Scenario: You work in the analytics department for a consulting
company. Your assignment is to analyze the Consumer Food database:Provide a detailed statistical report including the following:
Explain the context of the case
Provide a research foundation for the topic
Present graphs
Explain outliers
Prepare calculations
Conduct hypotheses tests
Discuss inferences you have made from the results Part 1 – Preliminary Analysis (3-4 paragraphs)Generally, as a statistics consultant, you will be given a problem
and data. At times, you may have to gather additional data. For this
assignment, assume all the data is already gathered for you.State the objective:
What are the questions you are trying to address?
Describe the population in the study clearly and in sufficient detail:
What is the sample?
Discuss the types of data and variables:
Are the data quantitative or qualitative?
What are levels of measurement for the data?
Part 2 – Descriptive Statistics (3-4 paragraphs) Examine the given data.Present the descriptive statistics (mean, median, mode, range, standard deviation, variance, CV, and five-number summary).Identify any outliers in the data.Present any graphs or charts you think are appropriate for the data.Note: Ideally, we want to assess the conditions of normality
too. However, for the purpose of this exercise, assume data is drawn
from normal populations. Part 3 – Inferential Statistics (2-3 paragraphs)Use the Part 3: Inferential Statistics document.
Create (formulate) hypotheses
Run formal hypothesis tests
Make decisions. Your decisions should be stated in non-technical terms.
Hint: A final conclusion saying
“reject the null hypothesis” by itself without explanation is basically
worthless to those who hired you. Similarly, stating the conclusion is
false or rejected is not sufficient. Part 4 – Conclusion and Recommendations (1-2 paragraphs)Include the following:
What are your conclusions?
What do you infer from the statistical analysis?
State the interpretations in non-technical terms. What information might lead to a different conclusion?
Are there any variables missing?
What additional information would be valuable to help draw a more certain conclusion?
option_3_consumer_food.docx
option_3_consumer_food.xlsx
option_3_inferential_statistics.docx
Unformatted Attachment Preview
Region: Location:
Annual
Annual
Non
1 = NE 1 = Metro
Food
Househol mortgage 2 = MW
2=
Spending d Income househol 3 = S
4 Outside
($)
($)
d debt ($)
=W
Metro
8909
56697
23180
1
1
5684
35945
7052
1
1
10706
52687
16149
1
1
14112
74041
21839
1
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18866
1
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21899
1
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2
2
Region: Location:
Annual
Annual
Non
1 = NE 1 = Metro
Food
Househol mortgage 2 = MW
2=
Spending d Income househol 3 = S
4 Outside
($)
($)
d debt ($)
=W
Metro
8909
56697
23180
1
1
5684
35945
7052
1
1
10706
52687
16149
1
1
14112
74041
21839
1
1
13855
63182
18866
1
1
15619
79064
21899
1
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15766
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27685
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21407
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19114
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7817
1
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14415
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17295
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16687
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19538
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20165
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4
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2
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2
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2
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2
2
2
2
2
2
Option 3: Consumer Food
1. Suppose you want to 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. Use the Midwest region data and a 1% level of
significance to test this hypothesis. Assume that annual food spending is normally distributed in the
population.
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. Let α = 0.01.
3. The Consumer Food database contains data on Annual Food Spending, Annual Household Income,
and Non-Mortgage Household Debt broken down by Region and Location. Using Region as an
independent variable with four classification levels (four regions of the U.S.), 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). Did you find any significant differences by
region?
…
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