Statistics
for Business
Every
day organizations collect data from multiple sources such as sales reports,
social media clicks, invoices, and productivity reports. Companies, managers,
and employees have access to volumes of data, but many do not know how to apply
that information in a useful way (Forbes, 2016). Data analysis skills are essential for
numerous job positions and departments from management to marketing. Data
analysis can help managers make flow charts for productivity. Marketers can use
data to create effective campaigns in the most suitable markets. Entrepreneurs
may use analytics when choosing which market to invest in. To make wise
decisions, these individuals must know how to test data sets for differences,
correlations, strengths, and anomalies. However, one must learn terms to describe
the data, how to make inferences from the data, formulate applicable
hypotheses, and perform the correct tests. With this fundamental knowledge
combined, professionals can make statistically meaningful inferences beneficial
to their businesses.
Descriptive
Statistics
Descriptive
statistics describe the way data is dispersed on a scatter plot. The mean is
the average midpoint of all the data. The median is middle ordinal score. Mode
is any number that occurs most frequently in the data (Doctoral…, 2013). Excel
can calculate a five-number summary that contains the max, min, and median. Range,
variance, and standard deviation are measures of dispersion which explain how
the data is spread. The range consists of the highest value and lowest value (Clark, Reich,
& Rubenstein, 2000). Variance is the average of squared distances from the
mean. Variance is calculated subtracting the mean from a data point, squaring
the result, then dividing by the number of the total sample. The F-test in
Excel calculates data variances. The standard deviation is the variance
squared. In normal distribution, sixty-eight percent of data points fall within
one standard deviation one both sides of the mean. Ninety-five percent of the
data lies within two standard deviations and 99.7 percent falls within three
standard deviations (Doctoral…,
2013). Descriptive statistics tells a lot
about the structure of data, and at this point one can formulate questions, but
it is not enough information to provide answers.
Inferential
Statistics
One uses inferential statistics to
create hypotheses and test data for significant relationships. With several
excel formulas, one can test sample data against the population data to see if
the data sample is standard or rare (Research, 2016). Excel features several tests used for inferential statistics
like the t-Test, Analysis of Variance (ANOVA), Chi Square test, regression
analysis, and F-tests, but before performing any tests, one must formulate a
hypothesis.
The hypothesis contains the null
hypothesis, alternate hypothesis, p-value, test name, test statistic, direction,
conclusion, and interpretation (Choudhry, 2019). The null hypothesis states the
equality value, which essentially means the sample and any relationships are
likely to occur within the population. An alternate hypothesis states the
sample data is significantly different from the population. If the alternate
hypothesis proves true, then the tested data is different from the population
and the difference is unlikely due to mere chance.
Test
Selection
In order to obtain statistically
strong evidence, one must select the appropriate test for the sample data. The
type of test is dependent on the number of samples, the type of data collected,
and what the tester wants to test. Nominal or ordinal data are tested
differently from interval and ratio data that contains numbers. Data may
include two samples such as male and female data, or it may have only one
sample and multiple variables. A
regression analysis test works with a scatter plot, whereas a Chi-Square test
is suitable for data tables (Dr…, 2012). The 5
–number summary gives the range, median, and quartile information. The
descriptive statistics data is beneficial when analyzing one-sample data. An
employee may use these values when comparing number of items produced on a
production line compared to the number of employees working. The Two Sample
t-Test tests the sample(s) for mean differences. A manager may use this test to
assure that the raises she approves are equal among genders and ages. The F-test compares the variances of two
groups for significant differences. A marketer can use this test to determine
if one region’s consumer habits are significantly different than another area. The
Pearson Correlation test, regression analysis, and multiple regression test
data for correlations. If a business wants to know why their sales of fake
skeletons increases in September and October, but is substantially less all
other months, then the business will collect different samples in hopes to find
a correlation.
Analyzing the
Data
With the results of the test or
formula, one decides if the null is rejected or accepted. Generally the p-value is set at 0.05 if a
ninety-five percent confidence interval is acceptable for the results. The null
hypothesis states that the tested data is equal, while the alternate hypothesis
claims that the sample data is significantly different from the population. If
the p-value is smaller than 0.05, then the data is significantly larger or
smaller than the population. Sometimes the test explains that a relationship
exists and other times it may only explain that an abnormality with a small
percentage of occurring exists in the sample data. Correlations and patterns
are not explained by every test so one must be careful to avoid hasty
inferences. For example the mean and standard deviation explain how far the
data is spread and where the data is centered, but this information does not
explain differences between two data sets. An F-test demonstrates if the
variances of two data sets are significantly different, but that does not
explain anything about the means. Even when a significant correlation is the
result of a linear regression test, the only way to rule out an error in the
sample as the cause is to perform a sample effect size test.
Conclusion
With increased online capabilities
for consumers and businesses, a large amount of data is available. Many are
learning and seeking ways to apply this data in a beneficial way. Statistics
teaches us to analyze and perhaps find value within the data. People may use statistics
to create a monthly or annual budget to pay off loans faster. A couple may poll
their friends to decide what type of food to cater at their wedding.
Professionally, statistics can help streamline production, trim expenses, meet
seasonal demands, or decide if the company should raise the minimum pay to
sixteen dollars per hour. Statistics helps people make wiser decisions.
References
Choudhry, A. (2019). BUS
308 week 2 lecture 2: Examining differences – part 1. [Instructor lecture].
Retrieved from http://login.ashford.edu
Clarke, M. (Writer), Reich, J. M. (Director), &
Rubenstein, B. (Producer). (2000). Measures of variability and relative
standing [Video file]. Retrieved from https://fod.infobase.com/OnDemandEmbed.aspx?token=10255&wID=100753&plt=FOD&loid=0&w=640&h=480&fWidth=660&fHeight=530
Doctoral
Journey, The [Screen name]. (2013, August 26). Descriptive statistics, part 1 [Video file]. Retrieved from https://www.youtube.com/watch?v=8Iklj-lf1fY&feature=youtu.be
Dr
Nic’s Maths and Stats [Screen name]. (2012, February 1). Choosing which statistical test to use – statistics help [Video
file]. Retrieved from
https://www.youtube.com/watch?v=rulIUAN0U3w&feature=youtu.be
Forbes Insights. (2015,
April 9). The role of data & analytics today [Video file]. Retrieved from
https://youtu.be/fxroi4beKhE
Research by Design [Screen name]. (2016, August 23). Inferential statistics – sampling,
probability, and inference (7-5) [Video file]. Retrieved from
https://www.youtube.com/watch?v=EPWH91UcpZw&feature=youtu.be
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