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Exp19_Access_Ch05_CapAssessment – Paterson Credit Union 1.1

Exp19_Access_Ch05_CapAssessment – Paterson Credit Union 1.1

Exp19 Access Ch05 CapAssessment Paterson Credit Union 1.1

Access Chapter 5 Capstone Assessment – Paterson Credit Union

  

Project Description:

You work as a database administrator at the Paterson Credit Union. You are modifying a database to add validation, lookup fields, and an input mask. You will also modify queries to take advantage of advanced functions and features.

     

Start   Access. Open the file named Exp19_Access_Ch05_CapAssessment_Paterson_Credit_Union.accdb.   Grader has automatically added your last name to the beginning of the   filename.

 

You   want to make sure that the customer account types are documented and stored   correctly. To do this you will create a table that will list each account   type.
 

  Use Design view to create a new table. Add AccountType   as the first field name, with data type Short Text and field size 10. Ensure AccountType is set as the   primary key. Save the table and name it AccountTypes.   Add three records: Platinum,   Silver, and Gold.   Save and close the table.

 

Now,   you wish to ensure that, when customers are added to your database, the phone   number and account type must be entered. To do this you will set the   PhoneNumber and AccountType fields as required fields.
 

  Open the Customers table in Design view. Set the PhoneNumber and AccountType   fields to Required. Save and close the table.

 

Paterson   Credit Union only offers loans with interest rates between 2.0% and 10.25%.   To ensure that no loans are offered outside of those constraints you will add   a validation rule that will not allow loans outside of that range to the   InterestRate field in the Loans table.
 

  Open the Loans table in Design view. Establish a validation rule for the   InterestRate field that requires the value to be greater than or equal to 2.0 but less than or equal to 10.25. Create validation text for the   InterestRate: Value must be between 2   and 10.25   (no period). Save the table and switch to Datasheet view. Change the   InterestRate in the first record to 1.9.   The validation text appears. Press ESC to restore the original value. Close   the Loans table.

 

You’ve   made the PhoneNumber field required in the Customers table, but now you want   to ensure that phone numbers are entered in a specific format. To do this you   will add an input mask to the PhoneNumber field in the Customers table.
 

  Open the Customers table in Design view. Add a phone number input mask for   the PhoneNumber field, storing the symbols with the data.

 

You   would like to easily add the account type for each customer without typing   anything on your keyboard. To do this you will turn the AccountType field   into a Lookup Wizard using the AccountTypes table, that you recently created,   as the source.
 

  Change the Data Type of the AccountType field to Lookup Wizard. Use the   AccountTypes table for the values in the lookup field, select the AccountType   field from the table, accept the default sort, accept default column widths,   and then accept the default name AccountType.   Save the table. Switch to Datasheet view.

 

Change   the account type to Platinum in the   first record. Close the table.

 

For   ease of use, you would like for users to be able to indicate the minimum loan   amount on which they would like to pull loan information. You will do this by   adding a parameter criterion to the LoanAmount field in the Customer Loans   Parameter query.
 

  Open the Customer Loans Parameter query in Design view. Add criteria for the   Amount field. The user should be prompted to Enter Minimum   Loan Amount (no period). The query should display   all records that have a loan Amount that is greater than or equal to the   value entered as the parameter. Run the query. Enter 250000 when prompted to Enter Minimum Loan   Amount. You should have five results. Ensure that the query results display a   total at the bottom of the Date column, and an average at the bottom of the   Amount column. Save and close the query.

 

You   have noticed that a few of your customers are missing address information. You   would like to address this by creating a query that returns only the   customers that are missing addresses so that you can update that information.   You will complete this by adding a field that indicates whether an address is   missing then adding criteria to that field so that only customers with   missing addresses are returned.
 

  Open the Missing Addresses query in Design view. Add a new column to   determine if a customer does not have an address on file. If the customer’s   Address is null, it should display Missing.   If not, it should display nothing. Name the column AddressPresent. Add criteria of Missing to the column you just created, so   only the customers missing an address display. Move the AddressPresent field   so it appears between PhoneNumber and Address. Run the query. Ensure only   customers with null Address fields display. Save and close the query.

 

For   simplicity, you are now interested in rounding the interest rates for each   loan to the nearest whole number. To do so, you will utilize the Round   function in the Loans by Interest Rate query.
 

  Open the Loans By Interest Rate query in Design view. Create a new column to   round the InterestRate of each Loan to the nearest whole number. Name the   field RoundedRate. Run the query and verify the RoundedRate   column displays whole numbers. Save and close the query.

 

Seeing   what the total and average payments month over month are is important to your   operation. To display this information, you will use the DatePart function to   extract the month from the PaymentDate field then ensure that the query is   grouped by month.
 

  Open the Payment By Month query in Design view. Change the first column so   that instead of grouping by the payment date, you group by the month. Use the   DatePart function to extract the month from the date. Name the column MonthNumber. Group by the MonthNumber field and   display the Sum of the first Total field and the Average of the Average   field. Run the query. The first line should read 2 (as the month,   representing February), with a total of $5,246.51 as the total payments   received and $1,311.63 as the average payment amount. Ensure that the query   results display a total at the bottom of the Total column, and an average at   the bottom of the Average column. Save and close the query.

 

Finally,   you would like to classify the various loans as either high or low priority   for the Credit Union. To do this you will add a column that determines   whether the interest rate for a loan is greater than or equal to 7.9%, as   that is what is considered high priority.
 

  Open the Refinance Candidates query in Design view. This query displays all   adjustable loans in the database. Create a new column to display High Priority for all loans that have an   InterestRate of 7.9% or more, and Low Priority   otherwise. Name the field Priority.   Run the query. Notice customers with the highest interest rate values show a   higher priority. Save and close the query.

 

Save   the database. Close the database, and then exit Access. Submit the database   as directed.

EX19_AC_CH05_GRADER_CAP_AS_Instructions.docx

Grader – Instructions Access 2019 Project

Exp19_Access_Ch05_CapAssessment – Paterson Credit Union 1.1

Project Description:

You work as a database administrator at the Paterson Credit Union. You are modifying a database to add validation, lookup fields, and an input mask. You will also modify queries to take advantage of advanced functions and features.

Steps to Perform:

Step

Instructions

Points Possible

1

Start Access. Open the file named Exp19_Access_Ch05_CapAssessment_Paterson_Credit_Union.accdb. Grader has automatically added your last name to the beginning of the filename.

0

2

You want to make sure that the customer account types are documented and stored correctly. To do this you will create a table that will list each account type. Use Design view to create a new table. Add AccountType as the first field name, with data type Short Text and field size 10. Ensure AccountType is set as the primary key. Save the table and name it AccountTypes. Add three records: Platinum, Silver, and Gold. Save and close the table.

4

3

Now, you wish to ensure that, when customers are added to your database, the phone number and account type must be entered. To do this you will set the PhoneNumber and AccountType fields as required fields. Open the Customers table in Design view. Set the PhoneNumber and AccountType fields to Required. Save and close the table.

6

4

Paterson Credit Union only offers loans with interest rates between 2.0% and 10.25%. To ensure that no loans are offered outside of those constraints you will add a validation rule that will not allow loans outside of that range to the InterestRate field in the Loans table. Open the Loans table in Design view. Establish a validation rule for the InterestRate field that requires the value to be greater than or equal to 2.0 but less than or equal to 10.25. Create validation text for the InterestRate: Value must be between 2 and 10.25 (no period). Save the table and switch to Datasheet view. Change the InterestRate in the first record to 1.9. The validation text appears. Press ESC to restore the original value. Close the Loans table.

8

5

You’ve made the PhoneNumber field required in the Customers table, but now you want to ensure that phone numbers are entered in a specific format. To do this you will add an input mask to the PhoneNumber field in the Customers table. Open the Customers table in Design view. Add a phone number input mask for the PhoneNumber field, storing the symbols with the data.

16

6

You would like to easily add the account type for each customer without typing anything on your keyboard. To do this you will turn the AccountType field into a Lookup Wizard using the AccountTypes table, that you recently created, as the source. Change the Data Type of the AccountType field to Lookup Wizard. Use the AccountTypes table for the values in the lookup field, select the AccountType field from the table, accept the default sort, accept default column widths, and then accept the default name AccountType. Save the table. Switch to Datasheet view.

8

7

Change the account type to Platinum in the first record. Close the table.

2

8

For ease of use, you would like for users to be able to indicate the minimum loan amount on which they would like to pull loan information. You will do this by adding a parameter criterion to the LoanAmount field in the Customer Loans Parameter query. Open the Customer Loans Parameter query in Design view. Add criteria for the Amount field. The user should be prompted to Enter Minimum Loan Amount (no period). The query should display all records that have a loan Amount that is greater than or equal to the value entered as the parameter. Run the query. Enter 250000 when prompted to Enter Minimum Loan Amount. You should have five results. Ensure that the query results display a total at the bottom of the Date column, and an average at the bottom of the Amount column. Save and close the query.

16

9

You have noticed that a few of your customers are missing address information. You would like to address this by creating a query that returns only the customers that are missing addresses so that you can update that information. You will complete this by adding a field that indicates whether an address is missing then adding criteria to that field so that only customers with missing addresses are returned. Open the Missing Addresses query in Design view. Add a new column to determine if a customer does not have an address on file. If the customer’s Address is null, it should display Missing. If not, it should display nothing. Name the column AddressPresent. Add criteria of Missing to the column you just created, so only the customers missing an address display. Move the AddressPresent field so it appears between PhoneNumber and Address. Run the query. Ensure only customers with null Address fields display. Save and close the query.

10

10

For simplicity, you are now interested in rounding the interest rates for each loan to the nearest whole number. To do so, you will utilize the Round function in the Loans by Interest Rate query. Open the Loans By Interest Rate query in Design view. Create a new column to round the InterestRate of each Loan to the nearest whole number. Name the field RoundedRate. Run the query and verify the RoundedRate column displays whole numbers. Save and close the query.

10

11

Seeing what the total and average payments month over month are is important to your operation. To display this information, you will use the DatePart function to extract the month from the PaymentDate field then ensure that the query is grouped by month. Open the Payment By Month query in Design view. Change the first column so that instead of grouping by the payment date, you group by the month. Use the DatePart function to extract the month from the date. Name the column MonthNumber. Group by the MonthNumber field and display the Sum of the first Total field and the Average of the Average field. Run the query. The first line should read 2 (as the month, representing February), with a total of $5,246.51 as the total payments received and $1,311.63 as the average payment amount. Ensure that the query results display a total at the bottom of the Total column, and an average at the bottom of the Average column. Save and close the query.

10

12

Finally, you would like to classify the various loans as either high or low priority for the Credit Union. To do this you will add a column that determines whether the interest rate for a loan is greater than or equal to 7.9%, as that is what is considered high priority. Open the Refinance Candidates query in Design view. This query displays all adjustable loans in the database. Create a new column to display High Priority for all loans that have an InterestRate of 7.9% or more, and Low Priority otherwise. Name the field Priority. Run the query. Notice customers with the highest interest rate values show a higher priority. Save and close the query.

10

13

Save the database. Close the database, and then exit Access. Submit the database as directed.

0

Total Points

100

Created On: 07/11/2019 1 Exp19_Access_Ch05_CapAssessment – Paterson Credit Union 1.1

Papa_Exp19_Access_Ch05_CapAssessment_Paterson_Credit_Union.accdb

CustomerID FirstName LastName Address City State ZipCode PhoneNumber AccountType mSysRowId
1 Virginia Stewart 7245 NW 8 Street Minneapolis MN 55346 6128941511 Gold WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
2 Gina Mullins 5660 NW 175 Terrace Baltimore MD 21224 4107530345 Silver WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
3 Omar Barton 10000 Sample Road Coral Springs FL 33073 3054445555 Platinum WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
4 Melissa Lynch 7500 Reno Road Houston TX 77090 7134273104 Platinum WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
5 Aaron Figueroa 3456 Main Highway Denver CO 80228 3035556666 Silver WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
6 Shannon Abbott 2-2 Murray Street Chapel Hill NC 27515 9199427654 Gold WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
7 Morris Cook 7075423411 Gold WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
8 Krista Williams 9043745660 Gold WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
9 Jessie Richards 5070 Battle Road Decatur GA 30034 3013456556 Silver WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
10 Jesus Sutton 777 NW 67 Avenue Fort Lee NJ 07624 2016643211 Platinum WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
11 Roberta Harmon 409 Cook Road Stoneboro Pa 16137 7245551212 Platinum WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
15 Joyce Byrd 17 Snow Goose Place Altenburg MO 63732 9692598500 Gold WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
16 Arnold Porter 1085 Alana Drive Colorado City AZ 86021 9509375705 Silver WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
17 Esther Moreno 858 Toadstool Road Mayfield UT 84643 9595997109 Platinum WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
18 Alan Adkins 3512243437 Gold WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
19 Perry Sanchez 2028 Prentice Drive Clifton NJ 07012 4249352241 Silver WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
20 Nicholas Fowler 2087 Ondola Lane Dallas SD 57529 8337024885 Platinum WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
21 Yvonne Tucker 1674 Casper Street Van Buren AR 72956 9596058273 Gold WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
22 Nadine Christensen 539 July Creek Street Browntown WI 53522 6006664583 Silver WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
23 Garry Gross 8579824012 Platinum WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
24 Ruben Rodgers 2334 Imperial Circle Hollansburg OH 45332 9987362069 Gold WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
25 Joel Brady 954 Kidron Way Star TX 76880 8223268280 Silver WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
26 Courtney Santos 1018 Eide Terrace Hiram MO 63947 9768057256 Platinum WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
27 Milton Page 1197 Cyclone Terrace Cloverdale IN 46120 9504711359 Gold WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
28 Rufus Gibbs 1403 Maytag Lane Saint Regis MT 59866 2246105693 Silver WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
29 Heather Fuller 900 Midland Circle Lexington OK 73051 5552135251 Platinum WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
30 Charlie Quinn 1513 Valarian Boulevard Nashua NH 03063 3002422114 Gold WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
31 Linda Obrien 9768493226 Silver WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
32 George Stevenson 126 Allison Terrace Sanger CA 93657 6006531035 Platinum WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
33 Johanna Stephens 1351 Stonebridge Circle Danforth IL 60930 8304946646 Gold WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
34 Kate Simmons 1708 Stemp Court Crab Orchard NE 68332 7115293270 Silver WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
35 Neil Warren 1552 Sportsman Way Washington DC 20421 9596469432 Platinum WVoBOJM5oy4q+u24n3kfsQOpUrsVEID/JGzlAuYjpgw=-~Az1hV5R4WpMamBW8M1W6SQ==
LoanID Date Amount InterestRate Term Type CustomerID Adjustable mSysRowId
1 2019-01-15 ¤ 473,500.00 6.07 15 M 4 false w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
2 2019-01-23 ¤ 36,500.00 7.07 5 c 4 true w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
3 2019-01-25 ¤ 11,500.00 5.37 3 C 5 false w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
4 2019-01-31 ¤ 13,500.00 9.63 10 O 4 true w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
5 2019-02-08 ¤ 523,500.00 6.37 30 M 6 true w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
6 2019-02-12 ¤ 12,000.00 7.63 5 O 7 true w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
7 2019-02-15 ¤ 36,500.00 6.37 5 O 8 false w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
8 2019-02-20 ¤ 248,500.00 8.93 30 M 8 false w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
9 2019-02-21 ¤ 6,500.00 10.13 3 O 8 false w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
10 2019-02-28 ¤ 198,500.00 6.87 15 M 1 false w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
11 2019-03-01 ¤ 26,500.00 10.13 3 C 2 false w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
12 2019-03-01 ¤ 21,500.00 9.63 5 O 5 true w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
13 2019-03-03 ¤ 57,500.00 7.63 5 C 9 true w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
14 2019-03-10 ¤ 127,500.00 8.63 15 m 10 false w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
15 2019-03-11 ¤ 198,500.00 7.12 15 m 3 false w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
16 2019-03-21 ¤ 148,500.00 7.63 15 M 1 false w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
17 2019-03-22 ¤ 98,500.00 6.87 30 M 1 true w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
18 2019-03-31 ¤ 16,500.00 6.37 3 o 3 true w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
19 2019-04-01 ¤ 11,500.00 8.13 5 C 2 false w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
20 2019-04-15 ¤ 26,500.00 8.63 4 c 3 false w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
21 2019-04-18 ¤ 42,500.00 10.03 4 C 8 false w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
22 2019-04-22 ¤ 348,500.00 7.63 15 m 10 true w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
23 2019-05-01 ¤ 148,500.00 5.87 15 M 3 false w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
24 2019-05-03 ¤ 348,500.00 8.33 30 M 4 true w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
25 2019-05-08 ¤ 273,500.00 9.33 15 M 7 false w/vvL+XHYp3S5s49hgkAKcuNv0psBEBdoNbr/gSfO9Y=-~LxL/flNAQgs+VtQYyoOqdw==
Type LoanName mSysRowId
C Car mhV/q5hW91tYcdh+wcy6SyAxNoqJqHKMnMnMLOQU9sQ=-~4L9g6hdLVDwcTkPHqk2uYg==
M Mortgage mhV/q5hW91tYcdh+wcy6SyAxNoqJqHKMnMnMLOQU9sQ=-~4L9g6hdLVDwcTkPHqk2uYg==
O Personal mhV/q5hW91tYcdh+wcy6SyAxNoqJqHKMnMnMLOQU9sQ=-~4L9g6hdLVDwcTkPHqk2uYg==
ID mSysRowId
1 0XI/ucs8eH0C414G0owUnmc7ic3tYV26zFYvDzULrtI=-~LJFcRXT03PM7vLlsLe1VUQ==
PaymentID LoanID PaymentDate AmountReceived mSysRowId
1 1 2019-02-15 ¤ 4,317.92
2 2 2019-02-15 ¤ 621.35
3 3 2019-02-25 ¤ 226.96
4 4 2019-02-28 ¤ 80.28
5 5 2019-03-08 ¤ 3,393.36
6 6 2019-03-12 ¤ 135.40
7 1 2019-03-15 ¤ 4,317.92
8 2 2019-03-15 ¤ 621.35
9 7 2019-03-15 ¤ 609.82
10 8 2019-03-20 ¤ 2,050.69
11 9 2019-03-21 ¤ 86.34
12 3 2019-03-25 ¤ 226.96
13 10 2019-03-28 ¤ 1,872.66
14 4 2019-03-31 ¤ 80.28
15 11 2019-04-01 ¤ 731.68
16 12 2019-04-01 ¤ 345.04
17 5 2019-04-08 ¤ 3,393.36
18 14 2019-04-10 ¤ 1,195.31
19 15 2019-04-11 ¤ 1,900.73
20 6 2019-04-12 ¤ 135.40
21 1 2019-04-15 ¤ 4,317.92
22 2 2019-04-15 ¤ 621.35
23 7 2019-04-15 ¤ 609.82
24 8 2019-04-20 ¤ 2,050.69
25 9 2019-04-21 ¤ 86.34
26 16 2019-04-21 ¤ 1,315.52
27 17 2019-04-22 ¤ 590.30
28 3 2019-04-25 ¤ 226.96
29 10 2019-04-28 ¤ 1,872.66
30 4 2019-04-30 ¤ 80.28
31 18 2019-04-30 ¤ 384.74
32 11 2019-05-01 ¤ 731.68
33 12 2019-05-01 ¤ 345.04
34 19 2019-05-01 ¤ 127.76
35 5 2019-05-08 ¤ 3,393.36
36 14 2019-05-10 ¤ 1,195.31
37 15 2019-05-11 ¤ 1,900.73
38 6 2019-05-12 ¤ 135.40
39 1 2019-05-15 ¤ 4,317.92
40 2 2019-05-15 ¤ 621.35
41 7 2019-05-15 ¤ 609.82
42 20 2019-05-15 ¤ 541.21
43 21 2019-05-18 ¤ 962.90
44 8 2019-05-20 ¤ 2,050.69
45 9 2019-05-21 ¤ 86.34
46 16 2019-05-21 ¤ 1,315.52
47 17 2019-05-22 ¤ 590.30
48 22 2019-05-22 ¤ 3,319.54
49 3 2019-05-25 ¤ 226.96
50 10 2019-05-28 ¤ 1,872.66
51 4 2019-05-30 ¤ 80.28
52 18 2019-05-30 ¤ 384.74
53 11 2019-06-01 ¤ 731.68
54 12 2019-06-01 ¤ 345.04
55 19 2019-06-01 ¤ 127.76
56 23 2019-06-01 ¤ 1,190.79
57 24 2019-06-03 ¤ 2,692.14
58 5 2019-06-08 ¤ 3,393.36
59 25 2019-06-08 ¤ 2,897.05
60 14 2019-06-10 ¤ 1,195.31
61 15 2019-06-11 ¤ 1,900.73
62 6 2019-06-12 ¤ 135.40
63 1 2019-06-15 ¤ 4,317.92
64 2 2019-06-15 ¤ 621.35
65 7 2019-06-15 ¤ 609.82
66 20 2019-06-15 ¤ 541.21
67 21 2019-06-18 ¤ 962.90
68 8 2019-06-20 ¤ 2,050.69
69 9 2019-06-21 ¤ 86.34
70 16 2019-06-21 ¤ 1,315.52
71 17 2019-06-22 ¤ 590.30
72 22 2019-06-22 ¤ 3,319.54
73 3 2019-06-25 ¤ 226.96
74 10 2019-06-28 ¤ 1,872.66
75 4 2019-06-30 ¤ 80.28
76 18 2019-06-30 ¤ 384.74
SELECT LoanTypes.LoanName, Loans.Date, Loans.Amount, Loans.Term, Loans.Adjustable FROM LoanTypes INNER JOIN Loans ON LoanTypes.Type = Loans.Type;
SELECT Loans.LoanID, Loans.Date, Loans.Amount, Loans.InterestRate, Loans.Term, Loans.Type, Loans.Adjustable FROM Loans;
SELECT Customers.FirstName, Customers.LastName, Customers.PhoneNumber, Customers.Address, Customers.City, Customers.State, Customers.ZipCode FROM Customers;
SELECT Payments.PaymentDate, Payments.AmountReceived AS Total, Payments.AmountReceived AS Average FROM Payments;
SELECT Customers.FirstName, Customers.LastName, Customers.PhoneNumber, Loans.InterestRate, LoanTypes.LoanName, Loans.Adjustable FROM Customers INNER JOIN (LoanTypes INNER JOIN Loans ON LoanTypes.Type = Loans.Type) ON Customers.CustomerID = Loans.CustomerID WHERE (((Loans.Adjustable)=Yes)) ORDER BY Loans.InterestRate DESC;

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