Business Statistics For Dummies
Business Statistics For Dummies helps you understand the core concepts and principles of business statistics, and how they relate to the business world. This book tracks to a typical introductory course offered at the undergraduate, so you know you’ll find all the content you need to pass your class...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Inc
[2024]
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| Edición: | Second edition |
| Colección: | --For dummies.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009811321906719 |
Tabla de Contenidos:
- Intro
- 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.
