The consumers of the United States make decisions every day on how they will spend their money allocated towards consumer food. However, these food allocations may be different from various regions across the United States. Do consumers in the Midwest make different food spending choices differently than those in the Northeast, South and West regions? The case study presented in this paper will formulate a test hypothesis using the data set provided by the University of Phoenix to determine if the food average annual food spending for a household in the Midwest region Parts of the United States is more than $8,000.00. The data set gathered from the Midwest will include data gathered from the Midwest region in regards to Annual Food Spending per Household, Annual Household Incomes, Non-Mortgage Household Debt Geographic Region of the U.S. of the Household, and the Household Location. The data set contains 5 variables comprised of 200 samples each that entail both qualitative and quantitative data.
The purpose of this case study is to statistically explain the data provided by the University of Phoenix in regards to consumer food spending throughout 4 regions of the United States with emphasis on the Midwest categorized as region 2 within the data set. The objectives of the case study will be tested using 5 variables containing a 200-sample data set. The focus of the case study will be centered around three objectives: 1.) Test to determine if the average annual food spending for a household in the Midwest region of the U.S. is more than $8,000 using a 1% level of significance, 2.) Test to determine if there is a significant difference between households in a metro area and households outside metro areas in annual food spending using
α = 0.01, and 3.) Perform three different one-way ANOVA’s—one for each of the three dependent variables (Annual Food Spending, Annual Household Income, Non-Mortgage Household Debt) using Region as an independent variable with four classification levels (four regions of the U.S.). Find all significant differences by region.
The parameters around the case study will be used to solve the question, “Is the average annual food spending for a household located in the Midwest region of the United States greater than $8000.00”? The population of the case study is comprised of independent variables which are qualitative data such as North East, Mid-West, South, West regions. The breakdown of the qualitative data is coded in U.S. regions such as 1- Northeast, 2 – Midwest, 3 – South, and 4 – West. The location variable of the data set is identified as number 1 only if the household is in a metropolitan area and number 2 only if the household is outside the metropolitan area.
The data set is also made up of quantitative data that will be used as dependent variables in the case study classified as Annual Household Spending per Household, Annual Household Income, and Non-Mortgage Household Debts which will be measured in US currency. The case study data set contains sample data within Annual Food Spending, Annual Household Income per Household, and Non-Mortgage Household Debt characterized by regions and locations. The independent variable in the data set is the qualitative data called regions divided into four parts of the United States. The calculations have shown a variation amongst the regions and in this case study, the level of measurement will be utilized as a ratio variable. The level of measurement as a ratio will be utilized to solve the question based on a monetary variable.
The use of descriptive statistics is very important tools that help describe certain features within data sets. The use of data sets provides the user with complete summaries about sample means and also sample measures. The overall function of descriptive statistics is to describe what data is and what data shows. Descriptive statistics help us to simplify large amounts of data in a sensible way (“Descriptive Statistics”, 2017). The data presented in this section of the case study will provide a descriptive analysis of the data set derived from using the data analysis function using the analysis tool pack within Microsoft Excel 2016. Within the data presented the identification of outliers were present. The first noticeable outlier was found within the data set for Annual Food Spending with a value of 17740 which was out of range from the upper bound of 16974. The second noticeable outlier was discovered in the data set of Annual Household Income with a value of 96132 which was out of the range of the upper bound. The data set Non-Mortgage Household Debt had no identified outliers within the data set. The data tables compiled below presents the descriptive statistics mean, median, mode, range, standard deviation, variance, CV, and five-number summary.
A.) Descriptive Analysis for Consumer Food data: Annual Food Spending
B.) Descriptive Analysis for Consumer Food data: Annual Household Income
C.) Descriptive Analysis for Consumer Food data: Non-Mortgage Household Debt
In this part of the case study, there will be several tests ran using inferential analysis where predictions from the data will be made taken from the samples provided in the case study. The first test will provide if the average annual food spending for a household in the Midwest region of the U.S. is more than $8,000 using the Midwest region data and a 1% level of significance to test this hypothesis. The second test will be conducted testing to determine if there is a significant difference between households in a metro area and households outside metro areas in annual food spending by letting α = 0. The third test will analyze the quantitative factors of annual food spending, annual household income, and non-mortgage household debt by regions to determine if there are any significant findings.