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A Unified Approach To Measurement Error And Missing Data: Overview And Applications
posting ads with us Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow pcl xl error missing data Community Stack Overflow is a community of 4.7 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up ERROR: missing data for column SQL state: 22P04 postgresql http://h30434.www3.hp.com/t5/LaserJet-Printing/PCL-Xl-error-subsystem-userstream-error-quot-missing-data/td-p/5435333 up vote 0 down vote favorite I am trying to import CSV file into the postgreSQL database table. But i am getting an error : ERROR: missing data for column "portion" SQL state: 22P04 Context: COPY fooditems, line 2: "Chicken Curry,100,132,4.5,11.4,7.6" SQLCommands i am using is : create table FoodItems (Food_item varchar(20), Portion varchar(7), Calories varchar(7), Carbohydrates varchar(7), Protein varchar(7), Total_Fat varchar(7), primary key (Food_item)); COPY FoodItems http://stackoverflow.com/questions/29465703/error-missing-data-for-column-sql-state-22p04-postgresql (Food_item,Portion,Calories,Carbohydrates,Protein,Total_Fat) FROM 'D:\convertcsv.csv' WITH DELIMITER ';' CSV HEADER; link for the html page: http://www.buzzle.com/articles/indian-food-calories.html I need the data in table so that i can use it for back-end for an app. I don't understand what seems to be the issue. Hope someone can help me. Thanks in advance. sql postgresql csv share|improve this question edited Apr 6 '15 at 5:14 wingedpanther 6,84631542 asked Apr 6 '15 at 5:12 toyjrah 12 Can you show a screenshot of your CSV File ?? –wingedpanther Apr 6 '15 at 5:18 add a comment| 1 Answer 1 active oldest votes up vote 0 down vote accepted The problem is with the CSV file.To test just change DELIMITER ';' to DELIMITER ',' and run your COPY Command Ie. COPY FoodItems (Food_item,Portion,Calories,Carbohydrates,Protein,Total_Fat) FROM 'D:\food.csv' WITH DELIMITER ',' CSV HEADER; share|improve this answer answered Apr 6 '15 at 5:41 wingedpanther 6,84631542 add a comment| Your Answer draft saved draft discarded Sign up or log in Sign up using Google Sign up using Facebook Sign up using Email and Password Post as a guest Name Email Post as a guest Name Email discard By posting your answer, you agree to the privacy policy and terms of ser
can have a significant effect on the conclusions that can be drawn from the data. Missing data can occur because of nonresponse: no information is provided for one or more items or for a whole unit https://en.wikipedia.org/wiki/Missing_data ("subject"). Some items are more likely to generate a nonresponse than others: for example items about private subjects such as income. Attrition ("Dropout") is a type of missingness that can occur in longitudinal studies - for instance studying development where a measurement is repeated after a certain period of time. Missingness occurs when participants drop out before the test ends and one or more measurements are missing. Data often are missing in research error missing in economics, sociology, and political science because governments choose not to, or fail to, report critical statistics.[1] Sometimes missing values are caused by the researcher—for example, when data collection is done improperly or mistakes are made in data entry.[2] These forms of missingness take different types, with different impacts on the validity of conclusions from research: Missing completely at random, missing at random, and missing not at random. Contents 1 Types of missing error missing data data 1.1 Missing completely at random 1.2 Missing at random 1.3 Missing not at random 2 Techniques of dealing with missing data 2.1 Imputation 2.2 Partial imputation 2.3 Partial deletion 2.4 Full analysis 2.5 Interpolation 3 Model-based techniques 4 See also 5 References 6 Further reading 7 External links 7.1 Background 7.2 Software Types of missing data[edit] Understanding the reasons why data are missing is important to correctly handle the remaining data. If values are missing completely at random, the data sample is likely still representative of the population. But if the values are missing systematically, analysis may be biased. For example, in a study of the relation between IQ and income, if participants with an above-average IQ tend to skip the question ‘What is your salary?’ , analyses that do not take into account this missing at random (MAR pattern (see below)) may falsely fail to find a positive association between IQ and salary. Because of these problems, methodologists routinely advise researchers to design studies to minimize the occurrence of missing values.[2] Graphical models[3][4] can be used to describe the missing data mechanism in detail. The graph shows the probability distributions of the estimations of the expected intensity of depression in the population. The number of cases is 60. Let the true population be a standardised nor
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