Business Statistics For Dummies

Title Page
Copyright Page
Table of Contents
Introduction
	About This Book
	Foolish Assumptions
	Icons Used in This Book
	Beyond the Book
	Where to Go from Here
Part 1 Getting Started with Business Statistics
	Chapter 1 The Art and Science of Business Statistics
		Representing the Key Properties of Data
			Analyzing data with graphs
				Histograms
				Line graphs
				Pie charts
				Scatter plots
			Defining properties and relationships with numerical measures
				Finding the center of the data
				Measuring the spread of the data
				Determining the relationship between two variables
		Probability: The Foundation of All Statistical Analysis
			Random variables
			Probability distributions
				Discrete probability distributions
				Continuous probability distributions
		Using Sampling Techniques and Sampling Distributions
		Statistical Inference: Drawing Conclusions from Data
			Confidence intervals
			Hypothesis testing
			Simple regression analysis
	Chapter 2 Pictures Tell the Story: Graphical Representations of Data
		Analyzing the Distribution of Data by Class or Category
			Frequency distributions for quantitative data
				Figuring the class width
				Observing relative frequency distributions
			Frequency distribution for qualitative values
			Cumulative frequency distributions
		Histograms: Getting a Picture of Frequency Distributions
		Checking Out Other Useful Graphs
			Line graphs: Showing the values of a data series
			Pie charts: Showing the composition of a data set
			Scatter plots: Showing the relationship between two variables
	Chapter 3 Identifying the Center of a Data Set
		Looking at Methods for Finding the Mean
			Arithmetic mean
				Calculating the sample arithmetic mean
				Calculating the population arithmetic mean
			Geometric mean
			Weighted mean
				Calculating the weighted arithmetic mean
		Getting to the Middle of Things: The Median of a Data Set
		Determining the Relationship Between the Mean and Median
			Symmetrical
			Negatively skewed
			Positively skewed
		Discovering the Mode: The Most Frequently Repeated Element
		Computing the Mean, Median, and Mode with the TI-84 Plus Calculator
	Chapter 4 Measuring Variation in a Data Set
		Determining Variance and Standard Deviation
			Finding the sample variance
			Finding the sample standard deviation
			Calculating population variance and standard deviation
				Finding the population variance
				Finding the population standard deviation
				Finding the population standard deviation
		Finding the Relative Position of Data
			Percentiles: Dividing everything into hundredths
			Quartiles: Dividing everything into fourths
			Interquartile range: Identifying the middle 50 percent
		Measuring Relative Variation
			Coefficient of variation: The spread of a data set relative to the mean
			Comparing the relative risks of two portfolios
		Computing Measures of Dispersion with the TI-84 Plus Calculator
	Chapter 5 Measuring How Data Sets Are Related to Each Other
		Understanding Covariance and Correlation
			Sample covariance and correlation coefficient
			Population covariance and correlation coefficient
			Comparing correlation and covariance
		Interpreting the Correlation Coefficient
			Showing the relationship between two variables
			Application: Correlation and the benefits of diversification
		Computing Covariance and Correlation with the TI-84 Plus Calculator
Part 2 Probability Theory and Probability Distributions
	Chapter 6 Probability Theory: Measuring the Likelihood of Events
		Working with Sets
			Membership
			Subset
			Union
			Intersection
			Complement
		Betting on Uncertain Outcomes
			The sample space: Everything that can happen
			Event: One possible outcome
				Mutually exclusive events
				Independent events
			Computing probabilities of events
		Looking at Types of Probabilities
			Unconditional (marginal) probabilities: When events are independent
			Joint probabilities: When two things happen at once
			Conditional probabilities: When one event depends on another
			Determining independence of events
		Following the Rules: Computing Probabilities
			Addition rule
			Complement rule
			Multiplication rule
	Chapter 7 Probability Distributions and Random Variables
		Defining the Role of the Random Variable
		Assigning Probabilities to a Random Variable
			Calculating the probability distribution
			Visualizing a probability distribution with a histogram
		Characterizing a Probability Distribution with Moments
			Understanding the summation operator (Σ)
			Expected value
			Variance and standard deviation
	Chapter 8 The Binomial and Poisson Distributions
		Looking at Two Possibilities with the Binomial Distribution
			Checking out the binomial distribution
			Computing binomial probabilities
				Factorial: counting how many ways you can arrange things
				Combinations: Counting how many choices you have
				Binomial formula: Computing the probabilities
			Moments of the binomial distribution
				Binomial distribution: Calculating the expected value
				Binomial distribution: Computing variance and standard deviation
			Graphing the binomial distribution
		Keeping the Time: The Poisson Distribution
			Computing Poisson probabilities
				Poisson distribution: Calculating the expected value
				Poisson distribution: Computing variance and standard deviation
			Graphing the Poisson distribution
		Computing Binomial and Poisson Probabilities with the TI-84 Plus Calculator
			Computing binomial probabilities
			Computing Poisson probabilities
	Chapter 9 The Normal Distribution: So Many Possibilities!
		Comparing Discrete and Continuous Distributions
		Understanding the Normal Distribution
			Graphing the normal distribution
			Getting to know the standard normal distribution
			Computing standard normal probabilities
				Computing “less than or equal to” standard normal probabilities
				Property 1: The area under the standard normal curve equals 1
				Property 2: The standard normal curve is symmetrical about the mean
				Computing “greater than or equal to” standard normal probabilities
				Computing “in between” standard normal probabilities
			Computing normal probabilities other than standard normal
		Computing Probabilities for the Normal Distribution with the TI-84 Plus Calculator
	Chapter 10 Sampling Techniques and Distributions
		Sampling Techniques: Choosing Data from a Population
			Probability sampling
				Simple random samples
				Systematic samples
				Stratified samples
				Cluster samples
			Nonprobability sampling
				Convenience samples
				Quota samples
				Purposive samples
				Judgment samples
		Sampling Distributions
			Portraying sampling distributions graphically
			Moments of a sampling distribution
		The Central Limit Theorem
			Converting  to a standard normal random variable
Part 3 Drawing Conclusions from Samples
	Chapter 11 Confidence Intervals and the Student’s t-Distribution
		Almost Normal: The Student’s t-Distribution
			Properties of the t-distribution
			Degrees of freedom
			Moments of the t-distribution
		Graphing the t-Distribution
		Probabilities and the t-Table
		Point Estimates vs. Interval Estimates
		Estimating Confidence Intervals for the Population Mean
			Known population standard deviation
			Unknown population standard deviation
		Computing Confidence Intervals for the Population Mean with the TI-84 Plus Calculator
			Population standard deviation is known
			Population standard deviation is unknown
	Chapter 12 Testing Hypotheses about the Population Mean
		Applying the Key Steps in Hypothesis Testing for a Single Population Mean
			Writing the null hypothesis
			Coming up with an alternative hypothesis
				Right-tailed test
				Left-tailed test
				Two-tailed test
			Choosing a level of significance
			Computing the test statistic
			Comparing the critical value(s)
				Population standard deviation is unknown
				Population standard deviation is known
			Using the decision rule
		Testing Hypotheses About Two Population Means
			Writing the null hypothesis for two population means
			Defining the alternative hypotheses for two population means
			Determining the test statistics for two population means
				Using independent samples
				Working with dependent samples
		Testing Hypotheses about Population Means with the TI-84 Plus Calculator
			Single population mean
			Two population means
	Chapter 13 Applications of the Chi-Square Distribution
		Staying Positive with the Chi-Square Distribution
			Representing the chi-square distribution graphically
			Defining a chi-square random variable
			Checking out the moments of the chi-square distribution
		Testing Hypotheses about the Population Variance
			Defining what you assume to be true: The null hypothesis
			Stating the alternative hypothesis
				Right-tailed test: Determining whether the hypothesized variance is too low
				Left-tailed test: Determining whether the hypothesized variance is too high
				Two-tailed test: Determining whether the hypothesized variance is too low or too high
			Choosing the level of significance
			Calculating the test statistic
			Determining the critical value(s)
				Right-tailed test: Testing hypotheses about the population variance
				Left-tailed test: Testing hypotheses about the population variance
				Two-tailed test: Testing hypotheses about the population variance
				Making the decision
		Practicing the Goodness of Fit Tests
			Comparing a population to the Poisson distribution
			Comparing a population to the normal distribution
		Conducing a Goodness of Fit Test with the TI-84 Plus Calculator
	Chapter 14 Applications of the F-Distribution
		Getting to Know the F-Distribution
			Defining an F random variable
			Measuring the moments of the F-distribution
		Testing Hypotheses about the Equality of Two Population Variances
			The null hypothesis: Equal variances
			The alternative hypothesis: Unequal variances
			The test statistic
			The critical value(s)
				Right-tailed test for the F-distribution
				Left-tailed test for the F-distribution
				Two-tailed test for the F-distribution
			The decision about the equality of two population variances
		Testing Hypotheses about Two Population Variances with the TI-84 Plus Calculator
Part 4 More Advanced Techniques: Regression Analysis and Spreadsheet Modeling
	Chapter 15 Simple Regression Analysis
		The Fundamental Assumption: Variables Have a Linear Relationship
			Defining a linear relationship
			Using scatter plots to identify linear relationships
		Defining the Population Regression Equation
		Estimating the Population Regression Equation
		Testing the Estimated Regression Equation
			Using the coefficient of determination ()
			Computing the coefficient of determination
			The t-test
				Null and alternative hypotheses
				Level of significance
				Test statistic
				Critical values
				Decision rule
		Using Statistical Software
		Assumptions of Simple Linear Regression
		Conducting Simple Regression Analysis with the TI-84 Plus Calculator
	Chapter 16 Key Statistical Techniques in Excel
		Implementing Excel Functions
		Checking Out Excel’s Key Statistical Functions
			Measures of central tendency
				Mean
				Median
				Mode
			Measures of dispersion
				Variance
				Standard deviation
			Measures of association
				Covariance
				Correlation
			Discrete probability distributions
				Binomial distribution
				Poisson distribution
			Continuous probability distributions
				Normal distribution
				The standard normal distribution
				t-distribution
			Confidence intervals
			Regression analysis
		Going Deeper with the Analysis ToolPak
			Computing covariance and correlation
			Computing descriptive statistics
			Regression analysis
			Hypothesis testing
Part 5 The Part of Tens
	Chapter 17 Ten Common Errors That Arise in Statistical Analysis
		Designing Misleading Graphs
		Drawing the Wrong Conclusion from a Confidence Interval
		Misinterpreting the Results of a Hypothesis Test
		Placing Too Much Confidence in the Coefficient of Determination ()
		Assuming Normality
		Thinking Correlation Implies Causality
		Drawing Conclusions from a Regression Equation When the Data Do Not Follow the Assumptions
		Using Regression Analysis to Make Predictions About Values Outside the Range of Sample Data
		Placing Too Much Confidence in Forecasts
		Using the Wrong Distribution
	Chapter 18 (Almost) Ten Key Categories of Formulas for Business Statistics
		Summary Measures of a Population or a Sample
		Probability
		Discrete Probability Distributions
		Continuous Probability Distributions
		Sampling Distributions
		Confidence Intervals for the Population Mean
		Testing Hypotheses about Population Means
		Testing Hypotheses about Population Variances
		Using Regression Analysis
Index
EULA