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I am new to SAS and still learning. I need to submit my school assignment and posting below questions for which I need some helping hand in completing those.
Deciding an appropriate path for analysis often requires many steps. An important first step is exploring and examining the data. An initial exploratory data analysis provides understanding of the meaning of study variables and can provide crucial clues into data preparations needed before analyzing the data.
- Open and examine the SASHELP.HEART dataset and its variables. Familiarize yourself with the context and meanings behind the variables and their values.
- How many observations are in the dataset?
I answered this :- There are total of 5209 observations in the data set.
- How many variables are in the dataset? How many are numeric? How many are character?
I answered this :- There are 17 variables in the dataset. 10 variables are numeric & 7 variables are character type.
Exploring the assigned values of character variables can demonstrate patterns and inherent orderings. The default ordering of levels in SAS is alphabetical order. The levels of many character variables have an inherent ordering of magnitude. For example, non-smokers smoke less than light smokers who smoke less than moderate smokers.
- Tabulate the levels of the character variables in the SASHELP.HEART dataset. For each of the character variables:
- What data values or levels are observed for each?
- Which variables have an inherent ordering of magnitude? Does alphabetical order of the levels correspond to ordering levels by magnitude for any of these character variables?
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Thank you Reeza..
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And to get a supporting report for the questions you've already answered:
proc contents data=sashelp.heart order=varnum;
quit;
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I'm having issues on the problem:
Tabulate the percent of observations in the SASHELP.HEART dataset that have non-missing values for all the predictor variables that you will use in later analyses: AgeAtStart, BP_Status, Chol_Status, Cholesterol, Diastolic, Height, MRW, Sex, Smoking, Smoking_Status, Systolic, Weight, and Weight_Status.
Please help!
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@vsandoval6 wrote:
I'm having issues on the problem:
Tabulate the percent of observations in the SASHELP.HEART dataset that have non-missing values for all the predictor variables that you will use in later analyses: AgeAtStart, BP_Status, Chol_Status, Cholesterol, Diastolic, Height, MRW, Sex, Smoking, Smoking_Status, Systolic, Weight, and Weight_Status.
Please help!
This is a new question, too. Please create a new topic and show what you have already tried.
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This question was cross-posted to
where an answer was provided.
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@AMweneykhondo wrote:
Greetings i am so lost. The professor told us to copy and paste the code. but it keeps giving me errors. I am so frustrated. It is the exact question
Please start a new topic and post the log, so that we see the errors actually.
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