Table of contents for Essential statistics for economics, business and management / Teresa Bradley.

Bibliographic record and links to related information available from the Library of Congress catalog.

Note: Contents data are machine generated based on pre-publication provided by the publisher. Contents may have variations from the printed book or be incomplete or contain other coding.


Counter
Essential Statistics for Economics, Business and Management
Table of contents
Introduction:	An overview: 
 What is the subject of statistics about?
 Who needs statistics?
 Should we believe statistics? 
 
Part I: <titl to follow>
1. Data collection and its graphical presentation 
 1.1	Introduction to statistics	
 1.2	Data collection, samples, surveys and experiments
 1.3	Some sources of statistical data
 1.4	Sorting and classifying data 
 1.5	Bar charts and pie chart. Plotting in Excel 
 1.6	Graphs: histograms and Ogives. Graphs in Excel
 1.7	Line and Lorenz graphs. 
 1.8	Graphical misrepresentation of data
2. Descriptive statistics
		2.1	Summary statistics for raw data: mean, quartiles and mode 
		2.2	Summary statistics for grouped data: mean, quartiles and mode. 
		2.3	Measures of dispersion for raw data. Variance, QD, IQR
		2.4	Measures of dispersion for grouped data. Variance, QD, IQR
		2.5	Use of calculator for descriptive statistics.
2.6	Other descriptive statistics. CV, skewness and box plots.
2.7	Descriptive statistics in Excel 
3.	Regression and correlation; rank correlation
		3.1	Introduction to regression. Scatter plots and lines 
		3.2	The least-squares line. Criteria and equation for a best fit line
		3.3	Excel: XY (Scatter) plots, least-squares line and formulae 
		3.4	Coefficient of Determination and Correlation. 
		3.5	Rank correlation. Calculate and interpret rank correlation
		3.6	Use the calculator for regression and correlation. 
		3.7	Why bother with formulae?
4.	Probability 
4.1	Introduction to probability.
4.2	The multiplication and addition rules for probability 
4.3	Joint, marginal and conditional probability. 
4.4	Bayes Rule.
 5.	Probability distributions.
		5.1	Introduction: probability distributions and random variables
		5.2	The Binomial probability distributions 
		5.3	The Poisson probability distributions 
		5.4	Normal probability distribution 
		5.5	Expected values.
 
Part II: Statistical Inference
6.	Sampling distributions for means and proportions
		6.1	Statistical inference and sampling distribution of the mean.
		6.2	Sampling distribution for proportions for n > 30
		6.3	Some desirable properties of estimators
7	 Confidence intervals for means and proportions
		7.1	Confidence intervals for means 
		7.2	Confidence intervals for proportions
		7.3	Precision of a confidence interval: sample size determination 
		7.4	Confidence intervals for the difference between proportions
 
8.	Tests of hypothesis for means and proportions
		8.1	Tests of hypothesis for the population mean
		8.2	Tests of hypothesis for a population proportion
		8.3	Tests of hypothesis for difference between means and proportions 
		8.4	Minitab and Excel for confidence intervals and tests of hypothesis
9. Inference from small samples. Confidence intervals and tests of hypothesis 
9.1	Inference from small samples: Normal population, å known. 
9.2	The Student t-distribution
9.3	Inference from small samples: Normal population, å not known
9.4	Differences between means, small independent samples
9.5	F-tests for equality of variances
9.6	Difference between means, paired samples
10. Analysis of variance 
10.1	The rationale behind one-way analysis of variance
10.2	One-way Analysis of variance: The ANOVA Table 
10.3	Two-way Analysis of variance
10.4	Observation studies and Designed experiments
10.5	Excel and Minitab for ANOVA
11.		Chi-squared tests
11.1	Introduction
11.2	The ?2 probability distribution
11.3	Contingency tables
11.4 	Tests for independence (no association)
11.5 	Test for homogeneous populations 
11.6 	Tests for the equality of several proportions
11.7 	Goodness of fit tests
11.8 	Use of Excel and Minitab in ?2 tests
12.	Regression analysis 
12.1	The Simple Linear Regression model: description and model assumptions 
12.2	Inference about the population slope (rates of change)
12.3	Confidence intervals and prediction intervals 
12.4	Checks for the model assumptions based on residual plots.
12.5	Regression analysis in Minitab and Excel
12.6	Multiple Regression 

Library of Congress Subject Headings for this publication:

Economics -- Statistical methods.
Commercial statistics.