Applied Statistics for Economists

Front Cover
Applied Statistics for
Economists
Copyright page
Contents
List of figures
List of tables
List of charts
List of boxes
Preface
A note on the textbook’s focus and scope
A note on the “Where can I find. . .?” boxes
1. The role of statistics in economics
	1.1 Understanding the economy using empirical evidence
	1.2 Measuring economic welfare
	1.3 Distribution of household income
	1.4 Assessing full employment
	1.5 Calculating economic growth
	1.6 Measuring inflation
	1.7 Theoretical relationships between unemployment and economic growth
	1.8 The connection between economic theory and statistical evidence
	Summary
	Concepts introduced
	Exercises
2. Visual presentations of economic data
	2.1 Economic graphs and charts
	2.2 Time-series data and charts
	2.3 Cross-section data and charts
	2.4 Panel data and charts
	2.5 Creating effective charts
	2.6 Constructing charts using Excel
	Summary
	Concepts introduced
	Exercises
Part I: Descriptive statistics of an economic variable
	3. Observations and frequency distributions
		3.1 The design of observations: an introduction
		3.2 Attributes and measured variables
		3.3 Organizing data: absolute frequency distributions
		3.4 Organizing data: relative frequency distributions
		3.5 Visual presentations of frequency distribution: histograms
		3.6 Classes in a frequency distribution
		3.7 Constructing a frequency distribution
		3.8 Frequency polygons
		3.9 Cumulative frequency distributions and ogives
		Summary
		Concepts introduced
		Exercises
	4. Measures of central tendency
		4.1 Desirable properties for descriptive statistics
		4.2 Three measures of central tendency: mean, median, and mode
		4.3 Measures of central tendency for frequency distributions
		4.4 Weighted arithmetic means
		4.5 Geometric means
		4.6 Positional measures for ungrouped data
		4.7 Positional measures for a frequency distribution
		Summary
		Concepts introduced
		Exercises
	5. Measures of dispersion
		5.1 The concept of dispersion
		5.2 Populations and samples
		5.3 Range
		5.4 Interquartile range
		5.5 Average deviation
		5.6 The concept of the standard deviation
		5.7 Calculating the standard deviation for ungrouped data
		5.8 Calculating measures of dispersion for frequency distributions
		5.9 Locating extreme values
		5.10 The shape of frequency distributions
		5.11 Choosing the appropriate descriptive statistics
		5.12 Assessing relative dispersion: coefficient of variation
		5.13 Assessing relative dispersion: index of dispersion
		5.14 Depicting relative dispersion: the Lorenz curve
		5.15 Assessing relative dispersion: Gini coefficient of inequality
		Summary
		Concepts introduced
		Exercises
Part II: Temporal descriptive statistics
	6. Measuring changes in price and quantity
		6.1 Important index numbers in empirical economics
		6.2 Why economists use index numbers
		6.3 Constructing a simple price index
		6.4 Constructing a weighted price index
		6.5 Selecting appropriate weights for an index number
		6.6 Chained price indices
		6.7 Price index applications
		6.8 Shifting an index’s reference period
		6.9 Quantity indices
		6.10 Composite indices
		Summary
		Concepts introduced
		Exercises
	7. Descriptions of stability: short-run changes
		7.1 Measuring economic change over time
		7.2 Calculating percentage growth
		7.3 Compound growth
		7.4 Annualized growth rates from sub-annual rates
		7.5 Annualized growth rates from supra-annual rates
		7.6 Continuous compound growth
		7.7 Continuously compounded annual growth and logarithms
		Summary
		Concepts introduced
		Exercises
	8. Patterns of long-term change
		8.1 Economic growth over time
		8.2 Constant long-run rates of growth
		8.3 Growth by constant amounts
		8.4 Change over time by constant rates or by constant amounts?
		8.5 A complete model for describing change
		8.6 Seasonal effects
		8.7 Cyclical effects
		8.8 Irregular effects
		Summary
		Concepts introduced
		Exercises
Part III: Statistical inferences about a single variable
	9. Basic concepts in statistical inference
		9.1 Populations and samples revisited
		9.2 Sampling procedures
		9.3 Concepts of probability
		9.4 Probability distributions
		9.5 Continuous probability distributions: the normal distribution
		9.6 Continuous probability distributions: standard normal distribution
		9.7 Identifying a normal distribution
		9.8 The concept of the sampling distribution of means
		9.9 Sampling distribution of means and the Central Limit Theorem
		9.10 Sampling distribution of the Z-statistic
		Summary
		Concepts introduced
		Exercises
	10. Statistical estimation
		10.1 Sample surveys as a source of data
		10.2 Interval estimates of the population mean when the variance of the population is known
		10.3 Confidence levels and the precision of an interval estimate
		10.4 The t-distribution
		10.5 Confidence intervals for the population mean when the variance of the population is not known
		10.6 Confidence intervals for proportions, percentages, and rates
		10.7 Confidence intervals for differences between means and proportions
		Summary
		Concepts introduced
		Exercises
	11. Statistical hypothesis testing of a mean
		11.1 Testing hypotheses in economics: an analogy to criminal trials
		11.2 An overview of hypothesis testing in economics: evaluating truth in advertising
		11.3 Economic hypothesis testing: stating the hypotheses
		11.4 Economic hypothesis testing: selecting the level of significance
		11.5 Economic hypothesis testing: establishing the decision rule
		11.6 Economic hypothesis testing: constructing the test statistic and making a decision about the null hypothesis
		11.7 Testing hypotheses versus estimating confidence intervals
		11.8 Evaluating a statistical rule in terms of a Type I error
		11.9 Evaluating a statistical rule in terms of a Type II error
		11.10 The p-value and hypothesis testing
		Summary
		Concepts introduced
		Exercises
Part IV: Relationships between two variables
	12. Correlation analysis
		12.1 Statistical relationships between two variables
		12.2 Correlation analysis: descriptive statistics
		12.3 Testing the significance of the correlation coefficient
		12.4 Testing the sign on the correlation coefficient
		Summary
		Concepts introduced
		Exercises
	13. Simple linear regression analysis: descriptive measures
		13.1 Introduction to simple linear regression analysis
		13.2 The algebra of linear relationships for regression analysis
		13.3 Simple linear regression analysis: education and GDP
		13.4 The algebra of variations in linear regression relationships
		13.5 The coefficient of determination in regression analysis
		13.6 Simple linear regression analysis: infant mortality rates and skilled health personnel at birth
		Summary
		Concepts introduced
		Exercises
	14. Simple regression analysis: statistical inference
		14.1 The need for statistical inference in regression analysis
		14.2 Testing hypotheses about the GDP–education regression model’s slope coefficient
		14.3 Sampling distributions of the linear regression’s slope and intercept coefficients
		14.4 Establishing the null and alternative hypotheses for the slope coefficient
		14.5 Levels of significance and decision rules
		14.6 The test statistic and p-value for the slope coefficient
		14.7 An example of a hypothesis about the slope coefficient: infant mortality rates
		14.8 What hypothesis testing does and does not prove
		Summary
		Concepts introduced
		Exercises
	15. Simple regression analysis: variable scales and functional forms
		15.1 Rescaling variables and interpreting the regression coefficients
		15.2 Specifying the regression equation: functional forms
		15.3 Semi-log functional forms: the log–lin model
		15.4 Semi-log functional forms: the lin–log model
		15.5 Double-log functional form
		15.6 Other functional forms
		15.7 Selecting the appropriate functional form
		Summary
		Concepts introduced
		Exercises
Part V: Relationships between multiple variables
	16. Multiple regression analysis: estimation and interpretation
		16.1 Simple to multiple regression analysis: an introduction
		16.2 The multiple-variable linear regression model
		16.3 Specifying the independent variables and functional form for the multiple regression model
		16.4 Specifying a multiple regression model for infant mortality rates
		16.5 Estimating a multiple regression model for infant mortality rates
		Summary
		Concepts introduced
		Exercises
	17. Multiple regression analysis: ypothesis tests for partial regression coefficients and overall goodness of fit
		17.1 General procedures for testing the significance of the partial regression coefficients (bi)
		17.2 Testing the significance of partial regression coefficients (bi): IMR model
		17.3 Evaluating the overall goodness of fit
		17.4 Interpreting the infant mortality model’s overall goodness of fit
		17.5 Testing joint hypotheses about the regression model’s overall significance: basic concepts and procedures
		17.6 Testing joint hypothesis about the IMR multiple regression model’s overall significance
		17.7 Is each independent variable statistically related to the dependent variable?
		17.8 The “best” statistical multiple regression model
		17.9 The “better” statistical infant mortality model?
		17.10 Closing observations about the IMR multiple regression results
		Summary
		Concepts introduced
		Exercises
	18. Multiple regression analysis: dummy variables and statistical problems
		18.1 Independent dummy variables: basic concepts and considerations
		18.2 Independent dummy variables: infant mortality rates and sub-Saharan Africa
		18.3 Multicollinearity
		18.4 Model misspecification and omitted variable bias
		18.5 “Misbehaved” regression residuals: heteroskedasticity and serial correlation
		Summary
		Concepts introduced
		Exercises
Notes
Bibliography
Index