How do you create dummy variables and run a multiple regression test and answer

Question

How do you create dummy variables and run a multiple regression test and answer the questions listed?

To prepare for this Discussion: Review Warner's Chapter 12 and Chapter 2 of the Wagner course text and the media program found in this week's Learning Resources and consider the use of dummy variables. Create a research question using the General Social Survey dataset that can be answered by multiple regression Using the SPSS software, choose a categorical variable to dummy code as one of your predictor variables. By Day 3 Estimate a multiple regression model that answers your research question. Post your response to the following: 1. What is your research question? 2. Interpret the coefficients for the model, specifically commenting on the dummy variable 3. Run diagnostics for the regression model. Does the model meet all of the assumptions? Be sure and comment orn what assumptions were not met and the possible implications. Is there any possible remedy for one the assumption violations?

Explanation / Answer

MULTIPLE REGRESSION MODEL

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August 3,2016

MULTIPLE REGRESSION MODEL

ESTIMATE A MULTIPLE REGRESSION MODEL

Multiple regressions provides a brilliant strategy to investigate multivariate information. Considerable caution, however, should be observed when interpreting the results of a multiple regression analysis. Individual suggestions incorporate a hypothesis that drives the determination of variables and cross-approval of the validation of the analysis

ESTIMATE A TREND IN A MULTIPLE REGRESSION MODEL

Multiple linear regression analysis can be used to predict trends in data. To determine a time series regression model is taking a line graph of the time arrangement. The line diagram indicates how a variable changes after some time; it can be utilized to examine the qualities of the information, specifically, to see whether a trend exists.

Multiple linear regression analysis can be used to predict trends, e.g., for every cigarette life shortens by 2 hours; for every pound, overweight life reduces by a month.

THE INTERPRETATION OF THE RESULTS OF A MULTIPLE REGRESSION ANALYSIS

The interpretation of the results of a multiple regression analysis is additionally more unpredictable for much the same reason.

The accompanying condition defines the forecast of Y:

Y'i = b0 + b1X1i + b2X2i + … + bkXki

The "b" values are called regression weights and computed in a way that minimizes the whole of squared deviations

in the same way as in simple linear regression. For this situation there are K predictor variables as opposed to two and K + 1 regression weights must be estimated, one for each of the K predictor variable and one for the constant (b0) term (Stockburger).

When you interpret a regression model that contains these types of conditions. You can't simply take a gander at the main impact (straight time) and understand what is happening. Unfortunately, if you are playing out separate relapse investigation, you won't have the capacity to utilize a fitted line plot to translate the outcomes graphically.It is the place branch of knowledge learning is extra valuable (psych).

Regression Diagnostics

A superb review of regression diagnostics given in John Fox's appropriately named Overview of Regression Diagnostics. Dr. Fox's car package provides advanced utilities for regression modeling.

# Assume that we fit a multiple linear regression
# on the MTCARS data
library(car)
fit <- lm(mpg~disp+hp+wt+drat, data=mtcars)

This example is for exposition only. We will ignore the fact that this may not be a great way of modeling. This particular set of data (statmethods).

ASSUMPTIONS

Several assumptions of multiple regression are vigorous to the violation, and others are satisfied in the best possible outline of a study (e.g., autonomy of perceptions). Consequently, we will concentrate on the assumptions of multiple regression that are not vigorous to infringement, and that analysts can manage if disregarded. In particular, we will examine the assumptions of linearity, consistent quality of estimation, homoscedasticity, and typicality.

References

psych. (n.d.). Example of Interpreting and Applying a Multiple Regression Model. Retrieved from psych: http://psych.unl.edu/psycrs/statpage/full_eg.pdf

statmethods. (n.d.). Regression Diagnostics. Retrieved from statmethods: http://www.statmethods.net/stats/rdiagnostics.html

Stockburger, D. W. (n.d.). Multiple Regression with Many Predictor Variables. Retrieved from psychstat: http://www.psychstat.missouristate.edu/multibook/mlt07m.html

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