptive statistics enables one to make a characterization of data based on its properties (Baseline Help Center, 2016). Therefore, the main types of this statistic that can allow one to analyze business decisions are: variation such as standard deviation, range, and variance. Hence, variation will identify the score of data through intervals. It is applied when the data analyst would like to show the spread out of data. Consequently, it is useful when knowing when your data are spread out correctly that it affects the mean.
The Measure of position like quartile and percentile ranks. It will assist while describing the fall of scores in relation to each other. Therefore, it depends on the standardized scores. However, it should be applied when comparing scores to a normalized score such as a national rating. Measure of central tendency (Mean, Median, and Mode). It allows one to distribute data by several points and it should be applied when showing the average, most commonly indicated data responses. Lastly, the measure of frequency, such as count, frequency, and percent. This shows how frequent data may occur. Thus, it is applied when showing how often the response data is provided.
Question two
There are several types of inferential statistics applied during the analysis of data. Moreover, they depend on the type of variables such as normal, ordinal, and ratio or interval. Similarly, the values of a single variable could be systematically higher or lower/ same to the other. For example the wages of men and women. On the other hand, there could be a relationship that may exist between such salaries, and in each case, a correlation needs to be found between them.
Since inferential statistics is the technique that enable people to use the samples of data while making a general conclusion concerning a population from where samples were drawn, sampling is the best type of inferential statistic to be used. This is because it makes accurate representation of the whole population. Secondly, it is appropriate because it comes into action at the time when one may have limited access to the aggregate data regarding the entire population. For example, it might be challenging to get the whole data for any corporation for the last five years. But it is necessary to sample of the financial data within such companies for last five years. Therefore, the samples will assist the data analyst to make some inferences concerning the entire records in the enterprise even if the factual information is absent. Consequently, a good guess will be made on the present data.
Question three
Probability has an ordinary meaning that might not be similar to the mathematical implication (Johnston, 2016). Therefore, a business owner may not act on hunches, instincts, and guesses. This will make business people think that some of their results were probable. The rules of probability can be applied in a more disciplined way as compared to guesswork when making a prediction of possible outcomes for the business plans. For instance, a business may have the following results: worst, likely, and the best case. The scenario of the worst case will have some figure from the lower end of the probability distribution. The likely will contain values towards the center of the distribution. Lastly, the best case will include values in the upper end of the case.
Question four
After making a collection of the information, then it is appropriate to apply the regression analysis to analyze the relationship that may exist between the variables. Consequently, such information will assist the management team of any business to determine the re-order level as well as the time frame that is required when ordering the supplies.
A linear regression is helpful, especially when evaluating the business trends, and making estimates of forecasts. For example, if there is a rise in production in the company for the past periods, the performance of linear regression analysis in the production of the total revenues will be indicated on the y-axis while time will be located on the x-axis. This will create a line that is likely to show the trend of earnings of the firm. After the creation of the trend line, the business may use the slope of the line to make the revenue forecast for the future periods. Moreover, linear regression may be used by any company to evaluate the trends and estimations. That is the when the constant increase in revenues of the business for every year exists, the performance of linear regression on the data will generate a line that shows an upward trend in production. Therefore, after the generation of the trend line, the company may use the slope of the line to make a production forecast for the coming years.
The application of linear regression is essential when analyzing the effects of pricing on the behavior of the consumers. It means that if any firm changes the price of some of its products for several times, the quantity of the total sales for every price and conduct a linear analysis with the total amount sold as a dependent variable while price as an explanatory variable. The resultant line will show the extent to which the consumption of the goods reduced as the prices increase. Thus, acting as a future pricing decisions. Lastly, linear regression is useful during the analysis of risks. For example, an insurance business may perform it when plotting the number of claims against each customer about their ages. Hence, such firms may realize that the elderly clients are likely to make more claims about their health. Therefore, the results of regression analysis will act as a guide to critical decisions made in business risks.
Question five
The time series is useful while studying the past behavior of the business. That is based on this; people can invest their money in such businesses. Therefore, it is the work of business people to make time series of previous sales or proceeds and see the trend of the sales proceeds in that type of business. Secondly, time series acts as equipment in your hand. On such basis, one is capable of evaluating the achievements of the firm. However, the performance will show a good face in the series when there is an upward trend. But if the performance is poor, then new policies need to be made to stabilize the business. Lastly, the time series allows businesses to compare their performance at branch levels. That is, based on their performance from the branch level, they need to be rewarded.
References
Baseline Help Center. (2016). Types of Descriptive Statistics. Retrieved 7 August 2016, from http://baselinesupport.campuslabs.com/hc/en-us/articles/204305665-Types-of-Descriptive-Statistics
Descriptive Statistics Definition | Investopedia. (2007). Investopedia. Retrieved 7 August 2016, from http://www.investopedia.com/terms/d/descriptive_statistics.asp
Johnston, K. (2016). What Is the Importance of Probability Rules in a Business? Smallbusiness.chron.com. Retrieved 7 August 2016, from http://smallbusiness.chron.com/importance-probability-rules-business-31263.html
