Question one
The descriptive statistics is the analysis of data for describing, showing, and summarizing records in a meaningful way so that the patterns to emerge from the information. However, it does not allow the analysts to draw conclusions beyond the data to be analyzed concerning any hypotheses being made. The types of descriptive statistics are as follows:
The measure of central tendency. It is the way of describing the central position of a distribution for the collection of data. That is the frequency distribution the distribution pattern of records of a company particularly from the lowest to the highest. Therefore, the central position can be described by the use of statistics such as mode, median, and mean
The measure of spread.it the way of summarizing a group of data of the company by describing the spread of the nature of the score. For instance, the mean of the total sales in a company may be 75 out of 100, but not all sales people met the target of 75%, in this case, their sales will be spread out where some will be low and others high. Consequently, the measure of spreads is important because it will enable the managers to make summaries on how the spread out of the scores. Similarly, it has several statistics such as range, quartiles, variance, and standard deviation.
Question two
Sampling
The inferential statistics are the methods that allow individuals to apply data samples to make generalizations regarding the population from which samples were made. Therefore, it is essential to make accurate sampling represent the entire population. This type of statistics comes into action when one may not have access to the aggregate information concerning the population. For example, if one need to get the average sales for Apple Inc. for ten years, it might be difficult to obtain such information that you want (Klazema, 2014). Instead of getting the information for ten years, it is necessary to take a small sample of the data that is already with the firm. The sample will enable the analyst to make inferences regarding the whole records of the total sales in the company even though the factual data are not present. Thus an educated guess is made on the data that may be present.
Question three
Probability distribution
It is a statistical model that shows the possible results of particular events, and the likelihood of every statistical event (Richards, 2016). In this case, a corporation like Apple may experience a distribution concerning change in sales revenue for a given marketing campaign. Therefore, both left and right end values of the distribution are less likely to arise as compared to those in the middle of the curve. It can be applied during the creation of scenario analyses. Consequently, the scenario analyses will create many theoretical and different potentials for the result of a given course of action of future occurrence. Consequently, the distribution chances will allow a company to generate some scenarios such as the worst, likely, and best; where the worst scenario will contain certain figures from the lower part of the distribution. Similarly, the likely situation will have values towards the last part of the middle distribution while the best scenario will contain the prices in the higher part of the case.
Practically, the application of the probability distribution in business problems will assist in predicting the level of sales (Richards, 2016). Since it is difficult to make a prediction on the exact price of the future level of sales, the company may plan for its future events. The application of situation analysis rely on the probability distribution and it can help an enterprise to set its probable future figures concerning the likely level of sales revenues, worst case, and best case scenarios. This will enable the company to rely on its business plans while considering the likelihoods of the alternative
Question four
After data collection, the regression analysis should be applied to analyze the relationship that may exist between the variable. This will be important especially to the managers because it will help the management team to determine the reorder level and the timeframe that is required for ordering the supplies. Linear regressions are used in business for evaluating business trends, and making estimates of forecasts. If there is an increase in revenues for the Apple Corporation for the previous years, the conduct of linear analysis on the total sales revenue with the sales represented on the y-axis and time on the x-axis will generate a line that is likely to depict the upward sales trend. Therefore, after the creation of the trend line, Apple Company can apply the slope of the line to make a sales forecast for the coming years. Linear regression can be applied in business to evaluate the business trends and make estimates. For example, a steady increase in sales revenue for each year for the past five years exists, the performance of linear analysis on sales data will produce a line that illustrates the upwards trend in sales. Therefore, after the creation of the trend line, the company may apply the slope of the line to forecast sales for the future years.
It can also applied when analyzing the influence of pricing on the behavior of consumers (Hamel, 2016). This implies that if a company changes the price of some goods and services for several times, then such a company may record the quantity it sells for every price and performs a linear regression with the quantity sold as a dependent variable and price as the explanatory variable. The result will be a line that indicates the degree in which the consumers reduced their consumption of the goods with the increase in prices. This will act as a guide for future pricing decisions. Additionally, linear regression is also useful during risk analysis. That is an insurance firm may conduct a linear regression to plot the number of claims against every client regarding their ages. Therefore, such firms may realize that elderly clients tend to make more claims about their health. Consequently, the results of the analysis will guide crucial business decisions made in accounting for risks.
Question five
Time series is important because it is applicable in several ways to help the top managers to make better decisions while addressing any business problem. Thus, the managers will consider several questions concerning the applicants that have been processed within the previous years as compared to the present period. Moreover, time series will enable the management team to know whether there is anything that took place differently within the past year, and the exact time of the year that the supplies run out.
References
Hamel, G. (2016). What Are Some Ways Linear Regression Can Be Applied in Business Settings? Smallbusiness.chron.com. Retrieved 3 July 2016, from http://smallbusiness.chron.com/ways-linear-regression-can-applied-business-settings-35431.html
Klazema, A. (2014). Descriptive and Inferential Statistics: How to Analyze Your Data. Udemy Blog. Retrieved 3 July 2016, from https://blog.udemy.com/descriptive-and-inferential-statistics/
Richards, L. (2016). The Role of Probability Distribution in Business Management. Smallbusiness.chron.com. Retrieved 3 July 2016, from http://smallbusiness.chron.com/role-probability-distribution-business-management-26268.html
