Signature Assignment: Consumer Food Case Study

Signature Assignment: Consumer Food


Signature Assignment – Consumer Food Case Study

Consumer goods are physical goods with physical entities, sense they can be touched by tangible items such as automobiles, furniture, household appliances, etc. They are goods which are projected for everyday cloistered consumption. The longevity and the often higher cost of durable goods usually cause consumers to postpone expenditures on them, which makes durables the most volatile (or cost-dependent) component of consumption. They cover a huge product assortment including food and non-food categories in order to meet consumer demand. They are additionally classified in dissolute moving consumer goods (FMCG) and unhurried moving consumer goods (SMCG). The definitions are based on how fast products are sold to the customer, a determining factor in the rotation of goods (“Consumer Goods & Fmcg”, 2017). The goal is to interpret the information given in order to find the results based by performing hypothesis tests. Many steps are involved in Hypothesis Testing (Black, 2017). The data will be broken down into four parts; Preliminary Analysis, Examination of Descriptive Statistics, Examination of Inferential Statistics, and finally the Conclusion/Recommendation (University of Phoenix, 2017).

Part 1 – Preliminary Analysis

The reason for the case study is to determine the results of the consumer food spending habit of the four regions throughout the country. 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 objectives. First, is to understand 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 purpose 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 objective is to conduct a different one-way, ANOVA, for each three dependent variables. All the data’s are revealed 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 used for this analysis, and all the other regions as well, as they are for actual expenditure levels. The level of extent would be a ratio variable to decide if the question in hand would be shown as a pecuniary variable.

Regions Location
1 (Northeast) 1 (Metro)
2 (Midwest) 2 (Non-Metro)
3 (South)  
4 (North)  

Part 2 – Descriptive Statistics

Descriptive statistics is a branch of statistics that aims at describing a number of features of data usually involved in a study. The main purpose of descriptive statistics is to provide a brief summary of the samples and the measures done on a particular study. Coupled with a number of graphics analysis, descriptive statistics form a major component of almost all quantitative data analysis. Descriptive statistics are quite different from inferential statistics. Basically, descriptive statistics is about describing what the data you have shown. For inferential statistics, you are trying to come up with a conclusion drawing from the data you have. (Descriptive Statistics, n.d). The data displayed 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.

Annual Food Spending

Annual Household Income

Non-Mortgage Household Debt

Part 3 – Inferential Analysis

Test 1

  • In this part of the case analysis, in order gather a greater understanding of the annual food spending, I will run three different hypothesis test against the data provided. The first test is based off the estimate of the average annual food spending in the Midwest region is greater than $8,000. The second test will be conducted to find out if there is a difference in average annual food spending between the metro region and outside the metro region. The third test will compare the quantitative factors of annual food spending, annual household income, and non-mortgage household debt by regions to determine if there are any significant outcomes. These conclusions will help give directions as to what actions may need to be taken.

H0: µ = 8000

  • To establish whether the average annual food spending for households in the Midwest region of U.S. is more than $8,000, data was sorted according to regions in order to obtain the descriptive statistics for the annual household expenditure in this region. A One Sample Z Test (Black, 2017) was run to test the null hypothesis the average household spending in Midwest region is equal to $8,000, against the alternative hypothesis that this average was greater than $8,000 i.e.
  • Test Hypothesis:

H1: µ > 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  

Test 2

A Two Sample Z Test (Black, 2017) was run to test the null hypothesis that. There is no significant difference between household expenditure in metro and outside metro against the alternative hypothesis that there is a significant difference on expenditure between the two locations i.e

H0: µ metro = µ outside metro

H1: µ metro ≠ µ 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  

Test 3

To test whether each of the 3 variables are significantly affected by regional differences, there will be a comparison of each of the data classifications among the four different regional areas. This will be conducted by calculating a One-way Analysis of Variance or ANOVA (Black, 2017) for each variable to test the null Hypothesis that regional means were equal, against the null hypothesis that regional means were not equal.

Test Hypothesis:

H0: µ NE = µ MW = µ South = µ West

H1: µ NE ≠ µ MW ≠ µ South ≠ µ West

Test Analysis:

The average household expenditure observed in metro location was found to be $9,436 with standard deviation $3,244.49 while the Average household expenditure observed outside metro is $8261 with a standard deviation $2,811.50. The z score of the test is 2.719835 with a p value=0.006531, at 0.01 the null hypothesis was therefore rejected hence conclusion that household expenditure is significantly different on the two locations.

The mean household spending observed in Midwest region was $8,660 with a standard deviation $2,334.44. This favors the perception that the average expenditure is above $8,000; I attach probability in this, in order to determine whether the observations were by chance. Since the observed sample size is greater than 30 Central Limit Theorem (Black, 2017) I assumed to be in effect. A z score z = 1.895666 was obtained, with a p value = 0.029002, at 0.01 level, the null hypothesis.

For the ANOVA tables, the use of the data from Annual Food Spending produced then obtain a value for F, the test statistic named in honor Sir Ronald Fisher (Frost, 2016). Since F = 3.019608 with a p value = 0.030955, there is no significant difference on mean expenditure based on the regions. The annual Household income groups have fisher value 2.599594 with p value 0.053419 again income is not significantly different across groups. Non-mortgage household debt produced an F value of 5.717824 with p value 0.000902, therefore, I can conclude that non-mortgage household debt significantly differs between the four regions.

Part 4 Conclusion/Recommendations

It was found that mean annual household expenditure in Midwest region was not significantly different from $8,000, though the raw calculations produced a mean greater than $8,000 it can be concluded that the perception that the population mean was greater than $8,000 can only be attributed to chance.

Based on these results, it is recommended that the prices on commodities be fairly controlled to protect residents from being exploited for large profit gains. The review of hardship allowances and salaries for persons in these areas is also recommended to implement, and maintain equality between the two areas. Additionally, the test conducted to determine whether annual household spending is significantly different from metro area versus non-metro areas found that there were significant differences. The results from that test affirm that cost of living is more expensive in a metro area than that of non-metro areas.

Performing the ANOVA on each location for the three variables identified that the mean annual spending was not significantly different between the four regions. This suggest that the cost of living by region are similar. This information can be useful to anyone who is afforded the opportunity to obtain different jobs to significantly maximize one’s annual income.

It was also found that annual household income is significantly the same in the four regions. Therefore, a recommendation should be made to chain business owners or suppliers to price their products the same in these regions, since having the same annual household income indicates that people living in in these are likely to be indifferent in terms of spending. Non-mortgage household debts are found to be significantly different in the four regions.


Black, K. (2017). Business Statistics for Contemporary Decision Making (6th ed.). Hoboken, NJ: John Wiley & Sons.

Consumer Goods & FMCG. (2017). Retrieved from

Descriptive Statistics. (n.d). Retrieved from

Frost, J. (2016, May 18). Understanding Analysis of Variance (ANOVA) and the F-test. Retrieved from