Business Statistics Australia New Zealand 6th Edition by Antony Selvanathan, Saroja Selvanathan, Gerald Keller 0170237001 9780170237000
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Product details:
ISBN 10: 0170237001
ISBN 13: 9780170237000
Author: E. Antony Selvanathan; Saroja Selvanathan; Gerald Keller
Table of contents:
1 What is statistics?
Introduction to statistics
1.1 Key statistical concepts
1.2 Practical applications
Case 3.6
Case 5.9
Case 15.1
Case 17.1
Case 18.1
1.3 How managers use statistics
1.4 Statistics and the computer
1.5 Online resources
Appendix 1. A Introduction to Microsoft Exce
l
2 Types of data, data collection and sampling
Introduction
2.1 Types of data
2.2 Methods of collecting data
2.3 Sampling
2.4 Sampling plans
2.5 Sampling and non-sampling errors
Summary
Part 1 Descriptive measures
and probability
3 Graphical descriptive techniques – Nominal data
Introduction
3.1 Graphical techniquesto describe nominal data
3.2 Selecting a chart: Which chart is best?
3.3 Graphical techniques to describeordinal data
3.4 Describing the relationship betweentwo nominal variables
Summary
Case 3.1
Case 3.2
Case 3.3
Case 3.4
Case 3.5
Case 3.6
Case 3.7
Case 3.8
Case 3.9
Case 3.10
4 Graphical descriptive techniques –Numerical data
Introduction
4.1 Graphical techniques to
describe numerical data
4.2 Describing time-series data
4.3 Describing the relationship between
two numerical variables
4.4 Graphical excellence and deception
Summary
Case 4.1
Case 4.2
Case 4.3
5 Numerical descriptive measures
Introduction
5.1 Measures of central location
5.2 Measures of variability
5.3 Measures of relative standing and
box plots
5.4 Approximating descriptive
measures for grouped data
5.5 Measures of association
5.6 General guidelines on
the exploration of data
Summary
Case 5.1
Case 5.2
Case 5.3
Case 5.4
Case 5.5
Case 5.6
Case 5.7
Case 5.8
Case 5.9
Appendix 5. A Summation notation
6 Probability
Introduction
6.1 Assigning probabilities to events
6.2 Joint, marginal and conditional probability
6.3 Rules of probability
6.4 Probability trees
6.5 Bayes’ law
6.6 Identifying the correct method
Summary
Case 6.1
Case 6.2
Case 6.3
Case 6.4
Case 6.5
Case 6.6
7 Random variables and discrete probability distributions
Introduction
7.1 Random variables and probability
distributions
7.2 Discrete probability distributions
7.3 Expected value and variance
7.4 Bivariate distributions
7.5 Applications in finance: Portfolio diversification and asset allocation
7.6 Binomial distribution
7.7 Poisson distribution
Summary
Case 7.1
Case 7.2
Case 7.3
Case 7.4
8 Continuous probability distributions
8.1 Probability density functions
8.2 Uniform distribution
8.3 Normal distribution
8.4 Exponential distribution
Summary
Part 2: Statistical inference
9 Statistical inference:
Introduction
Introduction
9.1 Data type and problem objective
9.2 How, when and why of statistical
inference
9.3 Systematic approach to statistical
inference: A summary
10 Sampling distributions
Introduction
10.1 Sampling distribution of thesample mean X
10.2 Sampling distribution of the sample
proportion p
10.3 From here to inference
Summary
11 Estimation: Describing
a single population
Introduction
11.1 Concepts of estimation
11.2 Estimating the population mean μ when the population variance σ2 is known
11.3 Estimating the population mean μ when the population variance σ2 is unknown
11.4 Estimating the population proportion p
11.5 Determining the required sample size
11.6 Applications in marketing:
Market segmentation
Summary
Case 11.1
Case 11.2
Case 11.3
12 Estimation: Comparing two
populations
Introduction
12.1 Estimating the difference between two population means μ1 − μ2 when the variances σ1 2 and σ2 2 are known: Independent samples
12.2 Estimating the difference between two population means μ1 − μ2 when the variances σ1 2 and σ2 2 are unknown: Independent samples
12.3 Matched pairs experiments:
dependent samples
12.4 Estimating the difference between two population proportions p1 − p2
Summary
Case 12.1
Case 12.2
13 Hypothesis testing: Describing a single
population
Introduction
13.1 Concepts of hypothesis testing
13.2 Testing the population mean when
the variance σ2 is known
13.3 The p-value of a test of hypothesis
13.4 Testing the population mean when the
variance σ2 is unknown
13.5 Calculating the probability of a Type II error
13.6 Testing the population proportion
Summary
Case 13.1
Case 13.2
Case 13.3
Appendix 13.
A Excel instructions
14 Hypothesis testing:
Comparing two populations
Introduction
14.1 Testing the difference between two population means: Independent samples
14.2 Testing the difference between two
population means: Matched pairs experiment
14.3 Testing the difference between
two population proportions
Summary
Case 14.1
Case 14.2
Case 14.3
Case 14.4
Case 14.5
Case 14.6
Appendix 14.A
Excel instructions
15 Inference about population
variances
Introduction
15.1 Inference about σ2
15.2 Inference about σ1
2 /σ 2 2
Summary
Case 15.1
Case 15.2
Case 15.3
16
Analysis of variance
Introduction
16.1 Single-factor analysis of variance: Independent samples (one-way ANOVA)
16.2 Multiple comparisons
16.3 Analysis of variance:
Experimental designs
16.4 Single-factor analysis of variance:
Randomised blocks (two-way ANOVA)
16.5 Two-factor analysis of variance
Summary
Case 16.1
Case 16.2
Case 16.3
Case 16.4
Case 16.5
17 Additional tests for nominal data: Chi-squared tests
Introduction
17.1 Chi-squared test of a multinomial
experiment
17.2 Chi-squared test of a contingency table
17.3 Chi-squared test for normality
17.4 Summary of tests on nominal data
Summary
Case 17.1
Case 17.2
Case 17.3
18 Simple linear regression and correlation
Introduction
18.1 Model
18.2 Estimating the coefficients
18.3 Error variable: Required conditions
18.4 Assessing the model
18.5 Using the regression equation
18.6 Coefficients of correlation
18.7 Regression diagnostics – I
Summary
Case 18.1
Case 18.2
Case 18.3
Case 18.4
Case 18.5
Case 18.6
Case 18.7
19
Multiple regression
Introduction
19.1 Model and required conditions
19.2 Estimating the coefficients and assessing the model
19.3 Regression diagnostics – II
19.4 Regression diagnostics – III (time series)
Summary
Case 19.1
Case 19.2
Case 19.3
Case 19.4
Case 19.5
20
Model building
Introduction
20.1 Polynomial models
20.2 Nominal independent variables
20.3 Applications in human resources: Pay equity
20.4 Logistic regression
20.5 Variable selection methods
20.6 Stepwise regression
20.7 Model building
Summary
Case 20.1
Case 20.2
21
Nonparametric statistics
Introduction
21.1 Wilcoxon rank sum test
21.2 Sign test and Wilcoxon signed rank sum
test
21.3 Kruskal–Wallis test and Friedman test
for independent samples
Sumary
Case 21.1
Case 21.2
22 Statistical inference:
conclusion
Introduction
22.1 Identifying the correct technique:
Summary of statistical inference
22.2 Beyond the statistics subject – the last word
Case 22.1
Case 22.2
Case 22.3
Case 22.4
Case 22.5
Case 22.6
Case 22.7
Case 22.8
Case 22.9
Part 3
Applications
23 Time series analysis
and forecasting
Introduction
23.1 Components of a time series
23.2 Smoothing techniques
23.4 Measuring the cyclical effect
23.5 Measuring the seasonal effect
23.6 Introduction to forecasting
23.7 Time series forecasting with
exponential smoothing
23.8 Time series forecasting with regression
Summary
Case 23.1
Case 23.2
Case 23.3
24
Index numbers
Introduction
24.1 Constructing unweighted index numbers
24.2 Constructing weighted index numbers
24.3 The Australian consumer price index (CPI)
24.4 Using the CPI to deflate wages and GDP
24.5 Changing the base period of an index number series
Summary
Case 24.1
Case 24.2
25
Decision analysis
Introduction
25.1 Decision problem
25.2 Using additional information
Summary
Appendix A Summary Solutions
for Selected Exercises
Appendix B
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Tags: Antony Selvanathan, Saroja Selvanathan, Gerald Keller, Business