Binary Regression

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来自 University of California, Davis 的课程

Quantitative Research

37 个评分

University of California, Davis

Quantitative Research

37 个评分

课程 3（共 4 门，Specialization Market Research）

In this course, you will obtain some insights about marketing to help determine whether there is an opportunity that actually exists in the marketplace and whether it is valuable and actionable for your organization or client. Week 1: Assess methods available for creating quantitative surveys, along with their advantages and disadvantages. Identify the type of questions that should be asked and avoid unambiguous survey questions. Week 2: Design, test, and implement a survey by identifying the target audience and maximizing response rates. You will have an opportunity to use Qualtrics, a survey software tool, to launch your own survey. Week 3: Analyze statistical models that can be applied to your marketing data, so that you can make data-driven decisions about your marketing mix. Week 4: Predict most likely outcomes from the marketing decisions and match the type of analysis needed for your business problem. Take Quantitative Research as a standalone course or as part of the Market Research Specialization. You should have equivalent experience to completing the second course in this specialization, Qualitative Research, before taking this course. By completing the third class in the Specialization, you will gain the skills needed to succeed in the full program.

从本节课中

Assess Approaches and Interpret Quantitative Study Results

In this module, you will be able to assess the various approaches for analyzing your data. You will be able to consider multiple variables throughout your analysis and be able to interpret your results. Like the last module, this module features a fair amount of advanced statistics related math. Several optional activities setup as practice quizzes have been provided to help reinforce what you're learning.

Assess Approaches and Interpret Study Results Introduction0:36

Regression and Errors6:39

Multiple Variables3:39

Using Multiple Variables3:47

Multiple Regressions: Understanding What Drives Pricing3:09

Binary Regression4:41

Segmentation and Cluster Analysis4:09

Factor Analysis4:53

Conjoint Analysis4:47

与讲师见面

Susan Berman
President, Impact Research
UC Davis Division of Continuing and Professional Education
Olivier Rubel, PhD
Associate Professor, Graduate School of Management
UC Davis

Welcome back. If you viewed the previous lesson of this course you've seen that

the examples we've use so far were such that the dependent variables were continuous.

In many cases, however,

marketing outcomes are not continuous and are instead

discrete like donating money or not to a charity.

By the end of this lesson,

you'll be able to discuss methods for analyzing how

marketing factors affect discrete consumer decisions.

First, let's look at the decision when the outcome is one or zero.

In this case, what you want to do is something called LOGIT or

logistic regression where the dependent variable

is going to be the logarithm of P over one minus P,

and the model is going to be A plus B1X1 plus B2X2 and so on.

So what is P here?

P refers to the proportion of people who decided to,

let's say, buy a house within a given period or not.

Another example could be the proportion of

people who decided to give blood within a market area.

You can see here that P and one minus P are going to be proportion that you'll use

to do the regression that measures the impact

of each of the variable on this proportion or probabilities.

In marketing, you often have situations

where what you observe are discrete decisions by customers or households.

Let's say that you have two brands in

the marketplace and customers can either buy brand A or brand B.

The first two observations relate to the same households for two purchase occasions.

That is, this household decided to buy a brand A.

The second household decided to buy brand B at two purchase occasions.

And household three decided to buy brand A in two purchase occasions.

The question is can I predict these choices and, most importantly,

can I understand how prices and

other marketing variables impact those choice probabilities?

If I decrease the price of B,

how likely am I going to observe someone switching from brand A to brand B?

And similarly, if brand A were to decrease its price,

can I predict the switching behavior of someone from brand B to brand A?

In such marketing model,

the question is what is the dependent variable and what are the independent variables?

It's a bit tricky because what we want to understand is

the impact of different marketing variables on the utility of consumers.

The assumption is that a given customer is going to choose brand A over brand B

if his or her utility for brand A is higher than for brand B.

The problem here is that utilities are

a latent construct that usually come from economic theory.

So that means that it's not directly observed by the marketing researchers.

It actually doesn't exist in reality.

So what we need to do is a way to indirectly measure the utility of the customer.

Formally, it means that you are going to say that

individual I has a utility of consuming product A.

That is a function of an intersect Alpha A plus Beta A times XI where

Beta A is going to be a vector of

all the parameter values that corresponds to the independent variable XIs.

Similarly, Alpha B is going to be the intersect

of customer I utility function for brand B.

And so we can see that Alpha A and Alpha B

could be some measure of brand values in the marketplace.

That would be the value that brand command

irrespectively of all the variables of choice prices.

From here, HN and AI and HN and BI are going to be

some error terms to which we are going to impose some parity distribution functions.

That is an assumption about how they evolve.

Long story short, what you want to do is work out the math to

find out what is the probability based on

the data that someone is going to choose brand A over brand B.

This particular function will be given by this last equation.

So here the point of this is for you to know how you

would do this analysis and what this analysis would mean.

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