2-1.3. Two-Tail Mean Test for Two Samples and Calculating the Confidence Interval in Excel

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来自 University of Illinois at Urbana-Champaign 的课程

Inferential and Predictive Statistics for Business

204 个评分

University of Illinois at Urbana-Champaign

Inferential and Predictive Statistics for Business

204 个评分

课程 6（共 7 门，Specialization Managerial Economics and Business Analysis ）

This course provides an analytical framework to help you evaluate key problems in a structured fashion and will equip you with tools to better manage the uncertainties that pervade and complicate business processes. The course aim to cover statistical ideas that apply to managers. We will consider two basic themes: first, is recognizing and describing variations present in everything around us, and then modeling and making decisions in the presence of these variations. The fundamental concepts studied in this course will reappear in many other classes and business settings. Our focus will be on interpreting the meaning of the results in a business and managerial setting. While you will be introduced to some of the science of what is being taught, the focus will be on applying the methodologies. This will be accomplished through use of Excel and using data sets from many different disciplines, allowing you to see the use of statistics in very diverse settings. The course will focus not only on explaining these concepts but also understanding the meaning of the results obtained. Upon successful completion of this course, you will be able to: • Test for beliefs about a population.. • Compare differences between populations. • Use linear regression model for prediction. • Learn how to use Excel for statistical analysis. This course is part of the iMBA offered by the University of Illinois, a flexible, fully-accredited online MBA at an incredibly competitive price. For more information, please see the Resource page in this course and onlinemba.illinois.edu.

从本节课中

Module 2: Statistical Inference Based on Two Samples

Does the medicine a person is taking to treat his condition really work better than a sugar pill? Is the new chip-enabled credit card more secure than the magnetic card? How do you know whether the claims being made about anything being “better than” or “faster than” a competitor are true? In this module we will learn to make this comparison.

2-1.1. Testing for No Difference in Means17:00

2-1.2. Testing for No Difference in Means in Excel9:41

2-1.3. Two-Tail Mean Test for Two Samples and Calculating the Confidence Interval in Excel6:32

与讲师见面

Fataneh Taghaboni-Dutta
Clinical Professor of Business Administration
University of Illinois, Urbana-Champaign College of Business Department of Business Administration

[SOUND]

I'm going to show you how to use data

analysis and the key tasks for two samples.

First, to do the hypothesis testing as well as how to come up with

the confidence interval for the difference we see between two population.

The manager of a store would like to know if the average sales measured in dollars

through her websites are any different than her in store sales.

The data has been collected and

we want to do an analysis of 5% level of significance.

So first of all all she's wondering is that is there a difference between online

and in stores?

So a non hypothesis is that there is not difference between the two, right?

This is their online sales is the same thing as the store sale and

the alternate would be that no, they're not the same.

So this is what we're checking, and again, what I said was that if you write it like

this might be easier for you to see what we're doing so

now we're going to say that the difference between the two, so

if I have to take the two mus, I am saying that these are equal to zero.

Or it is not equal to zero so this is the online and

this is the store, my handwriting isn't so good so I'm not going to follow it.

Now let's go look at our data and do the analysis.

So here's our data, these are dollars that has been recorded at end of day for

several days that we have taken the observations from online and in-store.

So first thing you want to do is go to Data > Data Analysis > Select t-Test,

again, Unequal Variances.

I will assume that, I don't want to simplify it.

So then I say OK.

And it will ask for range one, and I have shown you that you can click here and

do Ctrl+Shift+Down.

You could actually do this quicker by just selecting the column.

This will work in this type of analysis.

If it works only in few places,

not all the time you can use this kind of a selection.

But here you can and that will speed up the selection process.

So then the second variables appear in the B column.

I assume that there is no differences.

I have no preconceived idea.

I'm going to say that we have labels.

I'm going to select the output range.

Click here, click somewhere here, and then say OK.

And here we go, so once again I know that

I am doing a two-tail test because it was equal versus and inequality.

So what I need to do is that I just need to focus

on the two-tail part of this output.

And that would be right here.

So now looking at the p value, the p value is less than 0.05 so

I will end up rejecting the null hypothesis which was the two are the same.

So now that I know that they're different, I want to know what is the expected

difference between online versus the store sales.

So now I can go ahead and use the same output to develop the confidence

interval for the difference that exist between the two channels of sales.

So to develop the confidence interval of the difference between the two channels of

sales.

These are the mean differences between the two.

So let me highlight this in red.

But this is not enough so I cannot just take the difference between these two and

say this is what I expected difference to be.

There is a margin of error.

There is some variability from sample to sample.

So what we want to do is develop that entire difference.

So right now I can look at the mean differences between the two and

say this is x bar 1 minus x bar 2.

So I can develop this by saying this minus this value.

And by the way, it doesn't matter which one you do first, your

signs will change but the absolute value of the differences are still the same and

that's what we care about.

With either with say right now it looks like

mean sales in online is $105 less than what is in store.

If you did it the other way around,

this positive number would say that in store sales is $105 more than online.

So there's really no difference.

So going back to the formula that we have for the confidence interval of

the differences, the next thing that I need to find out is what is my t-value.

Now the confidence interval is always a two-tail.

Margin of error can occur plus or minus.

So the two tail here is 1.99.

That's my t-value so again, looking at our formula, the next thing that I

need in order for me to come up with the margin of error is the standard error.

And the standard error based on our equation is just

the square root of the variance of the first one divided

by its sample and in this case was 52 plus the second

sample's variance divided by its sample size.

And once I have the two I can use the square root and find the standard error.

Now I'm ready to calculate the margin of error.

Margin of error is simply your t value times the standard error.

And now I'm ready to calculate the lower bound and

the upper bound of the differences between these two channels.

The lower bound Is this value minus the margin of error and

upper bound is this value plus.

So essentially what they are saying is that you are 95% confident that

the difference that exist between sales online versus sales in store is that

the online channel can send an average about $18.80 per day up to $192.65 per day

less than what we have as sales for our in store.

Now as I have said before it is showing that is a significant difference,

a statistically significant difference between the two.

But in practice that could be as little as $18 difference between the two.

So as a manager you may decided that this is not important to you,

it's not enough differences.

So statistically significant does not mean that you have

found something meaningful at times.

Now if this number was thousands of dollars difference, then nobody would

argue that even in practice you would consider that as an important issue.

What is the threshold?

Where you as a businessperson will decide this is significant enough,

really depends on domain and the decisions that has go along with that domain.

So there is no standard answer for that.

This is where your expertise come into play and

it's not just about the statistical values that you're seeing.

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