Team D will examine positive relationship of wages with multiple variables. The question is, are wages dependent on the gender, occupation, industry, years of education, race, years of work experience, marital status, and union membership. We will use the technique of linear regression and correlation. Regression analysis in this case should predict the value of the dependent variable (annual wages), using independent variables (gender, occupation, industry, years of education, race, and years of work experience, marital status, and union membership).

Regression Analysis

Based on our initial findings from MegaStat, we built the following model for regression (coefficient factors are rounded to the nearest hundredth):

Global Test:
Ho: All regression coefficients for the variables in the population are zero H1: Not all regression coefficients are zero
Significance level = 0.05
Decision rule: Reject Ho if p-value < 0.05

The p-value generated by the regression analysis is non-zero (4.42x10-7), therefore we reject Ho and conclude that regression is a good fit for this model.

Individual tests:
Ho: Regression coefficient for each variable is zero
H1: Regression coefficient for each variable is not zero
Significance level = 0.05
Decision rule: Reject Ho if p-value < 0.05
Because these are all t-tests, we can read the p-values of these tests from the Regression output. he variables with p-values less than 0.05 have significant impact on wages earned, also that variables with p-values greater than 0.05 do not have significant impact on wages. According to the MegaStat output, the variables that significantly affect wages are education (p = 2.17x10-6), gender (p = .0001), and experience (p =...

...been retrieved from the case study titled “Housing Price” (Case #27 - Practical Data Analysis: Case Studies in Business Statistics- Marlene A. Smith & Peter G. Bryant)
The most important factor in determining the selling prices ofhouses is to know the features that drive the selling prices of the house. People tend to have more interest in houses with multiple bed rooms/bathrooms, fireplace, garage for multiple cars and location while choosing a...

...Chapter3
MultipleRegressionAnalysis: Estimation
Key drawback of SLR: all other factors affecting y are unrelated
with x, as is unrealistic.
Multipleregression allows us to control for many other
factors to explain dependent variable, which is useful both for
testing economic theories and for drawing the ceteris paribus
conclusion.
In addition, MR can incorporate fairly general functional form and
build better models...

...variables is 0, it means that the two variables aren’t related. – TRUE
2. In a simple regressionanalysis the error terms are assumed to be independent and normally distributed with zero mean and constant variance. – TRUE
3. The difference between the actual Y-value and the predicted Y-value found using a regression equation is called the residual (ε) – TRUE
4. In a multipleregressionanalysis...

...Multipleregression: OLS method
(Mostly from Maddala)
The Ordinary Least Squares method of estimation can easily be extended to models involving two or more explanatory variables, though the algebra becomes progressively more complex. In fact, when dealing with the general regression problem with a large number of variables, we use matrix algebra, but that is beyond the scope of this course.
We illustrate the case of two explanatory...

...RegressionAnalysis (Tom’s Used Mustangs)
Irving Campus
GM 533: Applied Managerial Statistics
04/19/2012
Memo
To:
From:
Date: April 19st, 2012
Re: Statistic Analysis on price settings
Various hypothesis tests were compared as well as several multipleregressions in order to identify the factors that would manipulate the selling price of Ford Mustangs. The data being used contains observations on 35...

...you
cannot consult the regression R2 because
(a) ln(Y) may be negative for 0 < Y < 1.
(b) the TSS are not measured in the same units between the two models.
(c) the slope no longer indicates the effect of a unit change of X on Y in the log-linear
model.
(d) the regression R2 can be greater than one in the second model.
1
(v) The exponential function
(a) is the inverse of the natural logarithm function.
(b) does not play an important role in modeling nonlinear...

...Introduction:
The main idea of a multipleregressionanalysis is to understand the relationship between several independent variables and a single dependent variable. (Lind, 2004) A model of the relationship is hypothesized, and estimates of the parameter values are used to develop an estimated regression equation.(abyss.uoregon.edu) The multipleregression equation used to describe the relationship is: Y'...

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