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The *dissertation
semester* is here for undergraduate students in most tertiary institutions,
at least for those whose academic calendar is uninterrupted J. The students are in different
stages of their *project*, as it is
commonly called. Some are yet to wrap up their chapter one which gives the
“study background” and the framing of research hypotheses, objectives and
questions. Some have moved on to chapter two reviewing relevant literature
related to their scope of study. Others have gone further in developing both
the theoretical and empirical frameworks for chapter three, but not without the
usual teething lags…but they’ll get around it, somehow J. A handful have made tremendous
progress in hitting chapter four attempting to analyse their data.

Because chapters one to three are
relative to each students’ scope of work, while a regression output is common
to all (although actual outcomes differ), I decided to do this tutorial in
explaining the basic features of a regression output. Again, this write-up is
in response to requests received from readers on (1) what some specific figures
in a regression output are and (2) how to interpret the results. Let me state
here that regardless of the analytical software whether Stata, EViews, SPSS, R,
Python, Excel etc. what you obtain in a regression output is common to all
analytical packages (howbeit with slight changes).

For instance, in undertaking an ordinary
least squares (OLS) estimation using any of these applications, the regression
output will give the ANOVA (analysis of variance) table,

*F*-statistic,*R*-squared, prob-values, coefficient, standard error,*t*-statistic, sum of squared residuals and so on. These are some common features of a regression output. However, the issue is: what do they mean and how can they be interpreted in relation to your study?
Hence, the essence of this tutorial is to
teach students the significance of these features and how to interpret their
results. I will be using

**EViews**analytical package to explain a regression output, but you can practise along using any analytical package of your choice. (See "How-to-interpret regression output" here for**Stata**and**Excel**users).**An Example: Use Gujarati and Porter Table7_12.xlsx dataset**

Note: I will not be discussing
stationarity or cointegration analysis in this contest, just doing a simple linear regression
analysis (a bi-variate analysis) with only one explanatory variable.

The dataset is on the United States from
1960 to 2009 (50 years data). The outcome variable is consumption expenditure (

*pce*) and the explanatory variable is income (*income*).**First step: Load data in Excel format into EViews**

Here is the data in excel format:

Data in Excel file format Source: CrunchEconometrix |

To import the Excel file into EViews, go
to:

**File**>>**Import**>>**Import from file**>>**Next**>>**Finish**. If it is correctly done, you obtain:Import Excel file into EViews Source: CrunchEconometrix |

Note: In EViews almost everything can
be done either by typing commands or by choosing a menu

item (the Guide User
Interface, GUI). The choice is a matter of personal preference.

**Second step: Visualise the relationship between the variables**

Before analysing the data, it is good to
always graph the dependent and key explanatory variable (using a scatter plot) in
order to observe the pattern between them. It sorts of gives you what to expect
in your actual analysis.

Since we want to see the relationship
between

*pce*and*income*over the 50-year period, it means that we want to look at the variables*pce*and*income*together. In EViews a collection of series dealt with together is called a**Group**. Thus, to create a group including*pce*and*income*, first click on*income*. Now, while holding down the Ctrl-key, click on*pce*. Then right-click anywhere on the interface highlighting**New Object**, bringing up the context menu as shown below:
Click

**New Object**and the dialogue box opens:EViews: New Object dialogue box Source: CrunchEconometrix |

Click

**OK**to open the**Series List**dialogue box and type in*income pce*:EViews: Series List dialogue box Source: CrunchEconomterix |

Click

**OK**and your data should look like this:EViews: Group data Source: CrunchEconometrix |

At this point it is important to save
your data file. Click on

**Name**and under**Name to identify objec**t change**group01**to the desired the file name:EViews: Object Name dialogue box Source: CrunchEconometrix |

Note:
Spaces are not allowed when naming an object in EViews.

I will save this file as

**pce_income**. Click**OK**and the file appears as**G pce_income**like this:EViews: Naming a file Source: CrunchEconometrix |

Now we have finished with all the data
prepping. It’s time to observe the relationship between two series. To do that,
we will use the scatter diagram. Click on

**G pce_income**to open the file. Then click on**View >> Graph >>****Scatter >> OK**

The scatter diagram indicates a positive relationship between the two variables:

EViews: Scatter plot (pce and income) Source: CrunchEconometrix |

This positive relationship seems
plausible because the more income you have, the more you’ll want to consume,
except you are very economical J.

To graph the model (

*pce*) with the linear prediction (*pce*), Click on_{hat}**G pce_income**to open the file. Then click on**View >> Graph >>****Scatter >>**on the left-hand side of the dialog that pops up >> select**Regression line**from the**Fit lines**dropdown menu. The default options for a regression line are fine, so hit to dismiss the dialog.
Or, simply right click inside the graph:

**Fit lines**>> select**Regression line**>>**OK**EViews: Scatter plot with fit line Source: CrunchEconometrix |

As observed from the graph, all the
points do not fall on the predicted line. Some lie above, while some are
beneath the line. These are all the

**residuals**(in order words, the remnants obtained after the regression analysis).**Third step: The scientific investigation**

Now we want to scientifically
investigate the relationship between

*pce*and*income*. In EViews you specify a regression with the**command followed by a list of variables. (“***ls***” is the name for the EViews command to estimate an ordinary***LS***east***L***quares regression.) The first variable is the***S***, the variable we’d like to explain***dependent variable**pce*in this case. The rest of the list gives the**, which are used to predict the dependent variable.***independent variables*
Also, one can “run a regression” either
by using the

**or***menu***approach. Using the***type-command***, from the Tool Bar, pick the menu item***menu approach***Quick >> Estimate Equation**and a dialog box opens:
Under

**Equation specification**, type “*pce c income*” click**OK**.*Hold on a bit.*If

*pce*is the dependent variable and

*income*is the explanatory variable so, where does the “

*C*” in the command come from? “

*C*” is a special keyword telling EViews to estimate the equation with an

**.**

*intercept*
And if you prefer to use the

*type-command approach*, go to the command section and type in:*ls pce c income*

(You
have simply told EViews to regress the dependent variable,

*pce*, on the explanatory variable,*income*and a*constant*).
Therefore, whether you use the menu or type
a command, EViews churns out the regression results shown below:

EViews: Regression Output Source: CrunchEconometrix |

**Fourth step: The features of a regression output**

So what do these figures mean? I will
explain each feature in turns.

**Dependent variable:**this is

*pce*and it is clearly defined. It is also the outcome variable.

**Method:**this is the estimation technique. In this example, it is ordinary least squares

**Date:**captures the exact time you are carrying out the analysis

**Sample:**must be in line with your scope of research; that is 1960 to 2009

**Included observations**: since the data span is from 1960 to 2009, observations = 50

**Variable**: includes both the intercept and slope

**Coeff**: these captures the estimates for intercept and slope. The sign of the coefficient also tells the direction of the relationship. A positive (negative) sign implies a positive (negative) relationship.

**Std. error**: this is the standard deviation for the coefficient. That is, since you are not so sure about the exact value for

*income*, there will be some variation in the prediction for the coefficient. Therefore, the standard error shows how much deviation occurs from predicting the slope coefficient estimate.

*t***-stat**: this measures the number of standard errors that the coefficient is from zero. It is obtained by:

*coefficient/std.error*. A

*t*-stat above 2 is sufficient evidence against the null hypothesis

**Prob.**: there are several interpretations for this. (1) it is smallest evidence required to reject the null hypothesis, (2) it is the probability that one would have obtained the slope coefficient value from the data if the actual slope coefficient is zero, (3) the

*p*-value looks up the

*t*-stat table using the degree of freedom (df) to show the number of standard errors the coefficient is from zero, (4) tells whether the relationship is significant or not.

So, if the

*p*-value is 0.35, then it means that you are only 65% (that is, (100-35)%) confident that the slope coefficient is non-zero. This is not good enough. This is because a very low*p*-value gives a higher level of confidence in rejecting the null hypothesis. Hence, a*p*-value of 0.01, implies that you are 99% (that is, (100 - 1)%) confident that the slope coefficient is non-zero. This is very comforting! J.

*R***-squared**: the value of 0.999273 gives the variation in

*pce*that is explained by

*income*. The higher the

*R*

^{2}, the better the model and the more predictive power the variables have. Although, an

*R*

^{2}that equals 1 will elicit some suspicion. The R is actually the correlation coefficient between the 2 variables. That implies that:

*Adjusted R***-squared**: this is the

*R*

^{2}adjusted as you increase your explanatory variables. It (0.999257) reduces as more explanatory variables are added.

**: this is the summary measure based on the estimated variance of the residuals.**

*S.E of regression***Sum squared resid**: implies sum of squared residuals for the Model (explained variation in

*pce*) and Residuals (unexplained variation in

*pce*). After doing the regression analysis, all the points on

*pce*

_{ha}_{t}do not fall on the regression line. Those points outside the line are known as

**. Those that can be explained by the model are known as**

*residuals***Explained Sum of Squares**(ESS) while those that are due to random nature, which are outside the model are known as

**Residual Sum of Squares**(RSS).

Having seen the plot of the scatter
diagram, it is pretty clear that the predicted line does an almost-accurate job
of giving a 50-year summary of

*pce*. In regression analysis, the amount by which the right-hand side of the equation misses the dependent variable is called the*residual**.*Calling the residual*e**(“***” stands for “***e***error**”), we can write an equation that really is valid in each and every year, that is:*pce =*-31.88*+*0.819*income + e*
Since the residual is the part of the
equation that’s left over after we’ve explained as much as possible with the
right-hand side variables, one approach to getting a better fitting equation is
to look for patterns in the residuals.

To obtain the table showing the
predicted and residual values, go to

**View >> Actual, Fitted, Residual**>>**Actual, Fitted, Residual Table**and you get:EViews: Table of actual, predicted and residual values Source: CrunchEconometrix |

If the predicted line falls above a
point, it means that

*pce*is over-predicted (that is,*pce – pce*is negative) and if it is beneath a point, it implies that_{hat}*pce*is under-predicted (that is,*pce – pce*is positive). The sum and mean of the residuals equals zero._{hat}
Likewise, to obtain the plot of the
predicted and residual values, go to

**View >> Actual, Fitted, Residual**>>**Actual, Fitted, Residual Graph**and you get:EViews: Graph of actual, predicted and residual values Source: CrunchEconometrix |

**Log likelihood**: this the difference between the log likelihood values of the restricted and unrestricted versions of the model.

*F***-statistic**: captures whether the explanatory variable,

*income*is significant in explaining the outcome variable,

*pce*. The higher the

*F*-stat, the better for the model.

**Prob (**: the probability value of 0.0000 is the probability value that indicates the statistical significance of the

*F*-statistic)*F*statistic. You will prefer to have a

*prob*-value that is less than 0.05.

**Mean dependent var**: the figure of 3522.160 indicates the average value of

*pce*in the data.

**S. D. dependent var**: the figure of 3077.678 indicates the deviation from the average value of

*pce*in the data

**Akaike/Schwartz/Hannan-Quinn info criterion**: these are often used to choose between competing models. The lower the value of these criteria, the better the model is. From this example, the Akaike info criterion (AIC) figure of 11.73551 is the lowest of the three and therefore indicates that it is the best model to adopt in this case.

**Durbin-Watson stat**: is used to find out if there is first-order serial correlation in the error terms.

*Rule of thumb:*if DW < 2 equals evidence of positive serial correlation. So, from our example, the DW value of 0.568044 indicates serial correlation in the residuals.

**Assignment:**

**Use Gujarati and Porter Table7_12.xlsx dataset.**

(1) With

*pce*as the dependent variable and*gdpi*as the explanatory variable, plot the graph of*pce*and*gdpi*, what do you observe?
(2) Run your regression. Can you interpret the
table and the features?

(3) Plot the predicted line. What are your
observations?

**[Watch video on how to interpret regression output in EViews]**

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