Friday, November 25, 2011
Command prompt window just appear and disappear!
The solution to this is just remove this key : HKLM\Software\Microsoft\Command Processor\AutoRun, from the registry, wich was added by the virus. remove the value EXIT
For more information about what the virus does, visit this web page "http://net-studio.org/fra/patch/patch/8-patch-pour-supprimer-le-virus-cradle-of-filth-vbe.html" (sorry if it is in french)
Wednesday, November 23, 2011
Stat
measures of location -- a statistic that describes a location within a data set. Measures of central tendency described the center of the distribution
mean -- the average; that value obtained by summing all elements in a set and dividing by the number of elements
mode -- a measure of central tendency given as the value that occurs the most in a sample distribution
median -- a measure of central tendency given as the value above which half of the values fall and below which half of the values fall
measures of variability -- a statistic that indicates the distributions dispersion
range -- the difference between the largest and smallest values of distribution
interquartile range -- the range of distribution income passing the middle 50% of the observations
variants -- the mean squared deviation of all the values from the mean
standard deviation -- the square root of the variance
coefficient of variation -- a useful expression in sampling theory for the standard deviation as a percentage of the mean
skewness -- a characteristic of a distribution that assesses its symmetry about the mean
kurtosis -- a measure of the relative peakedness or flatness of the curve defined by the frequency distribution
null hypothesis -- a statement in which no difference or effect is expected. If the null hypothesis is not rejected, no changes will be made
alternative hypothesis -- a statement that some difference or effect is expected. Excepting the alternative hypothesis will lead to changes in opinions or actions
one tailed test -- a test of the null hypothesis where the alternative hypothesis is expressed directionally
two tailed test -- a test of the null hypothesis where the alternative hypothesis is not expressed directionally
test statistic -- a measure of how close the sample has come to the null hypothesis. It often follows a well-known distribution, such as the normal, t, or chi- squared distribution
type I error -- also known as Alpha error, occurs when a sample results lead to the rejection of a null hypothesis that is in fact true
level of significance -- the probability of making a type 1 error
type II error -- also known as beta error, occurs when the sample results lead to the non-rejection of a null hypothesis that is in fact false
power of a test -- the probability of rejecting the null hypothesis when it is in fact false and should be rejected
Cross tabulation -- a statistical technique that describes two or more variables simultaneously and results in tables that reflect the joint distribution of two or more variables that have a limited number of categories or distinct values
contingency table -- a cross tabulation table. It contains a cell for every combination of categories of the two variables
chi-square statistic -- the statistic used to test the statistical significance of the observed association and cross tabulation. It assists us in determining whether a systematic association exists between the two variables
chi-square distribution -- a skewed distribution and shape depends solely on the number of degrees of freedom. As the number of degrees of freedom increases, the chi-square distribution becomes more symmetrical
phi coefficient -- a measure of the strength of Association and the special case of a table with two rows and two columns
contingency coefficient (C) -- a measure of the strength of association in a table of any size
Cramer's V -- a measure of the strength of association used in tables larger than 2 x 2
asymmetric lambda -- a measure of the percentage improvement in predicting the value of the dependent variable, given the value of the independent variable and contingency table analysis. Lambda also varies between zero and one
symmetric lambda -- the symmetric lambda does not make an assumption about which variable is dependent. It measures the overall improvement when production is done in both directions
tau b -- test statistic that measures the association between two ordinal-level variables. It makes adjustment for ties and is most appropriate when the table of variables is square
tau c -- test statistic that measures the association between two ordinal-level variables. It makes adjustment for ties and is most appropriate when the table of variables is not square but a rectangle
Gamma -- test statistic that measures the association between two ordinal-level variables. It does not make an adjustment for ties
parametric tests -- hypothesis testing procedures that assume that the variables of interest are measured on at least an interval scale
non-parametric tests -- hypothesis testing procedures that assume that the variables are measured on a nominal or ordinal scale
t test -- a univariate hypothesis test using the t distribution, which is used in the standard deviation is unknown and the sample size is small
t statistic -- a statistic that assumes that the variable has a symmetric bell shaped distribution in the mean is known (or assumed to be known) and the population variants is estimated from the sample
t distribution -- symmetric bell shaped distribution that is useful for small sample testing
z test -- a univariate hypothesis test using the standard normal distribution
independent samples -- to samples that are not experimentally related. The measurement of one sample has no effect on the values of the second sample
f test -- a statistical test of the equality of the variances of two populations
f statistic -- the f statistic is computed as the ratio of two sample variances
f distribution -- a frequency distribution that depends on two sets of degrees of freedom -- the degrees of freedom in the numerator and the degrees of freedom in the denominator
paired samples -- and hypothesis testing, the observations are paired so that two sets of observations relate to the same respondents
paired samples t test -- a test for differences in the means of paired samples
Kolmogorov-Smirnov one-sample test - A one sample nonparametric goodness of fit test that compares the cumulative distribution function for a variable with a specified distribution
runs test -- a test of randomness for a dichotomous variable
binomial test -- a goodness of fit statistical test for dichotomous variables. It tests the goodness of fit of the observed number of observations in each category to the number expected under a specified binomial distribution
Mann-Whitney U test -- a statistical test for the variable measured on an ordinal scale comparing the difference in the location of two populations based on observations from two independent samples
two-sample median test -- non-parametric test statistic that determines whether two groups are drawn from populations with the same median. This test is not as powerful as the Mann- Whitney U
Kolmogorov-Smirnov two-sample test -- nonparametric test statistic that determines whether to his divisions are the same. It takes into account any differences in the two distributions including median, dispersion, and skewness
Wilcoxon matched-pairs signed-ranks test -- a nonparametric test that analyzes the differences between the paired observations, taking into account the magnitude of the differences
sign test -- a nonparametric test for examining differences in the location of two populations, based on paired observations, that compares only the signs of the differences between pairs of variables without taking into account the magnitude of the differences
Thursday, November 17, 2011
Types of Survey Errors
Coverage errors occur when the sampling frame excludes some segments of the target population. Phone books are very convenient, but unlisted households are excluded. Election polls sample from the frame of registered voters, but the target population is the subset who are going to vote.
Nonresponse errors can cause serious bias in survey results. Phone interviewers find it easiest to reach families with young children, and hardest to reach young singles. Mail surveys tend to be answered by those who feel strongly about an issue, or by those who feel more civic responsibility, neither of which is a representative cross-section of the population.
Measurement Errors occur when respondents answer `inaccurately' because of question wording, question ordering, interviewer effect, or other external influences. For example, the answer to ``Do you approve of affirmative action?'' may be influenced by the gender of the interviewer. Also, the question ``Do you like living in Davis Hall?'' may be influenced by preceding it with the question ``Do you have enough parking spots in Davis Hall?''.
Survey Sampling Methods
It is incumbent on the researcher to clearly define the target population. There are no strict rules to follow, and the researcher must rely on logic and judgment. The population is defined in keeping with the objectives of the study.
Sometimes, the entire population will be sufficiently small, and the researcher can include the entire population in the study. This type of research is called a census study because data is gathered on every member of the population.
Usually, the population is too large for the researcher to attempt to survey all of its members. A small, but carefully chosen sample can be used to represent the population. The sample reflects the characteristics of the population from which it is drawn.
Sampling methods are classified as either probability or nonprobability. In probability samples, each member of the population has a known non-zero probability of being selected. Probability methods include random sampling, systematic sampling, and stratified sampling. In nonprobability sampling, members are selected from the population in some nonrandom manner. These include convenience sampling, judgment sampling, quota sampling, and snowball sampling. The advantage of probability sampling is that sampling error can be calculated. Sampling error is the degree to which a sample might differ from the population. When inferring to the population, results are reported plus or minus the sampling error. In nonprobability sampling, the degree to which the sample differs from the population remains unknown.
Random sampling is the purest form of probability sampling. Each member of the population has an equal and known chance of being selected. When there are very large populations, it is often difficult or impossible to identify every member of the population, so the pool of available subjects becomes biased.
Systematic sampling is often used instead of random sampling. It is also called an Nth name selection technique. After the required sample size has been calculated, every Nth record is selected from a list of population members. As long as the list does not contain any hidden order, this sampling method is as good as the random sampling method. Its only advantage over the random sampling technique is simplicity. Systematic sampling is frequently used to select a specified number of records from a computer file.
Stratified sampling is commonly used probability method that is superior to random sampling because it reduces sampling error. A stratum is a subset of the population that share at least one common characteristic. Examples of stratums might be males and females, or managers and non-managers. The researcher first identifies the relevant stratums and their actual representation in the population. Random sampling is then used to select a sufficient number of subjects from each stratum. "Sufficient" refers to a sample size large enough for us to be reasonably confident that the stratum represents the population. Stratified sampling is often used when one or more of the stratums in the population have a low incidence relative to the other stratums.
Convenience sampling is used in exploratory research where the researcher is interested in getting an inexpensive approximation of the truth. As the name implies, the sample is selected because they are convenient. This nonprobability method is often used during preliminary research efforts to get a gross estimate of the results, without incurring the cost or time required to select a random sample.
Judgment sampling is a common nonprobability method. The researcher selects the sample based on judgment. This is usually and extension of convenience sampling. For example, a researcher may decide to draw the entire sample from one "representative" city, even though the population includes all cities. When using this method, the researcher must be confident that the chosen sample is truly representative of the entire population.
Quota sampling is the nonprobability equivalent of stratified sampling. Like stratified sampling, the researcher first identifies the stratums and their proportions as they are represented in the population. Then convenience or judgment sampling is used to select the required number of subjects from each stratum. This differs from stratified sampling, where the stratums are filled by random sampling.
Snowball sampling is a special nonprobability method used when the desired sample characteristic is rare. It may be extremely difficult or cost prohibitive to locate respondents in these situations. Snowball sampling relies on referrals from initial subjects to generate additional subjects. While this technique can dramatically lower search costs, it comes at the expense of introducing bias because the technique itself reduces the likelihood that the sample will represent a good cross section from the population.
Friday, November 11, 2011
Life is like that.
Wednesday, September 21, 2011
SQL --// Restore failed for Server. //--
Applies to: Microsoft SQL Server 2005/2008 using SQL Server Management Studio as administrative tool.
Symptom
Restore failed for Server <Instance Name>. (Microsoft.SqlServer.Smo)
Additional information:
System.Data.SqlClient.SqlError: The backup set holds a backup of a database other than the existing <Current Database Name or Target DB Name>. (Microsoft.SqlServer.Smo)
Cause.
This error appears when you are trying to restore a backup set that was not created on the database that you want to apply this restore.
Solution.
To apply this restore and prevent this error message, you will need to select "Overwrite the existing database" on the restore dialog as shown in the image below.
Tuesday, September 20, 2011
"Where do you see yourself in three years?"
Story:
"Where do you see yourself in three years?" isn't a useful career development question—not without analysis. The Career Target is a fast, simple, effective technique. It's time-boxed; if you're not done #5 in 22 minutes you're doing it wrong!
Materials: paper; four pens (black, blue, red, green); highlighter.
1. Bull's-eye (90 seconds)
Draw a circle about 7cm/2" across on the page (use black until #4). Inside the circle record three to seven things you love to do in your current role. These are usually activities you do well. These are not about money; they motivate you and help bring meaning to your life. If your current role has nothing you love like this, note how you satisfy these drives (volunteer work, hobbies, travel, etc.). Highlight unpaid work.
2. Off-Target (2 minutes)
Draw a circle around the bull's-eye, about 15cm/6" across. Outside this circle, record activities in your current role that you loathe. You often perform these off-target activities poorly. To never do these again would be a relief.
3. Beside-the-Point (2 minutes)
Between the circles, record activities you do, but don't care about. You may be good at them, but they are not challenging or interesting. It wouldn't bother you to never do them again.
4. Clustering (90 seconds)
Present: Put a blue circle around everything that is necessary to success in your current role. Most people find an item in the bull's-eye, a few beside-the-point, and a few off-target. Put a red box around everything that prevents your success in your current role. Many people box an item in the bull's-eye.
Future: Highlight activities critical to your success in your desired role. Some may be red boxed in your current role. In green, record activities that you don't do today but which are critical to the desired role. Put them in the appropriate circles and highlight them.
5. Analyse and Plan (15 minutes)
Your target now shows what you care about and what activities you need to stop, start and continue for success. Is your desired role a good fit? What roles might be better? Find ways to do all critical activities yourself (perhaps with help). Don't try to stop behaviours that prevent success; find other venues for these drives (hobbies, volunteering, sports, etc.). Delegate everything else.
6. Act (the rest of your life)
Go make it happen, and revisit your target as needed.
Monday, September 12, 2011
“HTTP Error 404- File or Directory not found” IIS
Problem "HTTP Error 404- File or Directory not found"
Tryouts
IF
A.)To permit IIS to serve content that requires a specific ISAPI or CGI extension that is already listed in the Web service extensions list, follow these steps:
1. Open IIS Manager, expand the master server node (that is, the Server name node), and then select the Web service extensions node.
2. In the right pane of IIS Manager, right-click the extension that you want to enable. In this example, this is ASP.NET v2.0.50727.
3. Click to select the Allow check box.
Elseif
B.) If ASP.NET v2.0.xxx does not appear in Web Services do:
start/Run/cmd:
cd C:\WINDOWS\Microsoft.NET\Framework\v2.0.50727
(where Windows is your windows directory and .50727 is your .NET version)
aspnet_regiis –ir
Sunday, August 28, 2011
Research types / Research methods / Data collection Methods
Research types
- Deductive
- Inductive
Deductive Research Approach
Deductive reasoning works from the more general to the more specific. Sometimes this is informally called a "top--down" approach. down" approach. Conclusion follows logically from premises (available facts)
Theory -->Hypothesis-->Observation -->Confirmation (Waterfall)
Inductive Research Approach
Inductive reasoning works the other way, moving from specific observations to broader generalizations and theories. Informally, we sometimes call this a "bottom up" approach Conclusion is likely based on premises. Involves a degree of uncertainty
Observation-->Pattern-->Tentative Hypothesis -->Theory (Hill Climbing)
Research methods
1. Qualitative
2. Quantitative
Differences between
Qualitative and Quantitative Research Methods
Qualitative Methods | Quantitative Methods |
Methods include focus groups, in-depth interviews, and reviews | Surveys |
Primarily inductive process used to formulate theory | Primarily deductive process used to test pre-specified concepts, constructs, and hypotheses that make up a theory |
More subjective: describes a problem or condition from the point of view of those experiencing it | More objective: provides observed effects (interpreted by researchers) of a program on a problem or condition |
Text-based | Number-based |
More in-depth information on a few cases | Less in-depth but more breadth of information across a large number of cases |
Unstructured or semi-structured response options | Fixed response options |
No statistical tests | Statistical tests are used for analysis |
Can be valid and reliable: largely depends on skill and rigor of the researcher | Can be valid and reliable: largely depends on the measurement device or instrument used |
Time expenditure lighter on the planning end and heavier during the analysis phase | Time expenditure heavier on the planning phase and lighter on the analysis phase |
Less generalizable | More generalizable |
Data collection Methods
1. Primary
2. Secondary
Secondary data – data someone else has collected
- County health departments
- Vital Statistics – birth, death certificates
- Hospital, clinic, school nurse records
- Private and foundation databases
- City and county governments
- Surveillance data from state government programs
Primary data – data you collect
- Surveys
- Focus groups
- Questionnaires
- Personal interviews
- Experiments and observational study
Saturday, August 27, 2011
Story Behind your date of Birth
|
Thursday, August 25, 2011
Monday, August 22, 2011
Monday, August 15, 2011
Oracle Financials / E Business Suite
Gl_code_combinations
##Setup > Accounts > Combinations
This table stores the valid account combinations.
The value in the chart of account segments are stored in the columns segment1 to segment30 depending on application configuration.
For example, say your chart of accounts is
Company – Cost Centre – Account
then segment1 = company, segment 2 = cost centre and segment3 = account.
Another important column is the account_type which signifies the account is an Asset, Liability, Revenue, Expense or Owners Equity account.
Gl_je_batches
##Journals > Enter
This table stores the journal entry batches. Journal entries are batched in General Ledger.
Some columns of interest includes :
• Name
• Set_of_books_id (when we have more than one set of book, we’ll also need to link to gl_sets_of_books)
• Status
• Default_period_name
• Posted_date
• Posting_run_id
Gl_je_headers
##Journals > Enter
This table stores the journal entry headers. There is always two journal lines for each journal header.
Some columns of interest includes :
• Je_category
• Period_name
• Set_of_books_id (when we have more than one set of book, we’ll also need to link to gl_sets_of_books)
• Posted_flag
• Je_source
• Name
• Status
Gl_je_lines
##Journals > Enter
This table stores the journal entry lines.
The entered_dr and entered_cr stores the amount in the entered currency whereas the accounted_dr and accounted_cr stores the amount in the functional currency.
Other columns of interest includes :
• Set_of_books_id (when we have more than one set of book, we’ll also need to link to gl_sets_of_books)
• Period_name
• Status
• Description
• Reference_1..reference10 (these columns links back to the Subledgers)
For example, for Purchasing transactions
Reference_1 = ‘PO’
Reference_2 = po_headers_all.po_header_id
Reference_3 = po_distributions_all.po_distribution_id
Reference_4 = po_headers_all.segment? (this is the purchase order number)
Oracle Payables
Ap_invoices_all
##Invoices > Entry > Invoices
This table stores all the invoices entered in. For an invoice to be approved, the total invoice amount must be stored in ap_invoice_distributions_all and ap_payment_schedules_all.
Some columns of interest includes :
• Invoice_num
• Invoice_date
• Amount_paid
• Invoice_currency_code
• Invoice_type_lookup_code
• Payment_status_flag
Ap_invoice_distributions_all
##Invoices > Entry > Invoices
This table stores the accounting information for the invoice what have entered. There is one row for each invoice disribution, that is this table corresponds to the Distributions window.
Some columns of interest includes :
• Line_type_lookup_code
• Dist_code_combination_id (credit entry)
• Accts_pay_code_combination_id (debit_entry)
• Base_amount (in functional currency)
Ap_checks_all
##Payments > Entry > Payments
This table stores payments to suppliers.
Some columns of interest includes :
• Amount (in functional currency)
• Check_date
• Bank_account_name
• Check_number
• Payment_method_lookup_code
• Payment_type_flag
Ap_invoice_payments_all
##Payments > Entry > Payments
This table stores invoice payments to suppliers. This table is updated when we confirm an automatic payment batch, enter a manual payment or process a Quick Payment. Void payments are represented as a negative of the original payment line.
Some columns of interest includes :
• Accounting_date
• Period_name
• Amount
• Payment_num
Ap_payment_distributions_all
##Payments > Entry > Payments
This table stores accounting information for payments. There is at least one CASH payment distribution for each invoice payment. Additional rows may include DISCOUNT, GAIN and LOSS distributions where appropriate.
Some columns of interest includes :
• Line_type_lookup_code (CASH/DISCOUNT/GAIN/LOSS)
• Base_amount
Oracle Purchasing
Po_vendors
##Supply Base > Suppliers
This table stores supplier information.
Some columns of interest includes :
• Segment1 (supplier number)
• Vendor_name
• Terms_id
• Vendor_type
• Ship_to_location (link to hr_locations for location information)
• Bill_to_location (link to hr_locations for location information)
Po_vendor_sites_all
##Supply Base > Suppliers
This table stores supplier sites information.
Some columns of interest includes :
• Pay_site_flag
• Purchasing_site_flag
• Address_line1 to address_line3
• City
• State
• Area_code
• Zip
Po_headers_all
##Purchase Orders > Purchase Orders
This table stores the seven types of purchasing documents such as Purchase Order and Blanket Agreement.
Segment1 is the document number (i.e. purchase order number)
Some columns of interest includes :
• Agent_id (link to per_people_f for the buyer)
• Type_lookup_code
Po_lines_all
##Purchase Orders > Purchase Orders
This table stores purchasing document lines.
Some columns of interest includes :
• Line_num
• Item_description
• Unit_price
• Unit_meas_lookup_code (unit of measure)
• Quantity
• Item_id (link to mtl_system_items for the item number)
• Category_id (link to mtl_categories for the category name)
Po_line_locations_all
##Purchase Orders > Purchase Orders
This table stores purchase order shipment schedules and blanket agreement price breaks. A purchase order is closed when QUANTITY is equal to QUANTITY_RECEIVED.
Some columns of interest includes :
• Quantity
• Quantity_accepted
• Quantity_received
• Quantity_cancelled
• Need_by_date
• Ship_to_organization_id (link to org_organization_definitions for the organization code)
Po_distributions_all
##Purchase Orders > Purchase Orders
This table stores the accounting information on a purchase order shipment. This table is used for Standard and Planned Purchase Orders and Planned and Blanket Purchase Order Release.
Some columns of interest includes :
• Quantity_ordered
• Quantity_billed
• Amount_billed
• Quantity_delivered
• Quantity_cancelled
• Destination_organization_id (link to org_organization_definitions for the organization code)
• Destination_subinventory
Rcv_shipment_headers
##Receiving > Receipts
This table stores the receiving information. The three receipt sources are Supplier, Inventory and Internal Order. There is one receipt header per receipt source.
Some columns of interest includes :
• Receipt_num
• Shipment_num
• Receipt_source_code
• Shipped_date
• Ship_to_org_id
Rcv_shipment_lines
##Receiving > Receipts
This table stores information about items that have been shipped and/or received from a receipt source.
Some columns of interest includes :
• Line_num
• Quantity_shipped
• Unit_of_measure
• Item_id (link to mtl_system_items for item number)
• To_organization_id (link to org_organization_definitions for organization code)
• To_subinventory
• Shipment_line_status_code (EXPECTED, FULLY RECEIVED, PARTIALLY RECEIVED)
• Quantity_received
• Quantity_shipped
Oracle Inventory
Org_organization_definitions
##Setup > Organizations > Parameters
This view contains basic information on all inventory organisations.
Some columns of interest includes :
• Organization_code
• Organization_name
• Set_of_books_id (when we have more than one set of book, we’ll also need to link to gl_sets_of_books)
• Inventory_enabled_flag
Mtl_secondary_inventories
##Setup > Organizations > Subinventories
This table stores all subinventory information for an inventory organisation.
Some columns of interest includes :
• Secondary_inventory_name
• Description
Mtl_material_transactions
##Transactions > Material Transactions (Inquiry)
This table stores all inventory transactions including cost updates.
Some columns of interest includes :
• Transaction_quantity
• Transaction_type_id
• Transaction_source_type_id
• Transaction_source_name
Mtl_transaction_accounts
##Transactions > Material Distributions (Inquiry)
This table stores the inventory accounting information. There are two rows in this table for each transaction in mtl_material_transactions.
Some columns of interest includes :
• Transaction_date
• Gl_batch_id
• Accounting_line_type
• Base_transaction_value
Mtl_system_items
##Items > Master Items or Items > Organization Items
This table stores the item definition. An item must exist in an inventory organisation.
item number is stored in the columns segment1 to segment20 depending on the application configuration. If the application have configured the items to have to segments then we may be using segment1 and segment2
Some columns of interest includes :
• Segment1 to segment20
• Description
• Invetory_item_flag
• Purchasing_item_flag
• Inventory_asset_flag
• Stock_enabled_flag
• Invoiceable_item_flag
• Shippable_item_flag
• So_transaction_flag
• Mtl_transactions_enabled_flag
• Primary_unit_of_measure
Mtl_onhand_quantities
##On-hand, Availability > On-hand Quantities
This table stores quantity on hand in a location for each item.
Some columns of interest includes :
• Date_received
• Transaction_quantity
• Subinventory_code
Cst_item_costs
##Costs > Item Costs
This table stores the item cost information. Note that there can be multiple costs per item and the actual cost is where the cost type is Frozen.
Some columns of interest includes :
• Cost_type_id (link to cst_cost_types)
• Item_cost
Oracle Receivables
Ra_customers
##Customers > Standard
This table stores customer information.
Some columns of interest includes :
• Customer_name
• Customer_number
• Status
• Customer_prospect_code
• Customer_type
• Orig_system_reference (for imported customers from an external source)
Ra_addresses_all
Customers > Standard
This table stores customer address information and what remit-to addresses.
Some columns of interest includes :
• Status
• Orig_system_reference (for imported customer addresses from an external source)
• Address1 to address4
• City
• State
• Postal_code
Ra_site_uses_all
##Customers > Standard
This table stores the customer’s site and site purpose. we must have one row for each address. A customer must have one bill to address for Receivables. A customer must have one ship to address and one bill to address for Order Entry.
Some columns of interest includes :
• Site_use_code (BILL_TO, SHIP_TO, STMTS, DUN/LEGAL)
• Primary_flag
• Status
• Location
Ra_customer_trx_all
##Transactions > Transactions
This table stores invoice, debit memo, chargeback, commitment and credit memo header information.
Some columns of interest includes :
• Cust_trx_type_id (link to ra_cust_trx_types_all)
• Set_of_books_id (when we have more than one set of book, we’ll also need to link to gl_sets_of_books)
• Terms_id (link to ra_terms)
• Trx_number (invoice number)
• Trx_date (invoice date)
Ra_customer_trx_lines_all
##Transactions > Transactions
This table stores the invoice, debit memo, chargeback, commitment and credit memo line information.
Some columns of interest includes :
• Line_number
• Description
• Quantity_ordered
• Quantity_credited
• Quantity_invoiced
• Unit_standard_price
• Unit_selling_price
• Line_type
• Extended_amount
• Revenue_amount
Ra_cust_trx_line_gl_dist_all
##Transactions > Transactions
This table stores the accounting information for revenue, unearned revenue, unbilled receivables, receivables, charges, freight and tax for each invoice or credit memo line.
Some columns of interest includes :
• Amount_gl_date
• Gl_posted_date
• Account_class (CHARGES/FREIGHT/TAX/REC/REV/UNBILL/UNEARN)
• Acctd_amount (functional currency)
Ar_cash_receipts
##Receipts > Receipts
This table stores the payment information.
Some columns of interest includes :
• Set_of_books_id (when we have more than one set of book, we’ll also need to link to gl_sets_of_books)
• Status (APP, UNAPP, UNID, NSF, STOP, REV)
• Type (CASH, MISC)
• Receipt_number
• Amount
• Currency_code
• Pay_from_customer
• Receipt_date
Ar_receivable_applications
##Receipts > Receipts
This table stores accounting entries for cash and credit memo applications.
Some columns of interest includes :
• Amount_applied
• Line_applied
• Tax_applied
• Application_type
• Display
• Gl_date
• Set_of_books_id (when we have more than one set of book, we’ll also need to link to gl_sets_of_books)
Ar_payment_schedules
##Transactions > Transactions and Receipts > Receipts
This table stores all transactions except adjustments and miscellaneous cash receipts. This table is updated when a transaction occurs against an invoice, debit memo, chargeback, credit memo, on-account credit, or receipt.
Some columns of interest includes :
• Amount_due_original
• Status
• Class (DEP, DM, PMT, GUAR, CM, CB, INV)
• Due_date
• Amount_due_remaining
• Invoice_currency_code
• Amount_applied
• Anmount_credited
• Amount_adjusted