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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.