A Comparison of R, Python, SAS, SPSS and STATA for a Best Statistical Software

Common statistics program packages differ considerably in terms of their strengths, weaknesses, and handling. The decision as to which system is the best fit should be made with care. Changing to a new system can result in high costs for things like new licenses and re-training. This article introduces and contrasts the market leaders

Data Analysis Using IBM SPSS: Use and Interpretation

SPSS means “Statistical Package for the Social Sciences” and was first launched in 1968. Since SPSS was acquired by IBM in 2009, it’s officially known as IBM SPSS Statistics but most users still just refer to it as “SPSS”. SPSS is software for editing and analyzing all sorts of data. These data may come from basically any source:

AMOS SPSS

AMOS is statistical software and it stands for analysis of a moment structures. AMOS is an added SPSS module, and is specially used for Structural Equation Modeling, path analysis, and confirmatory factor analysis.  It is also known as analysis of covariance or causal modeling software. AMOS is a visual program for structural equation modeling (SEM). In AMOS, we can draw models graphically

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Data Analysis Using STATA: Use and Interpretation

Stata is a general-purpose statistical software package created in 1985 by StataCorp. The name Stata is a syllabic abbreviation of the words statistics and data. The FAQ for the official forum of Stata insists that the correct English pronunciation of Stata “must remain a mystery”; any of “Stay-ta”, “Sta-ta” or “Stah-ta” (rhymes of the three pronunciations of ‘data’) are considered acceptable. More recent updates indicate

Introduction to SmartPLS software

SmartPLS is a software with graphical user interface for variance-based structural equation modeling (SEM) using the partial least squares (PLS) path modeling method. Besides estimating path models with latent variables using the PLS-SEM algorithm, the software computes standard results assessment criteria (e.g., for the reflective and formative measurement models, the structural model, and the goodness of fit) and

Introduction to Eviews

EViews is a statistical package for Windows, used mainly for time-series oriented econometric analysis. It is developed by Quantitative Micro Software (QMS), now a part of IHS. Version 1.0 was released in March 1994, and replaced MicroTSP. The TSP software and programming language had been originally developed by Robert Hall in 1965. The current version of EViews is 12, released in November 2020. EViews is

Using EViews for Principles of Econometrics

This manual is a supplement to the textbook Principles of Econometrics, 3rd Edition, by Hill, Griffiths and Lim (John Wiley & Sons, Inc., 2008). It is not in itself an econometrics book, nor is it a complete computer manual. Rather it is a step-by-step guide to using EViews for the empirical examples in Principles

Starting EViews

To launch EViews double click the EViews 6 SV icon on the desktop, if one is present. It should resemble Alternatively, select EViews from the Windows Start Menu. When EViews opens you are presented with the following screen Across the top are Drop Down Menus that make implementing EViews procedures quite simple. Below the

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The EViews Help System

Click Help on the EViews menu The resulting menu is 1. EViews help topics First, click on EViews Help Topics. Select Getting Started (Student Version) to obtain basic information about the Student Version of EViews. Selecting User’s Guide/EViews Fundamentals opens a list of chapters that can take you through specifics of working with EViews.

Using a EViews Workfile

As noted earlier all the data for the book Principles of Econometrics is provided as EViews worhfiles. These will be used starting in Chapter 2. To illustrate the basic functioning of EViews we will use an example provided with EViews. Click on Help/EViews Help Topics/User’s Guide/EViews Fundamentals/A Demonstration. The demonstration starts by importing data

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Examining Several Series with EViews

Rather than examining one series at a time we can view several. In the workfile window select the series Ml and then while holding down the Ctrl-key select the PR series. Double click inside the blue area to open what is called a Group of variables. Click on Open Group. A spreadsheet view of

Using the Quick Menu of EViews

The spreadsheet view of the data is very powerful. Another key tool is the Quick menu on the EViews workfile menu. The options shown are 1. Changing the sample By selecting Sample from this menu we can change the range of sample observations. Change the sample to 1952:1 to 2003:4 and click OK. 2.

Using EViews Functions

Now we will explore the use of some EViews functions. Select Help/Quick Help Reference/Function Reference. 1. Descriptive statistics functions Select Descriptive Statistics from the list of material links. Some of the descriptive statistics functions listed there are on the next page. In this table of functions you will note that these functions begin with

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The Simple Linear Regression Model by using EViews

In this chapter we introduce the simple linear regression model and estimate a model of weekly food expenditure. We also demonstrate the plotting capabilities of EViews and show how to use the software to calculate the income elasticity of food expenditure, and to predict food expenditure from our regression results. 1. OPEN THE WORKFILE

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Interval Estimation and Hypothesis Testing by using EViews

1. INTERVAL ESTIMATION For the regression model y = β1 +β2x + e, and under assumptions SR1-SR6, the important result that we use in this chapter is given in equation (3.3) of POE.             ’ Using this result we can show that the interval bk ± tcse(bk) has probability 1-a of containing the true

Prediction, Goodness of Fit and Modeling Issues by using EViews

1. PREDICTION IN THE FOOD EXPENDITURE MODEL In this chapter we continue to work with the simple linear regression model and our model of weekly food expenditure. To begin, open the food expenditure workfile food.wfl. On the EViews menu choose File/Open and then open the file. So that the original file is not altered

The Multiple Linear Regression Model by using EViews

In the simple linear regression model the average value of a dependent variable is modeled as a linear function of a constant and a single explanatory variable. The multiple linear regression model expands the number of explanatory variables. As such it is a simple but important extension that makes linear regression quite powerful. The

Further Inference in the Multiple Regression Model by using EViews

1. F AND CHI-SQUARE TESTS In Chapter 5 we saw how to use EViews to test a null hypothesis about a single coefficient in a regression model. This test can be extended in two ways. We may want to test a single null hypothesis that involves two or more coefficients, or we might want

Nonlinear Relationships by using EViews

1. POLYNOMIALS In microeconomics you studied “cost” curves and “product” curves that describe a firm. Total cost and total product curves are mirror images of each other, taking the standard “cubic” shapes shown in POE Figure 7.1. Average and marginal cost curves, and their mirror images, average and marginal product curves, take quadratic shapes,

Heteroskedasticity by using EViews

1. EXAMINING RESIDUALS In this chapter we return to the example considered in Chapters 2 to 4 where weekly expenditure on food was related to income. Data in the file food.wfl were used to find the following least squares estimates. We are now concerned with whether the error variance for this equation is likely