Implementing Statistical measures in SC Railing Company
Types of descriptive statistics for summarizing data
Descriptive statistics are used in summarizing data in the meaningful and useful way. Descriptive analysis deals with the quantitative analysis of data. When using descriptive statistics the research is bound to make conclusions from the data only. It is from this data that he is also able to make conclusions and drive his hypothesis (Evans & Lindsa, 2002). Data is described in two different measures which are; central tendency and measures of variability. Descriptive statistics are presented in form of graphs and tables followed by explanations of the data.
When using measures of central tendency one value is used to describe the center of the data outlined. The modes of measurement thus include mean, median and mode. The mean is basically found by calculating the sum of the data and dividing it with total number of values. For instance, getting a mean sale of ten commodities sold you will add the sum cost of the ten commodities then divide by ten. Mode represents the most occurring figure from the given data. The median is the central point of the data. It is calculated by arranging data into a numerical order then locating the middle value. When dealing with an even list then you have to get the average of two numbers and divide by two.
When summarizing numerical data the researcher can apply descriptive modes of measurements. Median, mean and mode are used in evaluating numerical data. The mode can also be used in analyzing the nominal data (Teng, 2005). For instance, when dealing carrying out a research physical differences like gender and color of the customers, the researcher can use mode in her analysis. Additionally, the mean is the most considered measure when finding central tendency. This is because it considers all the numbers in the data provided. However median is more appropriate when there are extreme values from the data.
Measures of dispersion include the range, variance and standard deviation. Finding range is the simply getting the difference from the highest value and the lowest value. Calculating variance and standard deviation depends on the measures of central tendency. For instance, variance is the distance set by the data from the mean.
Types of inferential statistics in analyzing data
Inferential statistics are used to make conclusions that go beyond the numerical or nominal data available. Inferential statistics can be used to express people’s views on a certain issue. They can also be used to make judgments from an observation of two different groups. Data from a population is deducted to test the hypothesis and derive estimates. In inferential statistics data from observation is believed to be more accurate since sampling is done from a large population (Evans & Lindsa, 2002). Researchers rely on the use of random sampling design to ensure that a population is well represented. Inferential statistics are the most used in carrying out experiments on large populations this is because there it is the only method that can survey a large population through chosen samples. It is from the samples that researchers make conclusions.
The main inferential statistics are from a broad family of statistical models known as the General Linear Model. These methods include the t-test regression analysis, Analysis of Covariance and Analysis of Variance. There are also other methods derived from the inferential statistics like cluster analysis, multidimensional scaling among others. General Linear Model is one of the most effective methods of preparing complex analyses (Breyfogle, 2003). T-test method assess the difference on the mean of two different groups when finding an analysis using the two-group randomized experimental method.
Figure 1.1 comparison of group posttest values
The figure above shows the mean of a control group and treated groups in a study. The main issue of enquiry is whether the means of the two groups are statistically different. The two means are different but indicating medium variability.
Probability or trend analysis in addressing SC Railing Company problem.
The method of trend analysis in data collection is done by relying on past information to make future decisions. The SC Railing Company may make decisions based on the analysis of the past experiences. For instance, an organization may carry out an analysis on the total expenditure from the previous years and from the data they are able to come up with an estimated budget of the year. Probability as a method of data collection is used to collect data from a small sample of the population to represent the whole population (Evans & Lindsa, 2002). This method is effective that would be spent interviewing a large population.
Probability method is essential in SC Railing Company decision making. It can be used to enquire on the employees’ views and feelings on different matters. For instance, when a SC Railing Company wants to identify areas that need to be rectified to improve the well-being of the SC Railing Company, they can perform a survey where a group is chosen to represent the whole population. A survey can also be conducted from the customers and the society neighboring the SC Railing Company (Breyfogle, 2003). This will ensure that the SC Railing Company does will make effective decisions to both the employees and to their customers. Through technological advancement there are sites like the Generate Data and Graph Pad that are used to give simple random sampling from the internet.
Role of linear regression for trend analysis in addressing Company problem.
Linear regression is use to predict analysis which can be very useful to any Company. The main issues examined by linear regression is on whether the predictor variables are predicting outcome variable and which variables are significant in predicting dependent variables. They also try to identify the impact of the predictor variable to the dependent variables (Grover, Jeong, Kettinger & Teng, 2005). Linear regression is used in predicting the future of the SC Railing Company. A SC Railing Company person can be able to predict the possibility of high profit in the future. Linear regression is mostly used where there is no time component. It can also be used to develop a linear regression model that will be able to draw data from the past and extrapolate it in future. This helps the SC Railing Company cope with different economic seasons and prepare well for the future.
Role of time series in addressing business problem
Time series can also be used in predicting the future of a company as well solving different problems. This can be done by the SC Railing Company managers analyzing different seasons for a long period of time then coming up with a comprehensive report on the way forward. Times may change giving inaccurate data but at least the SC Railing Company gets a rough estimation on what to expect as the outcome (Breyfogle, 2003). For instance, a challenge and low seasons in a SC Railing Company can be identified by a research taken to record income for many time series.
Company requires statistical data in making decisions including SC Railing Company. Therefore statistical methods should be implemented in gathering relevant ideas which are effective in solving different challenges. A good business will only require good governance which can only be realized by incorporation of all SC Railing Company stakeholders like the customers, employees and the community at large (Zikmund, Babin, Carr & Griffin, 2013). Consequently, there is need to conduct research on various issues affecting the Company so that you can accommodate all the necessary factors.
Breyfogle III, F. W. (2003). Implementing six sigma: smarter solutions using statistical methods. John Wiley & Sons.
Darlington, R. B., & Hayes, A. F. (2016). Regression analysis and linear models: Concepts, applications, and implementation. Guilford Publications.
Evans, J. R., & Lindsay, W. M. (2002). The management and control of quality (Vol. 5, pp. 115-128). Cincinnati, OH: South-Western.
Grover, V., Jeong, S. R., Kettinger, W. J., & Teng, J. T. (2005). The implementation of SC Railing Company process reengineering. Journal of Management Information Systems, 12(1), 109-144.
Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2013). SC Railing Company research methods. Cengage Learning.
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