Statistics Essay Example: Consumer Food in the US

Preliminary analysis

The primary objective of this report is to analyze consumer food in the US by getting reliable and scientifically founded data on the annual food spending, yearly household income, non-mortgage household debt, region and location and other factors of living standards in the respective areas. The above-listed variables lie in the consumer food database. Also, another primary purpose is to determine if the average annual food spending for the household located in the Midwest region of the United States is greater than 8000 using significance level 0.01.

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Introduction

The targeted population in this study is the united states as a whole. In the recent reports done by analysts, it has been found that most of the American consumers try to eat healthier foods on a daily basis showing that they are fully aware of their way of eating. While the consumers become more conscious of what they eat, they have decided to look to other options which include the glutton free and vegetarian options. In this population, main plant-based proteins are considered to be healthy. It is possible that they could be the minority group regarding the numbers which is a factor that can influence the continues wellness of the consumers in the food culture. The consumers, in this case, have been creating the way in ecstasy and knowledge with the consumers who are hungry for guidance and direction. Being shoppers, they no longer know about condition management, i.e. reducing cholesterol or blood pressure but instead have narrowed their concentration to real quality food, fresh and processed foods, drinks and snugs. Due to the need for the continued wellbeing and healthy living, the consumers have been putting across the wellness beings for years. In the current era and even since time immemorial, food has always been the essential cultural manifestation that if within us since we have to eat.

In this research, the sample under study from the population was taken from 200 different households take from various regions and locations in the parts of the United States. This sample was chosen as a representation of the entire US population which was used to analyze their annual food spending per household. The analysis obtained from the sample statistics will ease on formulating an appropriate hypothesis concerning the population. The variables under discussion, in this case, include; the annual food spending per household, the annual household income, the non-mortgage household debt, and the geographic region in the US. The data in the study include 1 which represents the North East, 2 represent the Midwest, 3 represents the South, and finally, 4 is the region in the west. For the variable locations data, 1 indicates the household in the metropolitan area and 2 for those households outside the metro area. The data given is quantitative as it shows the measures of values and is in the number format. The numeric quantitative data is the one which will be used to evaluate the various measures of central tendencies. The levels of measurements used for the data are the nominal, ordinal and interval. These levels of measurements will be used to indicate the appropriateness of the statistical analysis.

Descriptive statistics

The use of descriptive statistics gives a basis of describing specific parameters within a given dataset. These datasets provide one with a summary of the sample measures, and also the sample mean. The descriptive statistics enables us to describe vast amounts of data more sensibly. From the data provided in the consumer food statistics, the following descriptive statistics were evaluated; mean, median, mode, range, standard deviation, variance, CV, and five-number summary for the respective variables(Miles, 2001). The results were as recorded in the table below

The range of the data can be obtained by subtracting the smallest value from the largest value. The range was evaluated for all the data and was found out to be as follows; annual food spending $15,153; annual household income $74,485; the non-mortgage household debt $36,374. these were the values of the ranges obtained from the statistical analysis. The range is found to be highest for the annual household income. Data outliers is also obtainable from the set group of data for every variable. this is going to give the inconsistencies that exist within the data. The main outliers are from the quartile and interquartile range. The mean and medians above also qualify to be the outliers of the given data.

Inferential Statistics

From this study, I think the variables in question the annual food spending and the annual household income have a direct proportionality. This can be stated from the analyzed statistical results above. The hypothesis in this study is to determine three main tests. First is to determine if the average food spending for the household in the Midwest region will be more than $8,000. The second test will be to determine if the significant difference between households in metropolitan area and households outside the metro area in annual food spending letting a = 0, the final test will give a detailed analysis of quantitative factors of the yearly food spending, annual household income and the non-mortgage household debt by the regions and checking if there are any significant findings(Chestnut, 2005). for the performance of the first test, the data was organized according to the region 2 (Midwest region) concerning the annual household food spending data. The null hypothesis under the test was the average spending in the Midwest region is equal to $8,000, and the alternative hypothesis was the average being greater than $8,000. From the test performed, the null hypothesis was rejected.

Test Hypothesis:

H0: = 8000

H1: > 8000 = H1: 8660 > 8000

Test Statistics:

z-Test: Two Sample for Means Annual Food Spending Test

Mean 8659.688889 8000

Known Variance 5449631 5449631

Observations 45 45

Hypothesized Mean Difference 0 z 1.34043846 P(Z<=z) one-tail 0.09005142 z Critical one-tail 2.326347874 P(Z<=z) two-tail 0.180102839 z Critical two-tail 2.575829304 For the second test, the test performed was the Z-test used to test the null hypothesis. From the test, it was found out that there was a significant difference between the metro area house and those outside the metro area. In this case, the test rejected the null hypothesis.

Test Hypothesis:

H0: metro = outside metro

H1: metro outside metro

Test Statistics:

t-Test: Two-Sample Assuming Unequal Variances 1 Inside Metro 2 Outside Metro

Mean 9435.933333 8261.2625

Variance 10526695.37 7904552.956

Observations 120 80

Hypothesized Mean Difference 0 df 185 t Stat 2.719835073 P(T<=t) one-tail 0.003576947 t Critical one-tail 2.34667322 P(T<=t) two-tail 0.007153893 t Critical two-tail 2.602665303

The ANOVA calculations display a difference amongst all four regions for Annual Food Spending, but the Northeast Region 1 and West Region 4 have similar annual food spending averaging at $545,084.50. Region Midwest 2 and Region South 3 Annual Food Spending were identical as well with an average of $351,522.00 annually for food spending. The Annual Household Income per Household ranged from a low of $50,508.15 to a high of $58,141.72. However, the ANOVA calculations compared provided an average among all four regions to be $55,117.60. The data from the case study also observed that Non-Mortgage Household Debt appeared not to be a major factor amongst the areas due to the amount of Debt seen in the four different regions. Data showed an Annual Non-Mortgage Debt in Northeast (Region 1) having $824,556.30, Midwest (Region 2) calculating to be $575,322.10, South (Region 3) being $748,678.20, and the West (Region 4) with a $971,274.90 annual debt other than mortgages. The Annual Non-Mortgage Debt calculations have more emphasis on consumer spending other than consumer food spending. The data tables below represent three different one-way ANOVA calculations for the three data sets of dependent variables which will be used as the quantitative data.

Conclusion

The mean Annual Household Food Spending in the Midwest region did not drastically appear to be significantly different from $8,000. However, the calculations did calculate a mean greater than $8,000 which could predict that the difference in estimates could have happened by chance based on what seasons, available produce, opening, and closing of restaurants, household incomes, etc. The Annual Household Spending test is for inside the metro location calculated to be significantly different from its location outside the metro (Chestnut, 2005). Therefore, the life of living inside the city r metro location is more expensive rather than locations outside the city. The cost of living is skyrocketed based on availability and convenience. Residents moving to the metro area can also be advised to prepare for more expenditure than before; prospective investors can also be encouraged to make for extra expenditure

References

Chestnut, L. G., & Mills, D. M. (2005). A fresh look at the benefits and costs of the US acid rain program. Journal of Environmental Management, 77(3), 252-266.

Miles, J., & Shelving, M. (2001). Applying regression and correlation: A guide for students and researchers. Sage.