Miah A.Q. Applied Statistics for Social and Management Sciences

Springer, 2016. — 446 p. — ISBN: 9811003998, 9789811003998Presents the application of statistical techniques in an easy-to-understand format. Makes statistical concepts clear through the use of everyday examples. Provides a useful tool for research work in validating and invalidating research hypotheses. Excellent reading for researchers, students and practitioners.This book addresses the application of statistical techniques and methods across a wide range of disciplines. While its main focus is on the application of statistical methods, theoretical aspects are also provided as fundamental background information. It offers a systematic interpretation of results often discovered in general descriptions of methods and techniques such as linear and non-linear regression. SPSS is also used in all the application aspects. The presentation of data in the form of tables and graphs throughout the book not only guides users, but also explains the statistical application and assists readers in interpreting important features. The analysis of statistical data is presented consistently throughout the text. Academic researchers, practitioners and other users who work with statistical data will benefit from reading Applied Statistics for Social and Management Sciences.Basics
History
Statistics
Contents of Statistics
Data
Level of Measurement
VariablePresentation of Statistical Data
Tabular Presentation
Graphical PresentationDescriptive Statistics
Central Tendency
Measures of DispersionProbability Theory
Probability Definition
Two Approaches in Calculating Probability
Axioms of Probability
Probability in Mutually Exclusive Events
Probability in Independent Events
Probability in Dependent Events (Conditional/Unconditional Probability)
Probability in Non-mutually Exclusive Events
Probability and Number of Possible Samples
Probability Distributions
Discrete Probability Distribution
Continuous Probability Distribution
The Normal Distribution
The t Distribution
The F Distribution
The Chi-Square Distribution
Joint Probability Distribution
Data Fitting to Probability Distribution
Statistical Inference
Parameter and Statistics
Estimation
Properties of Estimators
Central Limit Theorem
Some Examples in Estimation
Point Estimation
Interval Estimation/Confidence Interval of the Mean of a Single Population
Confidence Interval of the Difference of Means of Two Normal Populations
Confidence Interval of the Variance of a Normal Population
Confidence Interval of a Population Proportion
Confidence Interval of the Difference of Two Population Proportions
Finite Population Correction Factor
Hypothesis TestingTest Procedure
Hypothesis Testing — One Population Mean (Variance Known)
Hypothesis Testing — One Population Mean (Variance Unknown — Large Sample)
Hypothesis Testing — Equality of Two Population Means (Variance Known)
Hypothesis Testing — One Population Mean (Variance Unknown)
Hypothesis Testing — Equality of Two Population Means (Variance Unknown — Small Sample)
Testing of Hypothesis — Population Proportion
Power of Hypothesis Testing
The Chi-Square Test
Goodness-of-Fit Test
Test of Independence
Nonparametric Test
The Sign Test
The Rank Test
Method in Analysis of VarianceCorrelation
Simple Regression
Simple Linear Regression Model
Hypothesis Testing in Simple Linear Regression
Confidence Intervals of Parameters
Adequacy of the Regression Model
Coefficient of Determination
Data Transformation
Interpretation of Simple Regression Model
Multiple Regression
Multiple Regression Model
Interpretation
Prediction
Use of Dummy Variables
Other Regression ModelsSampling Theory
Advantages of Sampling
Considerations Prior to Sample Survey
Considerations in Sampling
Principal Steps Involved in the Choice of a Sample Size
Types of Commonly Used Sampling Methods
Determination of Sample Size
Basic Principle
Sample Size in the Case of Random Sampling (Continuous Data)
Sample Size in Case of Simple Random Sampling (Proportion)
Sample Size in the Case of Stratified Sampling (Means)
Sample Size in the Case of Stratified Sampling (Proportion)
Simple Cluster Sampling
Index Numbers
Priority
Satisfaction
Agreement
Performance
Price Index
Analysis of Financial Data
Financial Terms
Calculation of Net Present Values
Project Investment Data
Risk and Statistical Estimation of IRR
Time Series Financial Data
Models for Time Series Financial Data
Forecasting
Seasonal VariationExperimental Design
Definition of Design of Experiments
Terms Related to Experimental Design
Procedure for Design of Experiments
Types of Designs
Illustration of a Completely Randomized Design
Illustration of a 2³ Full Factorial Design
Concept of Anova
Single Factor Experiments
Statistical Quality Control
History
Areas of Statistical Quality Control
Variation
Control ChartSummary for Hypothesis Testing
PrerequisiteStatistical Tables