Week 4 DiscussionDiscussion Topic Overdue – Dec 24, 2021 12:59 AMDiscussion
The discussion assignment provides a forum for discussing relevant topics for this week based on the course competencies covered.
For this assignment, choose one of the following questions and post your initial response to the Discussion Area by the due date assigned.
To support your work, use your course and text readings and also use outside sources. As in all assignments, cite your sources in your work and provide references for the citations in APA format.
Start reviewing and responding to the postings of your classmates as early in the week as possible. Respond to at least two of your classmates. Participate in the discussion by asking a question, providing a statement of clarification, providing a point of view with a rationale, challenging an aspect of the discussion, or indicating a relationship between two or more lines of reasoning in the discussion. Complete your participation for this assignment by the end of the week.
Question One: Bivariate Regression
Regression analysis is a powerful and commonly used tool in business research. One important step in regression is to determine the dependent and independent variable(s).
In a bivariate regression, which variable is the dependent variable and which one is the independent variable?
What does the intercept of a regression tell? What does the slope of a regression tell?
What are some of the main uses of a regression?
Provide an example of a situation wherein a bivariate regression would be a good choice for analyzing data.
Question Two: Types of Regression Analyses
There are two major types of regression analysis—simple and multiple regression analysis. Both types consist of dependent and independent variables. Simple linear regression has two variables—dependent and independent. Multiple regression consists of dependent variable and two or more independent variables.
How does a multiple regression compare with a simple linear regression?
What are the various ways to determine what variables should be included in a multiple regression equation?
Compare and contrast the following processes: forward selection, backward elimination, and stepwise selection.
Justify your answers using examples and reasoning. Comment on the postings of at least two peers and state whether you agree or disagree with their views.
Two variables are said to be in correlation if a change in one of the variables results in a change in the
other variable. Correlation analysis is used to determine the degree of this association between two
variables. For example, portfolio managers can use a correlation analysis to measure the degree of
relationship between the returns of two stocks. However, correlation analysis cannot measure a
nonlinear relationship between two variables, if such a nonlinear relationship exists.
The Pearson correlation coef�cient, also known as the sample correlation coef�cient, is an important
component of correlation analysis. The sample correlation coef�cient can range from -1.0 to +1.0. It is
typically rare for the value to be exactly equal to -1.0, 0, or +1.0. However, these values represent
speci�c meanings that are worthy of discussion.
A correlation coef�cient that is equal to -1.0 indicates a perfectly negative relationship between two
variables. For example, variables representing the price and demands of a product will have negative
correlation. For most products, when the price of the product goes up, the demand for the product
A correlation coef�cient that is equal to 0 indicates that there is no linear relationship between two
variables. A change in one variable is predicted to have no impact on the other variable.
A correlation coef�cient that is equal to +1.0 indicates a perfectly positive relationship between two
variables. For example, variables representing the price and supply of a product will have positive
correlation. If supply can be adjusted, for most products, when the price of the product goes up, the
supply that will be provided to the marketplace goes up.
There is a t-test that can be used to determine if a correlation coef�cient is signi�cantly different from
zero. Thus, if this test shows that there is evidence that the correlation coef�cient is signi�cantly
different from zero, the research can conclude that there is a statistically signi�cant correlation (either
positive or negative) between the two variables.
Download: Video Transcript (PDF 78.08KB) (media/transcripts/SU_W4L1.pdf?
Linear regression is one form of bivariate regression, meaning that it involves two variables. The
simple regression model tries to �t a straight line to a set of data points consisting of one independent
variable (known as the x value) and one dependent variable (known as the y value). The model’s
parameters are denoted by Greek letters such as α_0,α_1,and ε.
There are a number of assumptions that must be met in order for a simple regression model to be a
reasonable choice for modeling the relationship between two variables. Probably the easiest
assumption to understand is that the relationship between the independent variable and the
dependent variable needs to be a linear relationship. For example, if we plotted lots of values that were
created by using the equation y = x2, and then attempted to predict a straight line that approximated
those points, you would have very little success in trying to model data points that had much variability
in terms of the values of the independent variable. While a linear model might work over a very small
range of x values, it would not work well over a wider range of values.
Other assumption violations which can cause unpredictable results include the assumption that the
errors (between the straight line estimate and the actual data) are independent (meaning that the
straight line estimates things just about equally well for all data points), the assumption that the
variance within the data is constant, and the assumption that all of the errors are approximately
The supplemental material entitled “Linear Regression Models” shows you both the most general form
of the model (which will include an error term) and the form of the model which is most often used
(which does not explicitly include the error term, but understand that there will be errors that must be
View a Pdf Transcript of Linear Regression Models
A multiple or multivariate regression is an extension of a bivariate regression, having at least two
independent variables. This method of regression is used when one independent variable is
inadequate to explain the relationship between the dependent variable and the independent variable.
While employing this method, it is important to use a correct model for analyzing data because a
model that is missing necessary independent variables can lead to results that are ambiguous.
Similarly, a model that has additional (unnecessary) independent variables can behave erratically,
especially if the additional independent variable is moderately or highly correlated with some other
independent variable. For this reason, care must be taken to examine the results of a multivariate
regression, to ensure that the model represents the relationship between the dependent variable and
the independent variables as thoroughly and as effectively as possible.
Statistical software packages are sometimes used in order to analyze a multivariate regression, and
the output from such a statistical analysis is useful for examining the degree to which the model is
complete or burdened with unnecessary independent variables. However, a multiple regression can be
alsoestimated with Microsoft Excel.
In order to measure the overall �t of a regression, an F test is utilized. Data from the analysis of
variance (ANOVA) table (the output of the regression) can be used to calculate the F test. An adjusted
coef�cient of determination (adjusted R2) can also be used to measure the overall �t of a regression. In
addition, the regression should be tested for nonlinearity. To measure non-quantitative variables, you
can use dummy variables in a regression.
Linear Regression Models
Simple Linear Regression Model
The following is an example of a linear regression model.
yi = α0 + (α1xi) + εi
This letter represents the dependent variable. It is the variable measured, predicted, or otherwise
monitored by the researcher. It is expected to be affected by the manipulation of the independent
This letter represents the intercept and it is one of the two regression coefficients. Intercept is the
value for the linear function when it crosses the Y-axis or the estimate of y when x is 0.
This letter represents the slope and it is one of the two regression coefficients. Slope is the change
in y for a 1-unit change in x.
This letter represents the independent variable. It is the variable manipulated by the researcher,
thereby causing an effect or change on the dependent variable.
This letter represents an error term. The error term is needed to account for errors in the
measurement of y and those that might be caused due to other variables impacting y. However, it
is not possible to observe this error. Therefore, an assumption can be made about the error term
being a random variable with a normal distribution having a mean of 0 and a standard deviation of
Fitted Regression Model
The following is an example of a fitted regression model.
yi = a0 +a1xi
yi Estimated value of yi
a0 Estimated intercept
2 [Document Title]
[Parent Lecture Name]
a1 Estimated slope
xi Any given value of x
Given a value of x, the fitted regression model can be used to predict a value of y.
When the estimated value of yi is subtracted from the observed value of yi, a
value known as residual is calculated. The residual can be used to estimate
© 2017 South University
Page 2 of 2
[Course Name (not number)]
©2016 South University
Why Choose Us
- 100% non-plagiarized Papers
- 24/7 /365 Service Available
- Affordable Prices
- Any Paper, Urgency, and Subject
- Will complete your papers in 6 hours
- On-time Delivery
- Money-back and Privacy guarantees
- Unlimited Amendments upon request
- Satisfaction guarantee
How it Works
- Click on the “Place Order” tab at the top menu or “Order Now” icon at the bottom and a new page will appear with an order form to be filled.
- Fill in your paper’s requirements in the "PAPER DETAILS" section.
- Fill in your paper’s academic level, deadline, and the required number of pages from the drop-down menus.
- Click “CREATE ACCOUNT & SIGN IN” to enter your registration details and get an account with us for record-keeping and then, click on “PROCEED TO CHECKOUT” at the bottom of the page.
- From there, the payment sections will show, follow the guided payment process and your order will be available for our writing team to work on it.