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STAT 200 Lesson 3 Lecture Video - Duration: 45:41.
Welcome to the lesson 3 lecture video.
This is the second of two lessons on describing data.
These are the eight learning objectives that we'll be covering this week.
We will be using Minitab Express a fair amount.
The hand calculations that you'll need to do in this lesson are limited
to using the interquartile range method to identify outliers,
and computing residuals.
All of the other calculations and graph construction we'll do in Minitab Express.
Let's get started with the first learning objective:
Construct and interpret a box plot and side by side box plots.
At the end of lesson 2 you learned about five number summaries.
The values from a five number summary can be used to construct a box plot.
Here we have a horizontal box plot.
You might also hear this referred to as a box-and-whisker plot.
The stars on the left and the right side are outliers.
We'll see you later how Minitab Express determines which points are outliers.
The minimum for this distribution is going to be the point on the far left.
Q1, or the first quartile, is the 25th percentile and this is the bottom of the box.
The median is the line in the middle of the box.
Q3, the third quartile, or 75th percentile is the upper end of the box,
and the maximum is the point on the far right.
The box represents the middle 50% of observations.
The lines extend to the lowest and highest points
that are not considered outliers.
The orientation of this particular box plot is horizontal,
I did that so would line up with our five number summary.
The default in Minitab Express, though, is make a vertical box plot, such as this one.
This is the same box plot that we just saw, but now our orientation is vertical.
At the bottom we have the minimum.
The bottom of the box is the first quartile.
The median is the line in the middle of the box.
The top of the box is the third quartile,
and the maximum will be the highest point.
This is a single box plot because we have just one quantitative variable and just one group.
We will also construct and interpret side by side box plots in this course.
Here we have side-by-side box plots comparing the heights of females and males.
A side-by-side box plot is used when you have one quantitative variable,
in this case height,
and one grouping variable,
in this case our data are grouped by biological sex.
This plot allows us to compare these two groups in terms of their five number summary values.
We're usually most interested in the boxes.
Here we can see that Q1 for females is lower than Q1 for males.
The median for females is lower than the median for males.
And, Q3 for females is lower than Q3 for males.
The size of the boxes, so the height in this case, is approximately equal for females and males.
This means that their inner quartile ranges are similar.
This is a measure of variability.
We could summarize these box plots by saying
that the central tendency is higher for males than for females,
but variability is similar for the two groups.
I'll take you to Minitab Express now to show you how I made these plots.
Here I have data collected from a sample of World Campus STAT 200 students.
In this example we'll be using the height in inches variable
and the biological sex variable.
We'll start with a single box plot of the height variable.
I'm on a PC right now but the instructions are the same if you're using a Mac.
We'll go to graphs and boxplot.
What minitab express is referring to as the y-variable here is
the quantitative variable that's being graphed.
I have a single Y variable with
one group, so this is a simple box plot.
I'll select the height variable and click OK.
Here's our box plot.
Below our box plot,
by default Minitab Express gives us some summary statistics.
You'll never have to change any of the graphing settings in this course,
but I'll show you how you could.
If I click on the graph, these two buttons appear.
The first button lets me change the orientation from vertical to horizontal.
The second button lets me change the titles, labels,
and it lets me add gridlines or reference lines.
Next I'll make a side-by-side box plot.
Again I'm going to graphs, box plot.
We still only have one quantitative variable,
but this time we have groups.
The y-variable is height
and the group variable is biological sex.
When we click okay,
this gives us our side-by-side boxplots.
We still have the ability to change the orientation
and many of the graphing elements.
Let's go back to the PowerPoint slides now to move on to the second learning objective.
Our second learning objective is to use the IQR method to identify outliers.
We started learning about outliers last week.
Outliers are values within a data
set that fall outside the general scope of the other observations.
In lesson two, you identified outliers by visually inspecting a dot plot or histogram.
Here you're going to learn a more objective method.
The IQR method uses the interquartile range.
This is the range of the middle 50% of observations.
Mathematically, the interquartile range is equal to the third quartile, which is
the 75th percentile, minus the first quartile, which is the 25th percentile.
The IQR method is the method that Minitab express uses when constructing boxplots.
All of the stars that you saw on the box plots that we just made were
outliers that were identified using this method.
The IQR method starts by setting up fences outside of Q1 and Q3.
Any observations that fall outside of those
fences are considered to be outliers.
Each fence is 1.5 times the interquartile range.
Any observations that are beyond Q1 -1.5 times the interquartile range
or Q3 + 1.5 times the interquartile range
are considered to be outliers.
When I say beyond, I mean less than
the lower fence, so less than Q1 minus 1.5 times IQR,
or greater than the upper fence of Q3 plus 1.5 times IQR
There are other objective ways to identify outliers
but this is the only one that we'll cover in this course.
This is the method that Minitab Express uses.
Let's look at one example
Here we have a list of quiz scores from 16 students.
We want to identify any scores that are outliers.
Just from looking at the distribution we
can guess what the lowest score for will probably be an outlier.
I'm not sure if eight or ten will be outliers or not
so we'll use the IQR method to objectively
identify all of the outliers in this distribution.
To do this we need to know q1, q3, and the IQR .
We didn't find quartiles by hand yet,
but the process is very similar to what we did to find the median last week.
We'll start by finding the median,
then q1 will be the middle of the lower half
and q3 will be the middle of the upper half.
With 16 observations, the median will be between the eighth and ninth observation
which is between the second and third score of 14.
Q1 will be the middle of the lower half of the distribution.
The midpoint here will fall between 11 and 13,
so Q1 will equal the average of 11 and 13
which is 12.
Q3 will be the middle of the top half of the distribution.
The midpoint here falls
between two scores of 15
so Q3 equals 15.
Our interquartile range
is equal to q3 minus q1.
15 minus 12 equals 3.
To set up our fences we need to multiply 1.5 times the interquartile range of three ,
This equals 4.5
So our lower fence
will be q1 minus 1.5 times the IQR.
12 minus 4.5 equals 7.5
Any value in this distribution less than 7.5 will be an outlier.
To find the upper fence,
we take Q3 plus 1.5 times the IQR,
giving us an upper bound of 19.5
There are no values in this distribution greater than 19.5,
so we have no outliers on the upper end.
Our third learning objective is to construct and interpret histograms with
groups and dot plots with groups.
We already saw how we could use
side-by-side box plots display data concerning one quantitative variable and
one categorical variable.
Both of these graphs are also used when we have a
quantitative variable and a categorical variable.
You were introduced to histograms and dotplots last week with just one group
now we're adding another dimension,
and this is a categorical grouping variable.
Both of these graphs compare the heights of females and males.
If we look more closely at the histogram with groups,
we have a histogram for females on the left and a histogram for males on the right.
The nice thing about these charts be the Minitab Express is
that they'll keep the scales on the bottom the same for both groups.
Here, for females and males it goes from about 50 to 80 even though there are no
females above 74 and no males below 56.
The same is true with the dot plot.
In most cases if I'm trying to compare groups I prefer the dot plot with groups.
I think it's easier to make a comparison when the groups are on top of each other
as opposed to left and right in the histogram.
On the top here we have a dot plot for females
and on the bottom a dot plot for males.
I think it's easier here to see that the distribution for females
tends to be lower than the distribution for males.
I'll take you to Minitab Express now to show you how you can make these graphs.
We'll start with the histogram which will be under graphs,
we now have a grouping variable so we have a single y variable with groups.
The y-variable is the quantitative variable, in this case Heights.
The group variable is the categorical variable, in this case biological sex.
Now we'll makes a dot plot with groups.
Again, graphs dot plot a single y variable with groups.
The y-variable again is height
and the group variables biological sex.
I'm actually not going to take you back to the PowerPoint slides right away.
We're going to stay here in Minitab Express for the beginning of our next learning objective.
The fourth learning objective is to construct and interpret a scatterplot.
A scatterplot is used to display data concerning two quantitative variables.
Later in the course we'll spend an entire lesson analyzing two quantitative variables.
This week we'll just introduce some of the basics.
To construct a scatter plot I'll go to graphs
scatterplot
we're making a simple scatterplot which
is one with just two quantitative variables.
When constructing a scatterplot the Y variable is the response variable and
the X variable is the explanatory variable
if you're in a scenario with a response and explanatory variable.
In Lesson 1 we learn that the explanatory variable
is used to explain variability in the response variable
or the explanatory variable is used to predict the response variable.
In this example I want to use height to predict weight
so weight is the Y variable
and height is the X variable.
I'll take you back to the PowerPoint slides now to interpret scatterplots.
These are the four things that we look at when we're interpreting a scatterplot.
First, the direction.
A positive relationship means that we're
going from the bottom left to the upper right.
In other words, the slope of the line of best fit, which we'll learn about soon, is positive.
A negative relationship would be a pattern that moves from the upper left
to the lower right.
A flat horizontal pattern would tell us that there's no relationship at all
Second, is the form or the shape of the line of best fit.
If the line of best fit is a straight line
then we have a linear relationship.
In this course we're going to focus on these linear relationships.
It is possible to have a nonlinear relationship.
For example, sometimes we see this a relationship between anxiety
and performance
is curvilinear.
So as anxiety increases to a point performance increases
but then if anxiety is too high performance can decrease.
This is one common example of a nonlinear relationship.
The third thing that we look at is the strength of the relationship.
For this we look to see how close the observations are to the
line of best fit.
If all of the observations are in a straight line then
the relationship is very strong.
If the observations are more spread-out,
then the relationship is weaker.
The last thing that we look for are bivariate outliers.
Bivariate means two variables.
A bivariate outlier is an observation that does not fit with the
general pattern of the other observations.
For example, if we have a moderately positive relationship between x and y
but then we have one point way over here.
That point would be a bivariate outlier because it doesn't fit
with the general pattern of all of the other points.
Let's walk through one
example of interpreting a scatter plot using the plot that we made in Minitab
Express with quiz averages and final exam scores
The direction of the relationship here is going from bottom left to upper right
so this is a positive relationship.
The form is linear
because if I were to draw a line of best fit
that would be a straight line.
I would describe the strength as moderately strong [and positive].
The points are not too close to
this line the best fit but they're also not too far apart,
and there are no bivariate outliers ,
all of the points are following the same general upward pattern.
Our fifth learning objective is to compute and interpret a correlation.
There are lots of different correlations, but in this course we're only going to be working with Pearson's r.
There are two conditions that must be met to use Pearson's r:
there needs to be exactly two quantitative variables
and there must be a linear relationship
Before computing a correlation we often make a scatterplot
to confirm that the relationship is linear.
Here are some properties of Pearson's r that you should be aware of:
First, Pearson's r must be between negative 1 and positive 1.
For a positive association r will be greater than zero,
for a negative association r will be less than 0, and
for no association r will equal zero.
In other words the sign of the correlation tells us if it's a positive or negative relationship.
The closer to 0 the weaker the relationship.
The closer to negative 1 or positive 1, the stronger the relationship.
So when we're judging the strength of a relationship
we're looking just as a number.
The sign only tells us if it's a positive or negative relationship.
For example, a correlation of negative 0.8 is actually stronger
than a correlation of positive 0.6
because the strength only refers to the number and not the sign .
Correlation is unit free
this means that the X and y variables do not need to be on same scale.
For example, we can compute the correlation between height and inches and weight and pounds.
The fact that one is being measured in inches
and the other in pounds is not a problem.
The correlation will standardize both variables.
It does not matter which variable you label as X and which you label as .
With correlation, the X and y variables are interchangeable.
The correlation between x and y
is equal to the correlation between y and x.
You can use this table as a guideline for interpreting the strength of a correlation.
In the first column we have the absolute value of r,
this means that we're just looking at the number
and not the positive or negative sign.
The number in the correlation gives us information about the strength of the relationship.
Here are a few cautions with using Pearson's r.
Some of these are common misconceptions or common mistakes.
Correlation does not equal causation.
A strong relationship between x and y does not mean that X causes Y.
We learned a bit about this in lesson 1.
In order to make a causal conclusion we need to have a well-designed experiment.
Pearson's r should only be used with linear relationships.
There are other correlational methods out there that can be used with nonlinear relationships
by Pearson's r as we're using it in this course
should only be used with linear relationships.
We often construct a scatter plot before computing the
correlation to confirm that the relationship is linear.
And third, Pearson's r may be heavily influenced by outliers.
Here are two scatterplot.
The only difference between these two plots is this one point.
Depending on where the outlier is it can make the correlation stronger or weaker.
In this case this outlier makes the correlation stronger.
If this point were at a different location, for example here,
that would make the correlation weaker.
There are a number of different versions of the formula for computing Pearson's r,
they're all really the same formula but some are in terms of z-scores while
others are in terms of means and standard deviations.
You should get the same correlation value regardless of which formula you use.
Note that you will not have to compute Pearson's r by hand in this course,
These formulas are presented here to help you understand what the value means
I prefer the top formula here because it shows Pearson's r in terms of z-scores.
So the sample correlation is
roughly the average of each cases z-score for the X variable
times their z-score for the Y variable.
When I've taught graduate level
introductory statistics courses in the past I used to make students calculate
these by hand because I really think it helps you see where the correlation
values are coming from.
Just from looking at the top part of the formula,
this will be a positive number if the two z-scores are both positive
or if the two z-scores are both negative.
In other words if someone is above the mean on the X variable
and above the mean on the Y variable.
This sum will be negative if a lot of
the observations have z-scores for x and y that are opposite signs.
So someone who is above the mean on X is below the mean on Y
and vice versa.
The stronger x and y increase or decrease together,
the stronger the correlation coefficient r will be.
I'll take you back to Minitab Express now to show you how to compute the correlation
coefficient there.
To compute a correlation coefficient on a PC,
we'll go to statistics, correlation, correlation.
If you're on a Mac you'll go to statistics,
regression, correlation.
Here you can actually select multiple variables.
First, I'll show you what it looks like with two variables. We'll do height and weight.
The order that you enter in the variables here does not matter.
For method will always be using the Pearson correlation method.
We click ok.
And this is what the output will look like with two variables.
You'll be given the correlation coefficient and a p-value.
The p-values we won't start to work with until Lesson 5.
So for right now, you just need to be able to obtain the
correlation coefficient.
Here, 0.487334
The output will look different if you enter in more than two variables.
I'll walk you through an example of this.
Again I'll go, to statistics, correlation, correlation,
but this time I'll enter more variables.
I'll do height weight, best marriage age,
and hour per week watching TV.
And here we get an error message: all columns for
this analysis must contain the same number of rows.
This message usually means that there's some missing data in the last row.
So we'll cancel out here
and I'll scroll down to the last row of data.
If we look across row 501 we can see that this person did not answer the
last two questions on the survey.
There are different ways to deal with missing
data like this but that's not usually covered until an intermediate or more
advanced statistics course.
For this example I'm going to delete this last row
so that I can get this correlation to run.
I'll go back to correlation
and again I'll select my variables
and we can see now we no longer get that error message.
Here we have a correlation matrix.
The key at the bottom,
tells us that in each cell the top row is Pearson's correlation
and the bottom row is the p-value.
This table is giving us every possible two-way combination, in other
words every possible pair of variables.
To find the correlation between weight and height,
we would look across the weight row
and down the height column.
The correlation between weight and height is 0.488299
This is a moderately strong positive correlation.
To find the correlation between best marriage age and height
we would go across the best marriage age row
and again down the height column.
The correlation between best marriage age height is -0.036445
This is a very weak negative correlation.
Let's look at one more example.
Let's find the correlation between best marriage age and hours per week watching TV.
We find the row in the column or these two variables meet
and the correlation is 0.002016, again a very weak correlation.
We'll go back to the PowerPoint slides now to introduce
simple linear regression.
Our sixth learning objective is to construct and interpret a simple linear regression model.
I this course we'll be using Minitab Express
to construct simple linear regression models
and we'll really be emphasizing that in Lesson 12.
The interpretation aspect is most important here.
In this course we'll be learning only about simple linear regression.
The simple part is that we have only one explanatory variable and
one response variable.
Our explanatory variable will be referred to as X,
and our response variable will be referred to as Y.
In regression it does matter which variable we call X and which we call Y.
The X variable is always used to predict the Y variable.
Both x and y must be quantitative.
The linear part of simple linear regression
is that we're going to be computing a straight line.
You may recall from an algebra class y=mx+b,
where m is your slope
and B is your y intercept.
The slope is a measure of how steep the line is.
In algebra, it's sometimes described as change in Y over change in X.
The triangle symbol here is Delta
where Delta means change.
This could also be written as
rise over run.
A positive slope
indicates that the line is moving from the bottom left
to the upper right
and a negative slope would be the opposite.
The y-intercept is the
point where the line crosses the y-axis.
This can also be described as the value
of y when x equals zero.
While y=mx+b is often used in algebra,
the notation and statistics is slightly different. For example, your book would
write this as y hat=a+bx,
where the y-intercept is a,
and the slope is b.
The Y here has a hat because it's a predicted Y value.
Putting a hat on something means that its predicted or estimated.
The y intercept is always going to be the value sitting by itself,
and the slope will always be the value attached to X.
For a population this may be written as y hat equals alpha plus beta X.
You could also see it written as Y hat equals beta sub 0 plus beta sub 1times X.
We won't work with these populations formula until Lesson 12.
I'll take you back to Minitab Express to construct a simple linear regression line
and to interpret it.
I actually want to use a different data set this time,
one that's smaller.
This is the exam dataset from the online notes it contains
the quiz averages and final exam scores for 50 students.
The steps for obtaining a regression equation are the same on a PC and Mac
we'll go to statistics
regression
simple regression.
For regression it does matter what we call the X and y variables.
X is used to predict Y.
Let's use quiz averages to predict final exam scores,
so final exam score is the Y variable
and quiz average is the X variable.
In this course we'll always be using the linear model.
At this point you can ignore the analysis of variance table,
model summary and coefficients tables.
We won't work with any of these until lesson 12.
What we want are the regression equation which here is
predicted final equals 12.12 + 0.7513 times the quiz average.
You can ignore the fits and diagnostics table for now,
but we will look at the scatter plot that we get with the regression line drawn on it.
Let's interpret the key features of this regression model.
We can start with the slope.
There are two different ways that we can know that the slope is positive,
first, we can look at the regression line we can see that it has an upward slope.
Second, we can look at the regression equation,
here we can see that the slope
is greater than zero so we have a positive relationship.
For the y-intercept the value from our regression equation is 12.12
this means that someone with a quiz average of zero has a predicted
final exam score of 12.12
In this particular case that doesn't mean much.
If we look at our scatterplot we can see the lowest quiz average was in the 60s.
If I add reference lines here,
you can see that Minitab Express does not extend the line beyond the data.
That would be known as extrapolation, which we'll see shortly.
You shouldn't try to interpret your regression line beyond the range of the data that you have.
We don't have data for lower quiz scores so we don't know if the
relationship would even be linear in that area.
I'll take you back to the
PowerPoint slides now to show you the method that Minitab Express using to
come up with this regression equation.
The sixth and seventh learning objectives run together a bit here.
The seventh learning objective is to compute and interpret residuals from a simple
linear regression model.
The methods that Minitab Express uses to construct its
simple linear regression equation is the least squares method.
The least squares method computes the values of the slope and y-intercept that make the sum of
squared errors SS e as small as possible.
Error is noted by E, this is also known
as the residual.
E is equal to Y minus y hat,
in other words it's the difference between an observed y value
and the Y value predicted using the regression equation.
If you're looking at the scatter plot,
this is the vertical distance from each point
to the regression line.
If a point is above the regression line it will have a positive residual.
If a point is below the regression line it will have a negative residual.
Let's look at this point.
It looks like it's at about 0.5 and 8.
So their x-value was 0.5,
and their observed Y value was 8.
The residual is y minus y hat.
We have Y and we can compute y hat using the given regression equation
y hat equals 6.5 - 1.8 times x, which here is 0.5
This equals 7.4
Their residual is equal to the observed y value of 8 minus the expected y-value
of 7.4 this equals 0.6
If we were to draw a vertical line from the
point down to the regression line the height of that line would be 0.6
The sum of squared errors, or SSE, is
equal to the sum of all of those residuals squared.
You might also see
this written as the sum of every Y minus y hat squared
You won't need to compute the sum of squared errors by hand,
Minitab Express will do these calculations for you.
You may be asked to compute the residual though,
if you're asked for a residual that is e.
A few last words on an objectives six and seven before we move on,
these are cautions.
First, avoid extrapolation.
We saw that Minitab Express
will only draw the regression line within the range of observations.
This is because the regression line should not be used to make predictions
about individuals who are outside of the range of the original data set.
For example, if you had a regression model using height to predict weight in a
sample of preschool children, you wouldn't use that same model to predict
the height of an adult.
If the regression equation was constructed using data from
preschoolers then it should only be used with preschoolers.
Second, make a scatterplot to check for linearity.
If the relationship between x and y is not linear
then you shouldn't be using linear regression methods.
And third,
outliers can heavily influence the regression model.
We saw these plots with correlation as well,
when this one outlier was removed the slope went from
being positive 1.02 to 0.044.
Our last learning objective is to interpret plots of more than two variables.
This is probably the most difficult objective for me to cover in this video because
there's really an unlimited number of possible visual displays that could be
used with more than two variables.
There are three plots that we'll cover here,
these are the three that are covered in the online notes: scatter plots with groups
bubble plot and time series plots.
But know that there are many more
different options out there and you might see some of them in your WileyPLUS
homework assignment this week or in real life.
Let's start with scatter plots with groups.
We made a simple scatter plot earlier that had
two quantitative variables displayed on the x-axis and y-axis, this is height and
weight, now we're adding a third variable and that will be a categorical variable.
Here the different markers signify females and males.
The blue circles are females
and the red squares are males.
This scatterplot has two quantitative
variables height and weight
and a third variable a categorical variable of biological sex.
We can pop over to Minitab Express so I can show you how
this plot was made.
On a PC or Mac you'll go to graphs,
scatterplot, single y variable with groups.
Our Y variable was weight and our X variable was height.
The group variable is the categorical variable biological sex.
We can see that overall there is a positive relationship between height and weight.
We can also see that females tend to be lower on both variables and males
tend to be higher on both variables.
The next graph that we'll look at is a bubble plot
This is one that Minitab Express will not make.
Bubble plots can be used to display data from three quantitative variables.
You can also add a categorical grouping variable.
The bubble plot here I made using the
statistic software program R.
We have the height and weight variables again
the size of the bubbles is determined by a third quantitative variable and that
is the number of days per week exercised.
So the larger bubbles are people who
exercise more often.
There's another example that I want to show you on this website.
You won't have to make bubble plots in this course but this site does
provide some instructions.
What I want to show you here is their first bubble plot.
This has per capita GDP on the x-axis
and life expectancy in years on the y-axis .
The size of the bubble is the country's population and the color of the bubble is the continent.
This lets us see the relationships between all four
variables at the same time.
We can see that there's a positive correlation
between per capita GDP and life expectancy
for the mid-sized and larger countries.
The relationship may not exist for the smaller countries over here we
have smaller countries with lower GDP s but longer life expectancies.
We'll go back to the PowerPoint slides now to look at some time series plots.
Time series plots can be made in Minitab Express
with or without a categorical grouping variable.
We're not going to do any time series analyses in this course
so we're not going to be making these plots I just want to show you a couple
of examples of how to interpret them.
The first example is a simple time series plot that I got from a Minitab blog.
On the x-axis we have the year and on the y-axis the price of gold.
The measure of time will always go on the x-axis.
On this plot we can see that prices were
low and stable from 1900 with a little change in the mid-1930s
and not a lot of changes starting in the 1970s.
Here's a time series plot with groups
that I took from a different Minitab blog.
Time is on the x-axis but
here we're looking at months instead of years.
The quantitative response variable
is on the y-axis which they're just calling data ,
and then we have blue circles and a blue solid line for company A,
red squares and a red dashed line for Company B.
This lets us compare the two companies over time.
If we had just looked at the mean for A and the mean for B,
those means probably would have been similar.
But here we can see that Company B is on a steeper upward
trend than Company A.
This concludes the Lesson 3 full video lecture,
if you have any questions after watching this video,
please post them to the Lesson 3 discussion board in Canvas.
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way down to two years old we are at the stage in in our family's life that we
wanted experiences time together to mean more than things things we could buy or
do we have been entrepreneurs by trade and got into doing a lot of real estate
hence I was looking at real estate investments and found equity residents
and very pleased with it thus far
with four kids we found quickly that hotels just don't work for us so we've
been using VRBO and rental homes for some time and finding that it's hit and
miss you get you get some good ones and you get some bad ones and I love the
idea of having really nice well-equipped homes that I had ownership in that I
could go stay in as part of a benefit as an investment
if you're an investor in your and you're getting to the point where you you have
additional cash you want to get a second home you want to have those vacations to
me too to spend the kind of money that it takes to have a second home and then
to have to take someone to clean it to deal with the management of it
to try to rent it yourself it becomes a big amount of work this is much better
to have equity residents to be able to go to different places to be able to
have different experiences and have somebody else take care all that work is
a huge driver to me looking at these funds I was really leery to make sure
that they were truly investment you know obviously you do get the benefit of
having places that you can go take your family and have amazing vacations which
was really fun and appealing to me as an investor but I wanted to make sure it
was a true investment and and not a gimmick and definitely it's turned out
to be fantastic for
I think one of those scarier things when I was looking at some of the other funds
some did have some more homes already on on their funds because they were older
funds but they were also costing more so I didn't know exactly how to reconcile
that until I learned about the partnerships that equity residence has
with elite Alliance and third home when we started looking at these vacation
opportunities we wanted to go have adventures as a family and this gave us
the best opportunity to do that and to feel like not only could we go to
amazing places and see amazing homes we were being driven to places we might not
have thought of originally
yeah I think one of the main things that I looked at was of course I loved the
fund I loved that it had an end time range you know it's gonna be ten years
it was there to make money but you never know when one year you might have to do
something else or something might come up and the kid might get sick you don't
know and some of these other funds you were still gonna have to pay a yearly
fee and not be able to do anything about it but with equity residents I could say
you know what I can't vacation this year I'd like to turn that time into extra
dividends for me and I thought that was fantastic I mean great flexibility
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