Introduction to Statistical Analysis Using IBM SPSS (V20)...0G510G [email protected] (34) 91 425 06 60 Introduction to Statistical Analysis Using IBM SPSS (V20) Duración:

0G510G www.globalknowledge.es [email protected] (34) 91 425 06 60

Introduction to Statistical Analysis Using IBM SPSS (V20)

Duración: 2 Días Código del Curso: 0G510G

Temario:

Introduction to Statistical Analysis Using IBM SPSS Statistics is a two day instructor-led classroom course that provides an application-orientedintroduction to the statistical component of IBM® SPSS® Statistics. You will review several statistical techniques and discuss situations inwhich you would use each technique, the assumptions made by each method, how to set up the analysis, as well as how to interpret theresults. This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing underlyingrelationships. You will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence,and interpret their output, and graphically display the results.

Dirigido a:

This basic course is for: anyone who has worked with IBM SPSS Statistics and wants to become better versed in the basic statisticalcapabilities of IBM SPSS Statistics Base anyone with limited or no statistical background anyone who wants to refresh their knowledge andstatistical experience that were gained many years ago

Objetivos:

Please refer to course overview.

Prerequisitos:

You should have:

General computer literacyCompletion of the "Introduction to IBM SPSS Statistics" and/or"Data Management and Manipulation with IBM SPSS Statistics"courses or experience with IBM SPSS Statistics (Version 15 orlater) including familiarity with opening, defining, and saving datafiles and manipulating and saving output


Contenido:

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Explain different types of statistical errors should and can be usedand power

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Use the Independent-Samples T Test to test Regression Analysis featuresthe difference in means Explain the options of the Linear The Independent- Samples T TestKnow how to interpret the results of a Regression procedure Use the Independent-Samples T Test toIndependent-Samples T Test Interpret the results of the Linear test the difference in meansUse the Chart Builder to create an error bar Regression procedure Know how to interpret the results of agraph to display mean differences Use Automatic Linear Models to perform Independent-Samples T TestThe Paired-Samples T Test regression Use the Chart Builder to create an errorInterpret the results of a Paired-Samples T Nonparametric Tests bar graph to display mean differencesTest Describe the options in the The Paired-Samples T TestOne-Way ANOVA Nonparametric Tests procedure dialog Interpret the results of a Paired-Samples TCheck the assumptions for One-Way box and tabs TestANOVA Interpret the results of several types of One-Way ANOVAInterpret the results of a One-Way ANOVA nonparametric tests Check the assumptions for One-Wayanalysis ANOVAUse the Chart Builder to create an error bar Interpret the results of a One-Way ANOVAto graph mean differences line analysisBivariate Plots and Correlations for Scale Explain differences between populations Use the Chart Builder to create an errorVariables and samples bar to graph mean differencesExplain the Pearson correlation coefficient Explain differences between experimental Bivariate Plots and Correlations for Scaleand its assumptions and non-experimental research designs VariablesInterpret a Pearson correlation coefficient Explain differences between independent Explain the Pearson correlation coefficientExplain the options of the Bivariate and dependent variables and its assumptionsCorrelations procedure Understanding Data Distributions - Interpret a Pearson correlation coefficientRegression Analysis Theory Explain the options of the BivariateExplain the options of the Linear Regression Use measures of central tendency and Correlations procedureprocedure dispersion Regression AnalysisInterpret the results of the Linear Regression Use normal distributions and z-scores Explain the options of the Linearprocedure Data Distributions for Categorical Regression procedureUse Automatic Linear Models to perform Variables Interpret the results of the Linearregression Interpret the results of the Frequencies Regression procedureNonparametric Tests procedure Use Automatic Linear Models to performDescribe the options in the Nonparametric Data Distributions for Scale Variables regressionTests procedure dialog box and tabs Interpret the results of the Frequencies, Nonparametric TestsInterpret the results of several types of Descriptives, and Explore procedures Describe the options in the Nonparametricnonparametric tests Making Inferences about Populations Tests procedure dialog box and tabs

from Samples Interpret the results of several types ofExplain the nature of probability nonparametric tests

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Interpret the results of the Frequencies, Tests procedure dialog box and tabsDescriptives, and Explore procedures Interpret the results of several types of

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Use the Paired-Samples T Test Describe the options in the Nonparametricprocedure Tests procedure dialog box and tabs

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crosstabulationcrosstabulation Explain different types of statistical errors Interpret cell counts and percents in aUse additional syntax-only Crosstabs and power crosstabulationfeatures Explain differences between statistical Use the Chi-Square test, interpret itsThe Independent- Samples T Test and practical importance results, and check its assumptionsUse the Independent-Samples T Test to test Relationships Between Categorical Use the Chart Builder to visualize athe difference in means Variables crosstabulationKnow how to interpret the results of a Request appropriate statistics for a Use additional syntax-only CrosstabsIndependent-Samples T Test crosstabulation featuresUse the Chart Builder to create an error bar Interpret cell counts and percents in a The Independent- Samples T Testgraph to display mean differences crosstabulation Use the Independent-Samples T Test toThe Paired-Samples T Test Use the Chi-Square test, interpret its test the difference in meansInterpret the results of a Paired-Samples T results, and check its assumptions Know how to interpret the results of aTest Use the Chart Builder to visualize a Independent-Samples T TestOne-Way ANOVA crosstabulation Use the Chart Builder to create an errorCheck the assumptions for One-Way Use additional syntax-only Crosstabs bar graph to display mean differencesANOVA features The Paired-Samples T TestInterpret the results of a One-Way ANOVA The Independent- Samples T Test Interpret the results of a Paired-Samples Tanalysis Use the Independent-Samples T Test to TestUse the Chart Builder to create an error bar test the difference in means One-Way ANOVAto graph mean differences Know how to interpret the results of a Check the assumptions for One-WayBivariate Plots and Correlations for Scale Independent-Samples T Test ANOVAVariables Use the Chart Builder to create an error Interpret the results of a One-Way ANOVAExplain the Pearson correlation coefficient bar graph to display mean differences analysisand its assumptions The Paired-Samples T Test Use the Chart Builder to create an errorInterpret a Pearson correlation coefficient Interpret the results of a Paired-Samples bar to graph mean differencesExplain the options of the Bivariate T Test Bivariate Plots and Correlations for ScaleCorrelations procedure One-Way ANOVA VariablesRegression Analysis Check the assumptions for One-Way Explain the Pearson correlation coefficientExplain the options of the Linear Regression ANOVA and its assumptionsprocedure Interpret the results of a One-Way Interpret a Pearson correlation coefficientInterpret the results of the Linear Regression ANOVA analysis Explain the options of the Bivariateprocedure Use the Chart Builder to create an error Correlations procedureUse Automatic Linear Models to perform bar to graph mean differences Regression Analysisregression Bivariate Plots and Correlations for Scale Explain the options of the LinearNonparametric Tests Variables Regression procedureDescribe the options in the Nonparametric Explain the Pearson correlation Interpret the results of the LinearTests procedure dialog box and tabs coefficient and its assumptions Regression procedureInterpret the results of several types of Interpret a Pearson correlation coefficient Use Automatic Linear Models to performnonparametric tests Explain the options of the Bivariate regression

Correlations procedure Nonparametric TestsRegression Analysis Describe the options in the Nonparametric

line Explain the options of the Linear Tests procedure dialog box and tabsExplain the basic steps of the research Regression procedure Interpret the results of several types of


process Interpret the results of the Linear nonparametric testsDescribe the levels of measurement used in Regression procedureIBM SPSS Statistics Use Automatic Linear Models to performUse the options in the Frequencies regression lineprocedure Nonparametric Tests Explain the basic steps of the researchUse the options in the Frequencies, Describe the options in the processDescriptives, and Explore procedures Nonparametric Tests procedure dialog Describe the levels of measurement usedExplain the influence of sample size box and tabs in IBM SPSS StatisticsUse the options in the Crosstabs procedure Interpret the results of several types of Use the options in the FrequenciesCheck the assumptions of the nonparametric tests procedureIndependent-Samples T Test Use the options in the Frequencies,Use the Paired-Samples T Test procedure Descriptives, and Explore proceduresUse the options in the One-Way ANOVA line Explain the influence of sample sizeprocedure Explain differences between populations Use the options in the CrosstabsVisually assess the relationship between two and samples procedurescale variables with scatterplots, using the Explain differences between experimental Check the assumptions of theChart Builder procedure and non-experimental research designs Independent-Samples T TestExplain linear regression and its Explain differences between independent Use the Paired-Samples T Test procedureassumptions and dependent variables Use the options in the One-Way ANOVADescribe when non-parametric tests should Understanding Data Distributions - procedureand can be used Theory Visually assess the relationship between

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graph to display mean differences Use the Chart Builder to create an error featuresThe Paired-Samples T Test bar to graph mean differences The Independent- Samples T TestInterpret the results of a Paired-Samples T Bivariate Plots and Correlations for Scale Use the Independent-Samples T Test toTest Variables test the difference in meansOne-Way ANOVA Explain the Pearson correlation Know how to interpret the results of aCheck the assumptions for One-Way coefficient and its assumptions Independent-Samples T TestANOVA Interpret a Pearson correlation coefficient Use the Chart Builder to create an errorInterpret the results of a One-Way ANOVA Explain the options of the Bivariate bar graph to display mean differencesanalysis Correlations procedure The Paired-Samples T TestUse the Chart Builder to create an error bar Regression Analysis Interpret the results of a Paired-Samples Tto graph mean differences Explain the options of the Linear TestBivariate Plots and Correlations for Scale Regression procedure One-Way ANOVAVariables Interpret the results of the Linear Check the assumptions for One-WayExplain the Pearson correlation coefficient Regression procedure ANOVAand its assumptions Use Automatic Linear Models to perform Interpret the results of a One-Way ANOVAInterpret a Pearson correlation coefficient regression analysisExplain the options of the Bivariate Nonparametric Tests Use the Chart Builder to create an errorCorrelations procedure Describe the options in the bar to graph mean differencesRegression Analysis Nonparametric Tests procedure dialog Bivariate Plots and Correlations for ScaleExplain the options of the Linear Regression box and tabs Variablesprocedure Interpret the results of several types of Explain the Pearson correlation coefficientInterpret the results of the Linear Regression nonparametric tests and its assumptionsprocedure Interpret a Pearson correlation coefficientUse Automatic Linear Models to perform Explain the options of the Bivariateregression line Correlations procedureNonparametric Tests Explain differences between populations Regression AnalysisDescribe the options in the Nonparametric and samples Explain the options of the LinearTests procedure dialog box and tabs Explain differences between experimental Regression procedureInterpret the results of several types of and non-experimental research designs Interpret the results of the Linearnonparametric tests Explain differences between independent Regression procedure

and dependent variables Use Automatic Linear Models to performUnderstanding Data Distributions - regression

line Theory Nonparametric TestsExplain differences between populations and Use measures of central tendency and Describe the options in the Nonparametricsamples dispersion Tests procedure dialog box and tabsExplain differences between experimental Use normal distributions and z-scores Interpret the results of several types ofand non-experimental research designs Data Distributions for Categorical nonparametric testsExplain differences between independent Variablesand dependent variables Interpret the results of the FrequenciesUnderstanding Data Distributions - Theory procedure lineUse measures of central tendency and Data Distributions for Scale Variables Explain differences between populationsdispersion Interpret the results of the Frequencies, and samplesUse normal distributions and z-scores Descriptives, and Explore procedures Explain differences between experimentalData Distributions for Categorical Variables Making Inferences about Populations and non-experimental research designsInterpret the results of the Frequencies from Samples Explain differences between independentprocedure Explain the nature of probability and dependent variablesData Distributions for Scale Variables Explain hypothesis testing Understanding Data Distributions - TheoryInterpret the results of the Frequencies, Explain different types of statistical errors Use measures of central tendency andDescriptives, and Explore procedures and power dispersionMaking Inferences about Populations from Explain differences between statistical Use normal distributions and z-scoresSamples and practical importance Data Distributions for CategoricalExplain the nature of probability Relationships Between Categorical VariablesExplain hypothesis testing Variables Interpret the results of the FrequenciesExplain different types of statistical errors Request appropriate statistics for a procedureand power crosstabulation Data Distributions for Scale VariablesExplain differences between statistical and Interpret cell counts and percents in a Interpret the results of the Frequencies,practical importance crosstabulation Descriptives, and Explore proceduresRelationships Between Categorical Use the Chi-Square test, interpret its Making Inferences about Populations fromVariables results, and check its assumptions SamplesRequest appropriate statistics for a Use the Chart Builder to visualize a Explain the nature of probabilitycrosstabulation crosstabulation Explain hypothesis testingInterpret cell counts and percents in a Use additional syntax-only Crosstabs Explain different types of statistical errorscrosstabulation features and powerUse the Chi-Square test, interpret its results, The Independent- Samples T Test Explain differences between statistical andand check its assumptions Use the Independent-Samples T Test to practical importanceUse the Chart Builder to visualize a test the difference in means Relationships Between Categorical


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Introduction to Statistical Analysis Using IBM SPSS (V20)...0G510G [email protected] (34) 91 425 06 60 Introduction to Statistical Analysis Using IBM SPSS (V20) Duración: