This document analyzes consumer food spending data from 200 households across 4 US regions: Northeast, Midwest, South, and West. Descriptive statistics are provided on annual household income, food spending, and non-mortgage debt. Inferential statistics include hypothesis tests comparing Midwest food spending to $8,000, differences between metro and non-metro areas, and a one-way ANOVA comparing the 3 variables across regions. Key findings include the Midwest average food spending of $8,660 exceeding $8,000, a significant difference in metro vs. non-metro food spending, and non-mortgage debt varying more between regions than other variables. Recommendations include price controls in the Midwest and limiting lending practices where non-mort

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Signature Assignment: Consumer Food
StudentQNT 561
Signature Assignment: Consumer Food
Every day in the United States, consumers continue to make
important decisions on how to spend their money, especially
towards food for their family. These decisions vary from
different areas and regions throughout the country. Would
consumers in the Northeast region make different choices on ho
they spend for food versus other parts, such as the Midwest,
South, and West regions? A survey of 200 statements from the
four various areas of the country was supplied and will be used
to test and verbalize a hypothesis using the given data set forth
with this paper. Each data set used from the four different
regions are their annual food spending, non-mortgage spending,
and annual household income. There are five different samples
from the data set, and they entail either quantitative and
qualitative information. There are four parts to this paper, and
they are preliminary analysis, descriptive statistics, inferential
statistics, and a final recommendation and conclusion.
Part One – Preliminary Analysis
The reason for the case study we are conducting here is to
determine in as plain language as possible the results of the
consumer food spending habit of the four regions throughout the

2. country. Here we will have an emphasis on the Midwest portion
of the country, which is on the excel sheet and shown as region
two data. The objective for this case study analysis will be the
five different variables that comprise the 200 sample dataset
given to us. The main areas around this case study are targeted
around three aims. First is to see if the average for annual food
spending for the Midwest region in the United States is more
than $8,000, by using the 1% significance level, the second aim
is to see if any the differences in household spending levels
between the metro and non-metro areas using the annual food
spending parameters. The third one is to conduct a different
one-way ANOVA for each three dependent variables All the
data are shown by household income, food spending and non-
mortgage debt held by households, are in different independent
regions by identifying the northeast as code 1, Midwest is code
2, south is 3, and the west is code 4 as shown below. These are
quantitative datasets we will be using for this and all the other
regions as well as they are for actual spending levels. The level
of measurement would be a ratio variable to determine if the
question in hand would be shown as a monetary variable.
Regions
Location
1 (Northeast)
1 (Metro)
2 (Midwest)
2 (Non-Metro)
3 (South)
4 (North)
Part Two – Assessment of Descriptive Statistics
There are various ways to get the data you need to help
determine an outcome you are looking for. Descriptive statistics
is one of those and is an essential tool employ because it
describes various descriptions in coefficients and summarizing

3. all the data set forth for this analysis. It is used to streamline
large amounts of data and brought in a way that would make
sense to someone (Descriptive Statistics, 2008). The data
described here will show outliers if present within in the data
sets shown here. All these samples were taken from the 200
sample population that was given. The annual household income
was at $55,552.39. The megastat analysis of descriptive
statistics was used, and all the locations and the regions were
also used. Shown below are the various data set for each of the
200 household samples taken. The mean, median, range,
minimum, maximum, standard deviation, sum, count confidence
level, and the coefficient variance. Also shown here is the five
number summary which shows in a graph how each region
defers in spending levels from one another.

4. For the annual food spending, the mean was $8,966.07, and that
was using the same sample of the 200 samples that were given.

6. The non-mortgage household debt spending mean is at
$15,604.16, using the same sample of 200.
Part 3 – Assessment of Inferential Statistics
This part of the analysis, there will be three test conducted
using inferential stats will show where there are predictions
from the data that was given for this case study. This is a form
of hypothesis testing which uses random variables by observing
the processes and used to infer data. To better understand how
this works, the author would first use a hypothesis test to see if
the average annual food spending in the Midwest area is greater
than $8,000. The following test will show differences in the
metro and non-metro areas of annual food spending. Using this
data can help over a period of time which can develop a trend
over that period on the spending habits of the consumers in the
region to assist the managerial choices for possible future prices
on consumer foods. The third hypothesis test shows to associate
the quantitative influences in the annual household income,
annual food spending and the non-mortgage household debt and
their effects on the various regions immersed in this study.
Annual Food Spending Midwest- Test 1
This first test we used data from the Midwest region to establish
whether the general household annual spending in this region is
greater than $8,000. This test was conducted using the single
sample z test. This is used to determine whether the average in
the Midwest region is equal to $8,000 or maybe the alternate
hypothesis not greater than $8,000. This ended up testing as H0;
µ= 8000, H1 > 8000, results show H1: 8660 > 8000 so this null
hypothesis was accepted due to the x-bar being greater than the

7. mean of 8000 as shown below.
(Question 1 of Part 3)
Metro vs. Non-Metro Households – Test 2
Here we like to see what if any differences there are between
the metro and non-metro areas of the data set that was given. So
the author had to sort through the two areas first to separate the
areas, then form a hypothesis on them. The hypothesis reads as;
H0 ; µ metro = µ non-metro, H1; µ metro ≠ µ non-metro. The
author also used the sample z test to figure the null hypothesis
on this, and the results are shown below. You can see below
that this was rejected as well as the was a significant difference
between the households in the different areas.
(Question 2 of Part 3)

9. Comparison of three variables using ANOVA
This part is now separating the three variables, annual income,
annual spending, and non-mortgage debt to determine whether
there is suggestively influenced by the four areas of the country
where the three variables split into. A one-way, Analysis of
Variance (ANOVA) was completed on each to see the null
hypothesis where the area means are equal, in contrary to the
alternate hypothesis where they are not equal. This can be
written to be as:
H0; µ Northeast = µ Midwest = µ West = µSouth, H1;
µNortheast ≠ µ Midwest ≠ µ West ≠ µ South.
These calculations show the difference between all four areas
for the annual food spending, but the northeast and west areas
have nearly the same food spending numbers that average about
$545,084. Whereas, the south and Midwest are also similar with
an average of $351,522 annually food spending. The household
incomes varied widely and shown to averaging out between
$50,508 to as high as $58,141, but the ANOVA showed the
mean for all four at $55, 117. There are other numerous data
shown below.
Anova: Single Factor Region 1 Northeast

10. Part 4 – Conclusion and Recommendations
The data we used in the Midwest area for the annual household
food spending suggested that the mean data was not extensively
different from the original mean of $8,000. Nonetheless, the
computations we came up with did come up a bit more than the
mean of $8,000. The observation showed us that the probability
could have been a factor in that along with other factors such
as; opening and shutting down of restaurants, household income
levels in the area, produce availability, and possibly the time of
year or season, etc. living in the Midwest area seems to be a bit
more pricier than living outside the area. Along with this
results, it is this author recommendation to have some type of
control on prices of commodities, and this is to provide
protection to the area consumers from being manipulated into
paying higher prices for items that can otherwise be purchased
cheaper in other locals.
The annual food spending mean in the metro area is at $9,436 as
compared to the non-metro area of $8,261. The null hypothesis
was rejected for this area as the p-value is at .0065 and is less
than the µ - .01 that was given to us. This shows that there was
not much of a variance in the metro and non-metro areas for
annual spending, but since these are close values, another
evaluation will be conducted in about six months. To get a
better understanding, it would have been easier with a better
breakdown of the background of consumers, maybe by racial or
religious types since different people would purchase different
types of food and they are usually concentrated in specific
locations in various areas.
The ANOVA performed in each of the locations, identified that
the mean spending was not as different as we thought among the
four regions. That test shows that the cost of living could be
close to the same as well. The information gathered using the

11. ANOVA could also assist people to apply for various jobs and
also bring investors into the region to bring up these areas to
perform better for the community. Now that this shows that the
income levels are close to the same, so suppliers and businesses
should have almost identical pricing strategies to encourage
people to come in and buy their products.
Non-mortgage debt showed during the ANOVA testing to be
much different among the different regions. A recommendation
would be for financial institutions to have some limits on
lending practices when providing loans in the regions having a
high annual mean for non-household debt. Households who have
these larger debts should be encouraged with some incentives to
better their circumstances in the areas that they live.
References
Black, K. (2017). Business Statistics: For Contemporary
Decision Making (9th ed.).
Danvers, MA: Wiley
Descriptive Statistics. (2008). Social Research Methods.
Retrieved from
http://www.socialresearchmethods.net/kb/statdesc.php
University of Phoenix. (2018). Signature Assignment: Consumer
Food Forecasts [Multimedia]. Retrieved from University of
Phoenix, QNT/561 - Applied Business Research & Statistics
website