Importance of statistics in business
Statistics is applied in a number of ways in the business sector, thus playing a significant role in business. The fact that statistics majorly entails making crucial decisions makes it quite handy in the planning function of any business (Evans, 2013). For example, when deciding on the marketing strategy of the business, statistics helps in getting the actual data that can be used to advertise the products of a business.
This is seen in most marketing phrases that will give actual figures on product efficiency or usage. For instance, a phrase that a specific brand of detergent removes 99% stains will definitely make the product sell. This information on efficiency is obtained through statistical analysis.
In addition to this, various types of data obtained from a business can be analysed and interpreted to give a performance review of the business hence being able to plan and predict the future of the business. It can therefore be concluded that statistics is key in the survival and existence of a business.
Difference between a population and sample
The primary exercise in statistics involves data identification and collection. In data collection two types of data sets are involved, which include population and samples. A population entails all the elements of the data on study while a sample will entail a proportion of the items on study picked from the total population.
This is the case because population involves a big number of elements making the study difficult hence the decision to have representatives of the population, which is the sample. A sample is easier to study and analyse thus giving results that will depict the whole population. It therefore is understood that the samples will have characteristics similar to those of the population since they have been selected from the population.
Most often than not, samples will contain fewer elements than the population. However, this depends on various factors among them the sampling method. Samples play an important role in statistics of minimizing chances of biasness since the number of elements involved in a sample is manageable.
Types of charts available in excel and their characteristic data sets
Microsoft Excel comprises of a variety of charts that help in data analysis as well as display. Some of the commonly used chart types include pie charts, line charts, column charts, area charts, surface charts, bar charts, stock charts and area charts just to mention a few. All the aforementioned chart types will be used in accordance to the type of data sets available.
Column charts
These charts entail columns that are vertically oriented whereby the values are in the vertical axis while the horizontal axis contains the categories (Evans, 2013). They are applicable in data that is periodic and will change over a given time such as the sales of a product by a company or within a given region. Column charts can also be used when comparing two different types of data sets such as sales of different brand types.
Bar charts
The only characteristic difference that bar charts have from column charts is their horizontal orientation. In that case, the horizontal axis contains values while categories are plotted on the vertical axis. They are used when comparing characteristics between different data sets.
Pie charts
These will entail graphical representation in a circle/ pie of data sets out of the total sum of items involved. This is computed by getting the percentage representation of each item. These proportions when slotted in the circle, they form pie-like shapes hence the name pie charts.
Line charts
These show trends of particular items that have a continuous type of motion. As such, the horizontal axis will contain periods while the vertical axis contains the values.
Statistical measures used for describing data dispersion
To describe data sufficiently the extent of variation is required, which is given by dispersion measures. Common measures of dispersion include standard deviation, variance, and range. “The range is normally the difference in value between the largest and smallest observation of the data set” (Gravetter and Wallnau, 2000, p. 163).
The standard deviation measures how far an item deviates from the mean value. “It is computed by taking the square root of sum of squared deviation from the mean divided by the number of observations” (Gravetter and Wallnau, 2000, p. 164). When the standard deviation of a data set is squared, variance is obtained which is yet another measure of dispersion.
The concept of correlation and correlation coefficient
In statistics, correlation describes the relationship between variables whereby some will show positive relation while others will not have any degree of relationship. Correlation coefficient on the other hand gives the strength of the relationship between the variables involved (Evans, 2013). It also indicates the direction of the relationship, which could be towards the positive, or negative depending on the computed values.
Interpretation
a. +0.3= this is can be interpreted as a positive correlation coefficient of thirty percent. Therefore, a thirty percent increase in one variable leads to a thirty percent increase in the other variable and vice versa.
b. 0.0= In this case, the two variables have zero relationship, that is there is no correlation between the two variables. As such, a change in value of one variable does not affect the other variable.
c. -0.95= this can be interpreted as a ninety-five percent negative correlation coefficient. This therefore means that a ninety-five percent increase in one variable will result to a similar percentage decrease of the other variable and vice versa.
Reference List
Evans, J. (2013). Statistics, Data Analysis, and Decision Modelling. New York: Prentice Hall.
Gravetter ,F., &, Wallnau, L. (2000). Statistics for the behavioural sciences. Belmont: Wadsworth – Thomson Learning.