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SAS Lesson 7 - ANOVA: Explanatory variable with 2 levels

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数据分析工具

卫斯连大学

4.5（345 個評分） | 33K 名學生已註冊

課程 2（共 5 門，数据分析和解释專項課程）

In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work.

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您將學習的技能

Chi-Squared (Chi-2) Distribution, Data Analysis, Statistical Hypothesis Testing, Analysis Of Variance (ANOVA)

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4.5（345 個評分）

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AM

Dec 03, 2015

Very good for beginners. concept explanation as well as coding were great. doesn't take too long to finish. I enrolled regression modeling course by Wesleyan and waiting to start.

SS

Mar 18, 2016

Amazing course for intermediate , well designed course material very good information about the different types of hypothesis testing.

從本節課中

Hypothesis Testing and ANOVA

This session starts where the Data Management and Visualization course left off. Now that you have selected a data set and research question, managed your variables of interest and visualized their relationship graphically, we are ready to test those relationships statistically. The first group of videos describe the process of hypothesis testing which you will use throughout this course to test relationships between different kinds of variables (quantitative and categorical). Next, we show you how to test hypotheses in the context of Analysis of Variance (when you have one quantitative variable and one categorical variable). Your task will be to write a program that manages any additional variables you may need and runs and interprets an Analysis of Variance test. Note that if your research question does not include one quantitative variable, you can use one from your data set just to get some practice with the tool. If your research question does not include a categorical variable, you can categorize one that is quantitative.

SAS Lesson 7 - ANOVA: Explanatory variable with 2 levels6:30

SAS Lesson 8 - ANOVA: Explanatory variables with more than 2 levels2:30

SAS Lesson 9 - Post hoc tests for ANOVA5:03

SAS Lesson 10 - ANOVA summary2:51

教學方

Jen Rose
Research Professor
Lisa Dierker
Professor

腳本

Is major depression associated with smoking quantity among current young adult

smokers?

Or, in hypothesis testing terms, are the mean number of cigarettes smoked per month

equal or not equal for those individuals with and without major depression?

The explanatory variable here is categorical with two levels,

that is the presence or absence of major depression.

The response variable, Smoking Quantity,

measured by the number of cigarettes smoked per month,

ranges from 1 to 2940.

First, we have the LIBNAME statement, which calls in or

points to the course data set, and tells us where to find the data.

Next, the data step begins with the DATA command.

And then it read in the specific data set.

In this case, the nesarc data.

We also added LABELS to help read the output easily.

And set appropriate missing data.

Then we created secondary variables measuring

usual smoking frequency per month.

Number of cigarettes smoked per month.

And also packs of cigarettes smoked per month, as both a quantitative and

categorical variable.

Just before the end of the data step, the data was subset to observations

including young adult smokers who had smoked in the past year.

We concluded the data step by sorting the data by the unique identifier.

As you know, all procedures that follow the data step,

which is the code located between the data statement and

the PROC SORT statement, are written to request specific output or results.

So we'll need to add the syntax for

analysis of variants after the PROC SORT statement.

To conduct and analysis of variance, we're going to use the PROC ANOVA procedure.

We start with PROC ANOVA; and we follow this with the CLASS statement.

>> The CLASS statement identifies the categorical explanatory variable.

The name of my categorical explanatory variable is MAJORDEPLIFE.

So I include this after the word CLASS and as always,

end the command with a semi-colon.

>> Next we write a MODEL statement naming the quantitative response variable.

In this case, that's NUMCIGMO_EST then the equal sign, and

then the categorical explanatory variable ,which is MAJORDEPLIFE,

and end the statement with a semicolon.

Finally, the MEANS statement tells us which groups you would like to compare

mean number of cigarettes smoked per month.

So again, we include the categorical explanatory variable,

MAJORDEPLIFE, and end the statement with a semicolon.

Don't forget, run semicolon.

So now we're ready to run the program and take a look at the output.

>> Proc ANOVA first displays a table that includes the following.

The name of the variable in the class statement.

The number of different values or levels of the class variable.

The value of the class variable.

And the number of observations in the data set and the number of observations

excluded from the analysis because of missing data, if any.

So here we see our categorical explanatory variable, MAJORDEPLIFE, as two levels.

And the values are 0 and 1.

Of the 1706 observations, 1697 were included in the analysis.

Proc ANOVA then displays an analysis of variance table for the response variable,

also known as the dependent variable from the MODEL statement.

>> In this case, our response or dependent variable was NUMCIGMO_EST.

Our calculated F statistic, called the F Value in this output, is 3.54.

The significance, probability, or

P value associated with this F statistic, is labeled Pr > F.

And as you can see, the P value is .0601,

just over our P value .05 cut point.

If we look at the means table,

we see that young adult smokers without major depression,

as indicated by a value of 0, smoke an average of 312 cigarettes per month.

And that those with major depression, indicated by a value of one,

smoke on average 341.5 cigarettes per month.

Because the P value is greater than 0.05, actually 0.06,

we must accept the null hypothesis and say that these means are statistically equal.

And that there's no association between the presence or

absence of major depression in the number of cigarettes smoked per month among

young adult smokers.

>> If I chose to reject the null hypothesis,

I would be wrong six out of 100 times.

And again, by normal scientific standards, this is not adequate certainty to

reject the null hypothesis and say that there is an association.

Instead, we're going to accept the null hypothesis and

say that there is no association.

Had the P value been less than .05, I would know that there was

a significant association and to interpret that as significant.

I would look at the means table,

if P would've been less than .05, I can see that individuals

with major depression smoke more than individuals without.

And again, with a significant P value,

I could have said that young adult smokers with major depression smoke significantly

more cigarettes per month than young adult smokers without major depression.

>> So, we've shown you the ropes in terms of a categorical explanatory variable that

has two levels, as it did here with depression.

For this interpretation, all we need to know is the P value and

the means for each of the two groups.

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