STAT200 UMD Descriptive Statistics Analysis Marital Status Case Study
STAT200 UMD Descriptive Statistics Analysis Marital Status Case Study
Statistics, I need this template below complete according to the instructions and data sheet attached, Please and thank you.
Description of Dataset:
The data is a random sample from the US Department of Labor’s 2016 Consumer Expenditure Surveys (CE) and provides information about the composition of households and their annual expenditures (https://www.bls.gov/cex/). It contains information from 30 households, where a survey responder provided the requested information; it is all self-reported information. This dataset contains four socioeconomic variables (whose names start with SE) and four expenditure variables (whose names start with USD).
Description of Variables/Data Dictionary:
The following table is a data dictionary that describes the variables and their locations in this dataset (Note: Dataset is on second page of this document):
|
Variable Name |
Location in Dataset |
Variable Description |
Coding |
|
UniqueID# |
First Column |
Unique number used to identify each survey responder |
Each responder has a unique number from 1-30 |
|
SE-MaritalStatus |
Second Column |
Marital Status of Head of Household |
Not Married/Married |
|
SE-Income |
Third Column |
Annual Household Income |
Amount in US Dollars |
|
SE-AgeHeadHousehold |
Fourth Column |
Age of the Head of Household |
Age in Years |
|
SE-FamilySize |
Fifth Column |
Total Number of People in Family (Both Adults and Children) |
Number of People in Family |
|
USD-AnnualExpenditures |
Sixth Column |
Total Amount of Annual Expenditures |
Amount in US Dollars |
|
USD-Food |
Seventh Column |
Total Amount of Annual Expenditure on Food |
Amount in US Dollars |
|
USD-Housing |
Eighth Column |
Total Amount of Annual Expenditure on Housing |
Amount in US Dollars |
|
USD-Transport |
Ninth Column |
Total Amount of Annual Expenditure on Transportation |
Amount in US Dollars |
How to read the data set: Each row contains information from one household. For instance, the first row of the dataset starting on the next page shows us that for the first household in the sample: the head of household is not married and is 51 years old, has an annual household income is $95,432, has a family size of 1, annual expenditures of $55,120, and spends $7,089 on food, $18,391 on housing, and $115 on transportation.
|
UniqueID# |
SE-MaritalStatus |
SE-Income |
SE-AgeHeadHousehold |
SE-FamilySize |
USD-AnnualExpenditures |
USD-Food |
USD-Housing |
USD-Transport |
|
1 |
Not Married |
95432 |
51 |
1 |
55120 |
7089 |
18391 |
115 |
|
2 |
Not Married |
97469 |
35 |
4 |
54929 |
6900 |
18514 |
145 |
|
3 |
Not Married |
96664 |
53 |
3 |
55558 |
7051 |
18502 |
168 |
|
4 |
Not Married |
96653 |
51 |
4 |
56488 |
6943 |
18838 |
124 |
|
5 |
Not Married |
94867 |
60 |
1 |
55512 |
6935 |
18633 |
131 |
|
6 |
Not Married |
97912 |
49 |
1 |
55704 |
6937 |
18619 |
152 |
|
7 |
Not Married |
96886 |
44 |
2 |
55321 |
6982 |
18312 |
153 |
|
8 |
Not Married |
96244 |
56 |
4 |
56051 |
7073 |
18484 |
141 |
|
9 |
Not Married |
95366 |
48 |
2 |
57082 |
7130 |
18576 |
149 |
|
10 |
Not Married |
96727 |
39 |
2 |
56440 |
7051 |
18376 |
120 |
|
11 |
Not Married |
96697 |
49 |
2 |
56453 |
6971 |
18520 |
136 |
|
12 |
Not Married |
95744 |
52 |
4 |
55963 |
7040 |
18435 |
146 |
|
13 |
Not Married |
96572 |
59 |
2 |
56515 |
7179 |
18648 |
123 |
|
14 |
Not Married |
98717 |
40 |
3 |
56393 |
7036 |
18389 |
114 |
|
15 |
Not Married |
94929 |
59 |
2 |
55247 |
6948 |
18483 |
133 |
|
16 |
Married |
95778 |
42 |
4 |
73323 |
9067 |
22880 |
201 |
|
17 |
Married |
109377 |
48 |
4 |
83530 |
10575 |
23407 |
99 |
|
18 |
Married |
95706 |
52 |
4 |
71597 |
8925 |
22376 |
181 |
|
19 |
Married |
95865 |
46 |
1 |
74789 |
9321 |
22621 |
168 |
|
20 |
Married |
109211 |
42 |
4 |
82503 |
11566 |
22219 |
62 |
|
21 |
Married |
95994 |
55 |
4 |
73404 |
9231 |
22852 |
177 |
|
22 |
Married |
114932 |
44 |
5 |
81186 |
11077 |
26411 |
153 |
|
23 |
Married |
112559 |
39 |
3 |
80934 |
11189 |
25531 |
73 |
|
24 |
Married |
95807 |
56 |
4 |
72949 |
9210 |
23139 |
186 |
|
25 |
Married |
99610 |
36 |
2 |
73550 |
9513 |
27164 |
33 |
|
26 |
Married |
95835 |
54 |
3 |
73092 |
9111 |
23252 |
186 |
|
27 |
Married |
102081 |
42 |
4 |
82331 |
11738 |
23374 |
121 |
|
28 |
Married |
104671 |
41 |
4 |
82786 |
10420 |
22245 |
84 |
|
29 |
Married |
107028 |
46 |
4 |
82816 |
10840 |
25671 |
109 |
|
30 |
Married |
114505 |
36 |
5 |
78325 |
11375 |
26006 |
140 |
