Conducting Regression Analysis Using SPSS: A Hands-On Guide with

PRESENTATION
2024
A HANDS-ON GUIDE WITH ECONOMIC DATA
www.tutorhelpdesk.com
CONDUCTING
REGRESSION ANALYSIS
USING
SPSS

Introduction
Regression analysis is a technique of great
importance that aims at analyzing the
correlations of variables, which are used mostly in
economics, medicine, social sciences, etc. This ppt
will explain how to undertake a regression
analysis using SPSS with an economic dataset as
our example. More precisely, the unemployment
rate and GDP data will be used to analyze the
relationship between economic growth
(measured by GDP) and unemployment.

In general, regression analysis provides a
relation between the dependent variable
and one or more independent variables. The
simplest form is the linear regression which
plots a straight line through the data points
so as to minimize the error between the
predicted, and actual values. Multiple
regression for instance allows for the use of
several predictors in the analysis.
Regression analysis helps in:
01
02
What is Regression Analysis?
Predicting outcomes: It
enables to predict the value
of the dependent variable on
the basis of the independent
variables.
Understanding relationships:
Correlation goes further
since it measures the extent
and direction of a
relationship between two or
more variables.
Hypothesis testing: It allows
you to hypothesize about
whether several variables
have a significant effect on
other variables.
03

WHY SPSS FOR
REGRESSION ANALYSIS?
It also has an easy-to-use graphical user interface and an exhaustive suite
for doing regression on SPSS without requiring any intensive
programming. SPSS users can carry out computations ranging from
descriptive statistics to comprehensive statistical analysis and modeling
best suited for novices and experts.

HOW TO PERFORM
REGRESSION ANALYSIS?
Simple Guide Using SPSS
For this demonstration, we will use a hypothetical economic dataset that contains two key
variables:
Unemployment Rate:
The dependent variable that we propose to forecast
is the one that any shift in GDP is expected to
impact.
GDP (Gross Domestic Product):
The independent variable, which is also known
as the economic output of a country.

STEP 1: PREPARE THE
DATASET
One important step you should take before
employing any tools of analysis is to check that
your data is clean, formatted correctly, and capable
of supporting regression analysis.
Here is a sample of our dataset:
Loading Data into SPSS:
Select the file option on SPSS software and click
open data then locate your data set in the .sav, xls,
or CSV format. Check that you have used the
appropriate variable name. For demonstration let
us label the “GDP” column as the independent
variable and “Unemployment Rate” as the
dependent variable.
Country
Year
GDP
(in
trillions)
Unemployment
Rate (%)
USA 2019 21.4 3.7
Canada 2019 1.84 5.7
Germany
2019 3.86 3.1
Japan 2019 5.08 2.4
UK 2019 2.83 3.8

STEP 2: VISUALIZE THE
DATA
Before running the regression, it's important to explore
the data visually.
1. Scatter Plot:
⚬ Go to Graphs > Legacy Dialogs > Scatter/Dot.
⚬ Select "Simple Scatter" and click "Define".
⚬ Place "GDP" on the X-axis and "Unemployment
Rate" on the Y-axis.
⚬ Click "OK" to generate the scatter plot.
You can be able to make an early decision in terms of
positive, weak, strong, or no relationship at all by just
looking at this scatter plot. If you see a downward-
sloping trend, that might indicate a negative
relationship: You can notice a reverse relation which
means that employment decreases as the GDP
increases.

STEP 3: CONDUCT THE
REGRESSION ANALYSIS
Now, let's run the linear regression analysis.
1. Access the Regression Menu:
⚬ Go to Analyze > Regression > Linear.
2.Specify Variables:
⚬ Place "Unemployment Rate" in the "Dependent"
box and "GDP" in the "Independent(s)" box.
3.Options:
⚬ You can leave the default options as they are or
explore additional features such as confidence
intervals, Durbin-Watson tests for
autocorrelation, or saving residuals for further
analysis.
4.Run the Regression:
⚬ Click "OK" to run the regression analysis.

STEP 4: INTERPRET THE RESULTS
MODEL SUMMARY
The R Square value shows
the extent up to which the
independent variable has
explained the dependent
variable. For instance, if the
obtained R Square is 0.65
this implies that 65 percent
of the variance in
unemployment can be
explained by changes in
GDP.
Model Summary:
R Square = 0.65
Adjusted R Square = 0.64
ANOVA TABLE
The ANOVA table checks if
the regression model is
significantly better in
outcome variable prediction
than a model which has no
predictors. The significance
value or p-value in the ANOVA
table shows the overall
significance of the model.
Model is significant when the
p-value is less than 0.05.
ANOVA:
F = 34.56
Significance (p-value) = 0.002
Once the analysis is complete, SPSS will generate several tables. Here's how to interpret
the key ones:
SIGNIFICANCE OF THE PREDICTOR
a. The Sig. (p-value) in the
coefficients table tests
whether the individual
predictor (GDP) is
statistically significant.
b. A p-value less than 0.05
indicates that GDP is a
significant predictor of
unemployment.
Coefficients:
GDP Coefficient = -0.45
p-value = 0.001

STEP 4: INTERPRET THE RESULTS (CONTD.)
COEFFICIENTS TABLE
a. This table provides the regression coefficients, which are used to
construct the regression equation.
b. The Unstandardized Coefficients column contains the values for the
intercept (constant) and the slope (GDP).
c. The regression equation can be written as:
Unemployment Rate=β0+β1×GDP
Suppose the table provides the following values:
i. Constant (Intercept): 5.8
ii. GDP (Slope): -0.45
This gives us the equation:
Unemployment Rate=5.8−0.45×GDP
This equation suggests that for every additional trillion dollars in GDP, the
unemployment rate decreases by 0.45 percentage points.

STEP 5: EVALUATE
MODEL ASSUMPTIONS
For linear regression, several assumptions
need to be met:
1. Linearity: The graph of the independent
and dependent variables must be a straight
line.
2. Independence of Errors: The residuals
(errors) should be independent.
3. Homoscedasticity: The residuals should
be of equal variance in each of the level of
the independent variable.
4. Normality of Residuals: The residuals
should be randomly distributed.
To check these assumptions in SPSS:
• Linearity: You can examine the
scatterplot. Based on our expectation we
should be able to observe a linear
correlation between the GDP and the
unemployment rate.
• Independence and Homoscedasticity:
You can select ‘Standardized Residuals’
from the drop-down ‘SCALE’ (Analyze >
Regression > Linear > Save) and then
determine if the residuals seem to have
any particular pattern.
• Normality: To make sure that the
assumptions are met, a histogram or Q-Q
plot of the residuals should be created by
going to Graphs> Legacy Dialogs >
Histogram.

STEP 6: REPORTING THE
RESULTS
When reporting the results of your
regression analysis, you should include the
following information:
1. The regression equation: Y=5.8−0.45×GDP
2. The R-squared value: Indicating how
much variance in the unemployment rate is
explained by GDP.
3. The significance levels (p-values):
Regarding the total proposed model and
each of the individual predictors
Example of a report statement:
To achieve this, a straightforward linear
regression was run on the variables of
GDP to arrive at a forecast of the
unemployment rate. The findings reveal
that GDP has a significant correlation
with the unemployment rate (r = -0.45,
p = 0.001) and accounts for 65% of the
variance, based on the model. The
regression equation shows that,
unemployment is inversely related to
GDP, and for every one trillion dollars
rise in the GDP the unemployment rate
declines by 0.45 percentage points.

Using regression analysis on large and
complicated data might prove to be a
challenge to learners working on
assignments, thesis, or research. As we
know, SPSS makes the job nearly
effortless but complex data structures,
multicollinearity, outliers, and
assumptions such as homoscedasticity
or normality are not trivial to handle.
Our SPSS Assignment Help allows
students to deal with these issues by
getting professional assistance to
analyze data correctly, create
comprehensive reports, and adhere to
academic requirements. Whether
you’re working with big economic
datasets, social science variables, or
multivariate models,
our experts help you:
WHY SPSS ASSIGNMENT
HELP IS NEEDED FOR
COMPLEX REGRESSION
ANALYSIS?
1. Pre-process
big data.
2. Understand
complex outputs
like ANOVA,
coefficients, and
residuals of a
variable.
3. Ensure
compliance
with regression
assumptions.
4. Develop clear
and concise
academic
reports that
meet your
institution's
requirements.

CONCLUSION
KEY TAKEAWAYS
We offer you individual coaching
to prevent the misuse of time,
avoid pitfalls, and receive only the
best results, making your output
distinctive.
Conducting regression analysis
using SPSS is a straightforward
process once you understand the
key steps: Prepare the dataset,
visualize the relationship, perform
a test run of analysis, and state
observations from the analytical
output. Looking at our example,
we get to establish the fact that
there is some negative
relationship between the GDP
and the unemployment rate,
something that makes economic
sense. SPSS simplifies the above
discussed steps in such a way
that it can explain regression
analysis to those who have no
statistical background.
Opt for our SPSS regression help
services where we ensure that you
fully understand how to execute a
successful process to achieve the
best results in your coursework!

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