1) The advantages of storing data in a relational database include which of the following: Option A - Help in enforcing business rules

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1) The advantages of storing data in a relational database include which of the following:

Option A - Help in enforcing business rules.
Option B - Increased information redundancy.
Option C - Integrating business processes.

2. Which attribute is required to exist in each table of a relational database and serves as the "unique identifier" for each record in a table?

3. The metadata that describes each attribute in a database is which of the following?

4. Why is Supplier ID considered to be a primary key for a Supplier table?

5. The Problems 2-1 to 2-7 correspond to the College Scorecard data. You should be able to answer each question by just looking at the data dictionary (CollegeScorecard_DataDictionary.pdf), but if you would like to use the raw data, feel free to do so (CollegeScorecard_RawData.txt).

The following problem corresponds to the College Scorecard data. For more information, you can reference the data dictionary (CollegeScorecard_DataDictionary.pdf). Which attributes from the College Scorecard data would you need to compare cost of attendance across types of institutions (public, private non-profit, or private for-profit)?

Select "Yes" for each attribute that is necessary for this analysis, and "No" for the attributes that you would not need to include.

 

Possible Attributes Necessary? UNIT ID – a unique identifier for the institution   INSTNM – Institution name   PCTFLOAN – Percent of all federal undergraduates receiving a federal student loan   CONTROL – 1 = Public. 2 = Private nonprofit. 3 = Private for-profit   TUITFTE – Net tuition revenue per full-time equivalent student   RET_FT4 – First-time, full-time student retention rate at four-year institutions   COSTT4_A - Average cost of attendance

 

6. The following problem corresponds to the College Scorecard data. For more information, you can reference the data dictionary (CollegeScorecard_DataDictionary.pdf). Which attributes from the College Scorecard data would you need to compare SAT scores across types of institutions (public, private non-profit, or private for-profit)?
 

Select "Yes" for each attribute that is necessary for this analysis, and "No" for the attributes that you would not need to include.

 

 

	Possible Attributes Necessary? COSTT4_A - Average cost of attendance   INSTNM – Institution name   CONTROL – 1 = Public. 2 = Private nonprofit. 3 = Private for-profit   SAT_AVG – average equivalent SAT of students admitted   UNIT ID – a unique identifier for the institution

 

7. The following problem corresponds to the College Scorecard data. For more information, you can reference the data dictionary (CollegeScorecard_DataDictionary.pdf). Which attributes from the College Scorecard data would you need to compare levels of diversity across types of institutions (public, private non-profit, or private for-profit)?

Select "Yes" for each attribute that is necessary for this analysis, and "No" for the attributes that you would not need to include.

 

Possible Attributes Necessary? INSTNM – Institution name   CONTROL – 1 = Public. 2 = Private nonprofit. 3 = Private for-profit   UNIT ID – a unique identifier for the institution   UGDS_2MOR – total share of enrollment of undergraduates who are two or more races   UGDS_NRA – total share of enrollment of undergraduates who are non-resident aliens   SAT_AVG – average equivalent SAT of students admitted   UGDS_HISP – total share of enrollment of undergraduates who are Hispanic   UGDS_BLACK – total share of enrollment of undergraduates who are black   UGDS_ASIAN – total share of enrollment of undergraduates who are Asian   UGDS_AIAN – total share of enrollment of undergraduates who are American Indian/Alaska Native   UGDS_UNKN – total share of enrollment of undergraduates whose race is unknown   UGDS_WHITE – total share of enrollment of undergraduates who are white   UGDS_NHPI – total share of enrollment of undergraduates who are Native Hawaiian/Pacific Islander

 

8. The following problem corresponds to the College Scorecard data. For more information, you can reference the data dictionary (CollegeScorecard_DataDictionary.pdf). Which attributes from the College Scorecard data would you need to compare completion rate across types of institutions (public, private non-profit, or private for-profit)?

Select "Yes" for each attribute that is necessary for this analysis, and "No" for the attributes that you would not need to include.

 

Possible Attributes Necessary? CONTROL – 1 = Public. 2 = Private nonprofit. 3 = Private for-profit   ADM_RATE – admission rate   STABBR – State postcode   C150_4 – Completion rate for first-time, full-time students at four-year institutions (6 year)   PFTFAC – Proportion of faculty that is full-time   PCTPELL – Percentage of undergraduates who receive a Pell Grant   RET_FT4 – First-time, full-time student retention rate at four-year institutions   UNIT ID – a unique identifier for the institution

 

9. The following problem corresponds to the College Scorecard data. For more information, you can reference the data dictionary (CollegeScorecard_DataDictionary.pdf). Which attributes from the College Scorecard data would you need to compare percentage of students who receive federal loans at universities above and below the median cost of attendance across all institutions (public, private non-profit, or private for-profit)?

Select "Yes" for each attribute that is necessary for this analysis, and "No" for the attributes that you would not need to include.

 

Possible Attributes Necessary? UNIT ID – a unique identifier for the institution   TUITFTE – Net tuition revenue per full-time equivalent student   CONTROL – 1 = Public. 2 = Private nonprofit. 3 = Private for-profit   COSTT4_A – Average cost of attendance   PCTPELL – Percentage of undergraduates who receive a Pell Grant   RET_FT4 – First-time, full-time student retention rate at four-year institutions   INEXPFTE – Instructional expenditures per full-time equivalent student   PCTFLOAN – Percent of all federal undergraduates receiving a federal student loan

 

10. The following problem corresponds to the College Scorecard data. For more information, you can reference the data dictionary (CollegeScorecard_DataDictionary.pdf). Which attributes from the College Scorecard data would you need to determine if different regions of the country have significantly different costs of attendance?

Select "Yes" for each attribute that is necessary for this analysis, and "No" for the attributes that you would not need to include.

 

 

	Possible Attributes Necessary? PPTUG_EF – share of undergraduate degree/certificate-seeking students who are part-time   PCTPELL – Percentage of undergraduates who receive a Pell Grant   UNITID – a unique identifier for the institution   STABBR – State postcode   INEXPFTE – Instructional expenditures per full-time equivalent student   COSTT4_A – Average cost of attendance

 

11. NOTE: Throughout this lab, every time a screenshot is requested, use your computer's screenshot tool, and paste each screenshot to the same Word document. Label each screenshot in accordance to the step in the lab. This document with all of the screenshots included should be uploaded through Connect as a Word or PDF document when you have reached the final step of the lab.

 

In this lab, you will:

 

Part 1: Identify appropriate questions and develop a hypothesis for each question.

Part 2: Master your data and prepare it for analysis in Excel.

Part 3: Perform an analysis using PivotTables.

Part 4: Address and refine your results.

Part 5: Communicate your findings.

 

Refer to Chapter 2 pages 59 through 67 for instructions and steps for each of the lab parts. After completing Lab parts 1 through 5, complete the following requirements.

 

Software needed

• Excel
• Access
• Screen capture tool (Windows: Snipping Tool; Mac: Cmd+Shift+4)

 

Data

• Sláinte dataset

 

After you have created your PivotTable, you should see columns for four months (January, February, March, and April), as well as a Grand Total field. Input the total quantity (sum) of each product sold from the Grand Total field below.

 

 

	Item Sum of Sales Imperial IPA 42 Imperial Stout 166 IPA 171 Pale Ale 81 Stout 118 Wheat 86

 

 

13. In this lab, you will:

 

Part 1: Identify appropriate questions and develop a hypothesis for each question.

Part 2: Master your data and prepare it for analysis in Excel.

Part 3: Perform an analysis using PivotTables.

Part 4: Address and refine your results.

Part 5: Communicate your findings.

 

Refer to Chapter 2 pages 59 through 67 for instructions and steps for each of the lab parts. After completing Lab parts 1 through 5, complete the following requirements.

 

Software needed

• Excel
• Access
• Screen capture tool (Windows: Snipping Tool; Mac: Cmd+Shift+4)

 

Data

• Sláinte dataset

14. In this lab you will:

• Extract data into a text editor and transform it into structured, ready-to-analyze data in Excel.

 

Refer to Chapter 2 pages 59 through 67 for instructions and steps for each of the lab parts. After completing Lab parts 1 and 2, complete the following requirements.

 

Software needed

• Text Editor (Windows: Notepad; Mac: TextEdit)
• Excel
• Screen capture tool (Windows: Snipping Tool; Mac: Cmd+Shift+4)

 

Data Summary

 

The data used are a subset of the College Scorecard dataset that is provided by the U.S. Department of Education. These data provide federal financial aid and earnings information, insights into the performance of schools eligible to receive federal financial aid, and the outcomes of students at those schools. You can learn more about how the data are used and view the raw data yourself at https://collegescorecard.ed.gov/data/. However, for this lab, you should use the text file provided to you.

 

Data:

• CollegeScorecard Datasets: CollegeScorecard_RawData

15. In this lab you will:

• Extract data into a text editor and transform it into structured, ready-to-analyze data in Excel.

 

Refer to Chapter 2 pages 59 through 67 for instructions and steps for each of the lab parts. After completing Lab parts 1 and 2, complete the following requirements.

 

Software needed

• Text Editor (Windows: Notepad; Mac: TextEdit)
• Excel
• Screen capture tool (Windows: Snipping Tool; Mac: Cmd+Shift+4)

 

Data Summary

 

The data used are a subset of the College Scorecard dataset that is provided by the U.S. Department of Education. These data provide federal financial aid and earnings information, insights into the performance of schools eligible to receive federal financial aid, and the outcomes of students at those schools. You can learn more about how the data are used and view the raw data yourself at https://collegescorecard.ed.gov/data/. However, for this lab, you should use the text file provided to you.

 

Data:

• CollegeScorecard Datasets: CollegeScorecard_RawData

 

In the checksums, you validated that the average SAT score for all of the records is 1,059.07. When we work with the data more rigorously, several tests will require us to transform NULL values. If you were to transform the NULL SAT values into 0, what would happen to the average (would it stay the same, decrease, or increase)?

16. In this lab you will:

• Extract data into a text editor and transform it into structured, ready-to-analyze data in Excel.

 

Refer to Chapter 2 pages 59 through 67 for instructions and steps for each of the lab parts. After completing Lab parts 1 and 2, complete the following requirements.

 

Software needed

• Text Editor (Windows: Notepad; Mac: TextEdit)
• Excel
• Screen capture tool (Windows: Snipping Tool; Mac: Cmd+Shift+4)

 

Data Summary

 

The data used are a subset of the College Scorecard dataset that is provided by the U.S. Department of Education. These data provide federal financial aid and earnings information, insights into the performance of schools eligible to receive federal financial aid, and the outcomes of students at those schools. You can learn more about how the data are used and view the raw data yourself at https://collegescorecard.ed.gov/data/. However, for this lab, you should use the text file provided to you.

 

Data:

• CollegeScorecard Datasets: CollegeScorecard_RawData

 

How would that change to the average impact the way you would interpret the data?

17. In this lab you will:

• Extract data into a text editor and transform it into structured, ready-to-analyze data in Excel.

 

Refer to Chapter 2 pages 59 through 67 for instructions and steps for each of the lab parts. After completing Lab parts 1 and 2, complete the following requirements.

 

Software needed

• Text Editor (Windows: Notepad; Mac: TextEdit)
• Excel
• Screen capture tool (Windows: Snipping Tool; Mac: Cmd+Shift+4)

 

Data Summary

 

The data used are a subset of the College Scorecard dataset that is provided by the U.S. Department of Education. These data provide federal financial aid and earnings information, insights into the performance of schools eligible to receive federal financial aid, and the outcomes of students at those schools. You can learn more about how the data are used and view the raw data yourself at https://collegescorecard.ed.gov/data/. However, for this lab, you should use the text file provided to you.

 

Data:

• CollegeScorecard Datasets: CollegeScorecard_RawData

18. In this lab, you will:

• Create an ERD (entity-relationship diagram), which provides some quick information on the data that’s provided in the database. In one diagram, you can view all tables to see the entire database, or you can pick just the two you're working with to focus in on those attributes.

 

Refer to Chapter 2 pages 59 through 67 for instructions and steps for each of the lab parts. After completing Lab parts 1 through 4, complete the following requirements.

 

Software needed

• Microsoft SQL Server Management Studio (available on the VMWare at the University of Arkansas)

 

Data

The data for this lab and all other Dillard's labs must be accessed through the University of Arkansas Remote Desktop. Documentation on how to connect and log in to the remote desktop can be found below by operating system. Contact your course instructor for the specific login credentials. The 2016 Dillard's data cover all transaction over the period 1/1/2014 to 10/17/2016.

• Logging into UARK Remote Desktop - Windows 10
• Logging into UARK Remote Desktop - Mac
• Logging into UARK Remote Desktop - Windows Legacy (versions 8 and previous)

 

Of the keys listed below, select the options that are primary keys for the TRANSACT and SKU tables. (You may select more than one answer. Single click the box with the question mark to produce a check mark for a correct answer and double click the box with the question mark to empty the box for a wrong answer. Any boxes left with a question mark will be automatically graded as incorrect.)

19. In this lab, you will:

• Create an ERD (entity-relationship diagram), which provides some quick information on the data that’s provided in the database. In one diagram, you can view all tables to see the entire database, or you can pick just the two you're working with to focus in on those attributes.

 

Refer to Chapter 2 pages 59 through 67 for instructions and steps for each of the lab parts. After completing Lab parts 1 through 4, complete the following requirements.

 

Software needed

• Microsoft SQL Server Management Studio (available on the VMWare at the University of Arkansas)

 

Data

The data for this lab and all other Dillard's labs must be accessed through the University of Arkansas Remote Desktop. Documentation on how to connect and log in to the remote desktop can be found below by operating system. Contact your course instructor for the specific login credentials. The 2016 Dillard's data cover all transaction over the period 1/1/2014 to 10/17/2016.

• Logging into UARK Remote Desktop - Windows 10
• Logging into UARK Remote Desktop - Mac
• Logging into UARK Remote Desktop - Windows Legacy (versions 8 and previous)

 

Which attribute should be connected in order to relate the SKU table to the TRANSACT table?