Problem 1

: Download the wine data set at UCI:

https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data

Dataset description:

https://archive.ics.uci.edu/ml/datasets/Wine

1)

There are 14 variables in this dataset. INPUT the 14 variables by first

four letters for each variable, e.g. 'alco' for 'Alcohol'. Then use

LABEL statement to name the full name for each variable.

NOTES:

The first variable is 'identifier' not 'Alcohol'.

2)

Change the dataset's TITLE to 'Wine Summary Data'.

3)

Generate descriptive statistics by using PROC MEANS.

4)

Generate a probability plot and test the normality for variable 'Malic

acid'. (i.e normal or not)

5)

Generate a vertical histogram for 'Malic acid' divided by variable

'identifier' (the first variable, use subgroup option)

6)

Generate a scatter plot for 'Malic acid' vs 'Ash' for each value of

'identifier'. (Use BY statement).

Submit code for parts 1) to 6), output 3) to 6).

Problem 2:

Download the corn data which measurements in six states from

1909 to 1927 at sakai, also see dataset description from the file corn_info

from sakai.

Create new variable catyield to categorize the corn yield:

if yield < 32, then catyield = 'poor';

if yield >= 32, then catyield = 'good'.

Create new variable catrain to categorize the amount of rainfall:

if rain < = 9.7, then catrain = 'drought';

if 9.7 < rain < = 12, then catrain = 'normal';

if rain >12, then catrain = 'wet'.

(Hint: please see the class 2 notes on 'create new variables' if needed.)

1) Use PROC FREQ to analyze the variables catyield and catrain. Explain

the SAS output.

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2) Create two way table for corn yield (i.e., catyield) by the amount of

rainfall (i.e., catrain). Do you notice anything unusual? Why? (i.e. how does

the amount of rainfall relates to the corn yield?)

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Please hand in your SAS code and output and provide your answers to the

above questions.