STAT200 Introduction to Statistics

Assignment #1: Descriptive Statistics Data Analysis

Assignment #1: Descriptive Statistics Data Analysis

Before conducting any statistical analyses, researchers develop a plan for how they will obtain and analyze data to answer their research question. The purpose of this assignment is to provide experience working with and executing a descriptive statistics analysis plan using real world data to answer a practical question.

In the first assignment you will execute the descriptive analysis plan using the provided data to create tables and graphs to focus your effort in determining a point estimate of the parameter in question. The second assignment, address Inferential Statistics, expanding the analysis of first assignment to include a confidence interval to the point estimate obtained in Assignment 1.

Assignment Plan

Identify 7 Subjects with similar attributes of your client and determine their average expenditures.Upon request and instructor approval you may use a profile of your choice.

Understanding the task to be addressed:

You are a Financial Advisor and have been asked to perform the following task:

Using provided real world data determine the budgetary annual expenditure your client should anticipate, assuming your client has the following social/economic family profile.

Profile:

Family Income$97,000.00

Marital StatusSingle

Family SizeFour (4)

AgeFifty-Three (53)

Project Approach

Identify 7 Subjects with attributes similar too your clients and determine their average expenditures. The average expenditures will be the point estimate

Similar attributes: salary, marital status, family size and age

Step #1: Review the provided random sample data set.

The data provided is a subsample from the US Department of Labor's Consumer Expenditure Surveys (CE) and provides information about the composition of households and their annual expenditures (https://www.bls.gov/cex/). Detailed information on the sample and variables is included with the data set file; please carefully review this information to familiarize yourself with the data.For your convenience the data set is also provided in an Excel spreadsheet.

Note: This data set will be used for both written assignments.

Step #2: Complete the following Table

Variable

Categorize*

Minimum

Maximum

Mean

Shape **

Outliers***

Income

Age

Family Size

Annual Expenditures

* Abbreviations: Qual. for Qualitative data (categorical), Quant for Quantitative data

**Left, Right, Symmetrical (Sym.)

***Identify outliers by Id number or 0

Step 3:Graphs (Dot plot, Histogram and a Box and Whisker plot) for the following variables

Income vs Marital Status

Annual Expenditures vs Income

Describe at least one important characteristic observed from each group of graphs.

Recommend-lock5stat.com-Statkey for graphing-and MS Snipping tool for pasting .

Step 4Based upon the data provided:Provide 7 or more subjects that best represent your client's social/economic lifestyle.If one or more of the 7 recommended subjects is a 'push', a possible outlier, identify the outlier or outliers by placing a * in the Outlier column.

Id # of Subject Chosen

Marital Status

Income

Family size

Age

Outlier

Step 5Explain in bullet form, in a clear and orderly procedure: the method used (in step format) for determining your recommended list of subjects.

Step 6Point Estimate Recommendation:

Based upon your analysis what do you believe the Annual Expenditures of your Client will be.

Project Data Uniqu e ID # 1 2 3 4 5 6 7 8 9 10 11 12 13 SEMarital Status Not Marrie d Not Marrie d Not Marrie d Not Marrie d Not Marrie d Not Marrie d Not Marrie d Not Marrie d Not Marrie d Not Marrie d Not Marrie d Not Marrie d Not Marrie d SEincome SE-Age Head of Househol d SEFamilySiz e USDAnnualExpenditure s USDHousin g USDElectricit y USDWate r 94929 59 2 55247 18483 1451 546 96621 54 2 55746 18149 1455 540 96664 53 3 55558 18502 1478 553 96522 43 4 56152 18483 1457 537 96697 49 2 56453 18520 1469 545 96727 39 2 56440 18376 1441 542 94867 60 1 55512 18633 1485 523 96690 57 2 56097 18334 1453 535 97469 35 4 54929 18514 1451 565 96886 44 2 55321 18312 1450 523 95744 52 4 55963 18435 1465 555 96572 59 2 56515 18648 1480 552 95366 48 2 57082 18576 1478 538 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Not Marrie d Not Marrie d Marrie d Marrie d Marrie d Marrie d Marrie d Marrie d Marrie d Marrie d Marrie d Marrie d Marrie d Marrie d Marrie d Marrie d Marrie d 97912 49 1 55704 18619 1450 553 96928 43 3 55932 18701 1479 520 42 5 84486 25728 1452 818 56 3 82676 22414 1688 709 37 6 80801 25392 1514 743 29 5 75393 26322 1386 626 30 5 80865 25018 1481 796 39 3 80934 25531 1504 794 97835 30 5 77765 27949 1297 641 10069 3 42 2 73294 26102 1354 597 95385 50 4 74110 22847 1302 684 10035 0 31 5 73771 26853 1405 585 95922 55 3 72228 22996 1326 674 96207 27 3 74620 28491 1310 588 97663 51 3 72971 23150 1320 689 10056 5 18 3 69203 27950 1358 626 97977 44 2 73198 22990 1298 696 11405 1 10662 7 10931 2 10314 4 11119 5 11255 9 Copy Data SEUnique Marital ID # Status SEincome Not Married 94929 1 SE-Age SEUSDUSDUSDUSDHead of FamilySize AnnualExpenditures Housing Electricity Water Household 59 2 55247 18483 1451 546 2 Not Married 96621 54 2 55746 18149 1455 540 3 Not Married 96664 53 3 55558 18502 1478 553 4 Not Married 96522 43 4 56152 18483 1457 537 5 Not Married 96697 49 2 56453 18520 1469 545 6 Not Married 96727 39 2 56440 18376 1441 542 7 Not Married 94867 60 1 55512 18633 1485 523 8 Not Married 96690 57 2 56097 18334 1453 535 9 Not Married 97469 35 4 54929 18514 1451 565 10 Not Married 96886 44 2 55321 18312 1450 523 11 Not Married 95744 52 4 55963 18435 1465 555 12 Not Married 96572 59 2 56515 18648 1480 552 13 Not Married 95366 48 2 57082 18576 1478 538 14 Not Married 97912 49 1 55704 18619 1450 553 15 Not Married 96928 43 3 55932 18701 1479 520 16 Married 114051 42 5 84486 25728 1452 818 17 Married 106627 56 3 82676 22414 1688 709 18 Married 109312 37 6 80801 25392 1514 743 19 Married 103144 29 5 75393 26322 1386 626 20 Married 111195 30 5 80865 25018 1481 796 21 Married 112559 39 3 80934 25531 1504 794 22 Married 97835 30 5 77765 27949 1297 641 23 Married 100693 42 2 73294 26102 1354 597 24 Married 95385 50 4 74110 22847 1302 684 25 Married 100350 31 5 73771 26853 1405 585 26 Married 95922 55 3 72228 22996 1326 674 27 Married 96207 27 3 74620 28491 1310 588 28 Married 97663 51 3 72971 23150 1320 689 29 Married 100565 18 3 69203 27950 1358 626 30 Married 44 2 73198 22990 1298 696 97977 Paired Data Unique ID # SE-Marital Status SE-income Unique ID # SE-income USDAnnualExpenditures 1 Not Married 94929 1 94929 55247 2 Not Married 96621 2 96621 55746 3 Not Married 96664 3 96664 55558 4 Not Married 96522 4 96522 56152 5 Not Married 96697 5 96697 56453 6 Not Married 96727 6 96727 56440 7 Not Married 94867 7 94867 55512 8 Not Married 96690 8 96690 56097 9 Not Married 97469 9 97469 54929 10 Not Married 96886 10 96886 55321 11 Not Married 95744 11 95744 55963 12 Not Married 96572 12 96572 56515 13 Not Married 95366 13 95366 57082 14 Not Married 97912 14 97912 55704 15 Not Married 96928 15 96928 55932 16 Married 114051 16 114051 84486 17 Married 106627 17 106627 82676 18 Married 109312 18 109312 80801 19 Married 103144 19 103144 75393 20 Married 111195 20 111195 80865 21 Married 112559 21 112559 80934 22 Married 97835 22 97835 77765 23 Married 100693 23 100693 73294 24 Married 95385 24 95385 74110 25 Married 100350 25 100350 73771 26 Married 95922 26 95922 72228 27 Married 96207 27 96207 74620 28 Married 97663 28 97663 72971 29 Married 100565 29 100565 69203 30 Married 97977 30 97977 73198