business decision making part 3

Decision making part 2

Scott Mosley

QNT/275

10/16/2017

Mark Vitale

· Identify which types of descriptive statistics might be best for summarizing the data, if you were to collect a sample.

Descriptive statistics is a term that is accorded to an analysis of data which helps in description, showing or summarizing data in a manner termed as meaningful. Looking at descriptive statistics, patterns could emerge from data which is defined by the descriptive statistics. However, descriptive statistics do not allow individuals to make conclusions beyond the data that have been analyzed or rather arrive at conclusions based on the hypotheses that have been made in the research (Davis, 2013). It is simply a manner in which data can be described. Data is usually exposed to hypothesis testing and sets of these data is usually defined through the use of descriptive statistics.

Descriptive statistics is very important because in case there was just the presentation of raw data, then it would be very hard to visualize what is really being represented by the data especially when we have a lot of data. Descriptive statistics therefore enables individuals to be in a position of presenting data in a way that is termed as meaningful. This consequently allows for the effective and simple interpretation of data that have been presented from a research process. For instance assume there is 200 pieces of the coursework of students and we are interested in analyzing the overall performance of these students. There could also be the need to distribute the overall spread of the marks to the students. This is made possible through the use of descriptive statistics.

Some of the types of descriptive statistics that are best in summarizing data in case I were to collect a sample include the following;

i. Measures of central tendency- this is the description of the central position of the frequency distribution for a given set of data. In such a case, frequency distribution is simply a distribution of the marks that are scored by the students.

ii. Ordinal data- this is an example of descriptive statistics that enables us to rank the data as well as aid in the categorization of data.

iii. Measure of spread- these are ways of summarizing a set of data through describing the tendency in the spread of the scores.

· Analyze which types of inferential statistics might be best for analyzing the data, if you were to collect a sample.

Inferential statistics is drawing conclusions of large sets of data usually called population. It involves making conclusions based on the trends of a population. In analyzing inferential statistics, we usually use regression analysis. Regression analysis is used in the examination of the relationship that subsists between two or more variables. We usually use inferential statistics in trying to infer from a sample data the assumption that the overall population might be having. Consequently, we use inferential statistics in making judgments of the probability that an observed difference in groups is dependable and that it might have happened because of chance.

With the inferential statistics, one is trying to reach conclusions extending beyond the immediate data that is collected. Judgments are usually made with reference to the inferential statistics and as such drawing an assumption on what the general population might be thinking of. It is used in making inferences from the data to more generalized conditions. This is different from descriptive statistics which is used in the description of what is really going on in the data that we have collected (Brockwell & Davis, 2016). Inferential statistics are usually useful in experimental research design or in the evaluation of the program outcome. One of the simplest inferential test is used in the event that you want to compare the average performance of the different groups of data that have been collected. In the event that one is interested in establishing an average performance between two different groups, they should consider using the t-test in analyzing the difference existing between groups.

· Analyze the role probability or trend analysis might play in helping address the business problem. 

Probability and trend analysis helps a business in making different forecasts which are in line with the marketing trends in the business. A probability analysis is the statistical model which shows the possible outcomes of a given event or a particular action course and the statistical likelihood for each of the events. Analyzing the trends in a business is very important because they shall make productions based on the actual sales forecasts rather than just making decisions without any factual data (Fairhurst, 2015). It is important to also note that probability analysis helps an organization to determine whether they shall undertake a particular course of action or abandon a particular action course that they have pursued.

· Analyze the role linear regression for trend analysis might play in helping address the business problem.

Linear regression is the statistical model which attempts to show the relationship that subsists between two different variables. It involves graphing a line over a given set of data points which closely fits an overall shape of data. It shows an extent to which a change in dependent variable usually contained in the y-axis can be attributed to a corresponding change in the dependent variable which is contained in the x-axis. This helps in addressing different business problems because a particular situation is exposed to different business situation which exposes a given variable to a condition that is determined by the outcome of the other variable (Brockwell & Davis, 2016).

Linear regression can be used in a business context in evaluating the trends as well as making estimates and forecasts. In case the sales of a company have been rising steadily over the last few months, conducting of a linear regression analysis with monthly sales on y-axis and time on the x-axis would produce a regression line which depicts an upward trend in the sales of the business.

· Analyze the role a time series might play in helping address the business problem. 

Time series is a sequence of data (numerical) that is arranged in a successive order. Time series tracks the movement of the various chosen data points for instance the price of security over a specified time frame with the data collected being recorded at intervals that are regular. Time series helps a business in analyzing the trends of the business as well as the exposure of some conditions to the business. Time series is significant as program managers as well as coordinators rely on it for the development of strategies and objectives which are important for the overall business growth and development.

References

Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting.

Davis, B. (2013). Managing business analysis services: A framework for sustainable projects and corporate strategy success. Ft. Lauderdale, FL: J. Ross Pub.

Fairhurst, D. S. (2015). Using Excel for business analysis: A guide to financial modelling fundamentals.