The data set in ALCOHOL contains information on a sample of...

The data set in ALCOHOL contains information on a sample of men in the United States. Two key. variables are self-reported employment status and alcohol abuse (along with many other variables). The variables employ and abuse are both bindicator, variables: they take on only the values) zero and one. (i) What is percentage of the men in the sample report abusing alcohol? What is the employment rate? (ii) Consider the group of men who abuse alcohol. What is the employment rate? (iii) What is the employment rate for the group of men who do not abuse alcohol? (iv) Discuss the difference in your answers to parts (ii) and (iii). Does this allow you to conclude that alcohol abuse causes unemployment?

The data set in ALCOHOL contains information on a sample of men in the United States. Two key.
variables are self-reported employment status and alcohol abuse (along with many other variables).
The variables employ and abuse are both bindicator, variables: they take on only the values)
zero and one.
(i) What is percentage of the men in the sample report abusing alcohol? What is the employment rate?
(ii) Consider the group of men who abuse alcohol. What is the employment rate?
(iii) What is the employment rate for the group of men who do not abuse alcohol?
(iv) Discuss the difference in your answers to parts (ii) and (iii). Does this allow you to conclude
that alcohol abuse causes unemployment?

Key Concepts

Binary (Indicator) Variables

These are variables that take on only two possible values, typically representing the presence or absence of a characteristic (such as employed/unemployed or abuse/no abuse). They are fundamental in statistical analyses when categorizing observations and are often used to simplify data analysis and modeling of relationships between different factors.

Descriptive Statistics

This involves summarizing and describing the main features of a dataset. In the context of the question, descriptive statistics are used to calculate percentages or rates such as the percentage of men who abuse alcohol or the employment rate within the sample. These summaries help in understanding the overall distribution and central tendencies in the data.

Conditional Probability and Segmented Analysis

This concept refers to the computation of statistics for specific subgroups within the data, such as the employment rate among men who abuse alcohol versus those who do not. It is important for analyzing how the presence or absence of one characteristic may alter the likelihood or rate of another outcome within different sections of a dataset.

Causality Versus Association

This concept highlights the distinction between a mere statistical relationship and a cause-effect relationship. Even if an association is observed between two variables (for example, alcohol abuse and unemployment), it does not necessarily indicate that one causes the other. Establishing causality requires more rigorous analysis, often involving controlled experiments or additional statistical methods to rule out confounding factors.

Transcript

00:02 Was done with chapter one, just one more question to go.

00:03 And today we'll answer computer question seven.

00:08 We're using the data set called alcohol, and it contains information on a sample of men in the united states.

00:15 Two key variables are self -reported employment status called employ and alcohol abuse, along with many other variables.

00:22 Right? so the main variables we keep track of is employ and abused, and they're both binary or indicator.

00:30 Dummy variables, whatever you want to call them.

00:32 So they take only the values of zero and one.

00:34 We don't want to take this for granted.

00:38 So we're going to describe our data set and see what's going on.

00:41 Indeed, we have 9 ,822 observations, 3 ,300 line variables.

00:48 And let's see, abuse indeed is a binary variable equal to one if the person has reported to abuse alcohol.

00:57 And the other one is employee right here.

01:00 Equals to 1 if a person is employed or an equal to 0 if the person is unemployed.

01:07 In part a, we're being asked to find the percentage of the men in the sample that report abusing alcohol.

01:14 So what you need to do is to count, first of all, how many of the binary variables are equal to 1? it's 974 men have reported abusing alcohol.

01:27 And in order to find the percentage of men in the sample reporting and using, we need to divide this number by the total number of men, which is equal to the total number of observations.

01:38 In this case, 9 ,820.

01:46 And if we do time to look at here, let's do it times a hundred to get the right answer right away.

01:52 It's 9 .91 % of men have reported using alcohol, a little bit less than 10%.

01:58 All right.

02:00 Next, we're being asked, what is the employment rate? i assume this means a general employment rate.

02:06 So what we need to do is counting the employee variable is equal to 1.

02:12 So this is a total number of men who have reported being employed.

02:16 And we need to divide the number, 882, by the total number of men in the sample, multiplied by 100.

02:28 And we get 89 .81, almost 90 % of men are employed in the sample, in the general population, right, without considering alcohol abuse or not.

02:41 The employment rate is 89 .81%.

02:46 Now, in part b, consider the group of men who abuse alcohol.

02:50 What is their employment rate? okay.

02:52 Now, we need to have to calculate a ratio with the numerator will be the men who abuse alcohol and are employed divided by the number of men who abuse alcohol.

03:09 So we have found the number of men who abuse alcohol is equal to 974.

03:15 Now we need to count how many men abuse alcohol and this is a symbol for an end and theta.

03:23 And our employee, 850...

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