Business Statistics for Dummies:
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Newark
John Wiley & Sons, Incorporated
2024
|
| Edition: | 2nd ed |
| Subjects: | |
| Online Access: | DE-2070s |
| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | 1 Online-Ressource (403 Seiten) |
| ISBN: | 9781394219933 |
Staff View
MARC
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| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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! | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a Two-tailed test: Testing hypotheses about the population variance | |
| 650 | 4 | |a Commercial statistics | |
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| author | Anderson, Alan |
| author_facet | Anderson, Alan |
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| contents | 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 |
| ctrlnum | (ZDB-30-PQE)EBC31050878 (ZDB-30-PAD)EBC31050878 (ZDB-89-EBL)EBL31050878 (OCoLC)1416747758 (DE-599)BVBBV049871530 |
| dewey-full | 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5 |
| dewey-search | 519.5 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | 2nd ed |
| format | Electronic eBook |
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| id | DE-604.BV049871530 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-05T17:02:42Z |
| institution | BVB |
| isbn | 9781394219933 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035211005 |
| oclc_num | 1416747758 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (403 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | John Wiley & Sons, Incorporated |
| record_format | marc |
| spelling | Anderson, Alan Verfasser aut Business Statistics for Dummies 2nd ed Newark John Wiley & Sons, Incorporated 2024 ©2024 1 Online-Ressource (403 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources 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 Commercial statistics Statistics Betriebswirtschaftliche Statistik (DE-588)4006243-0 gnd rswk-swf Betriebswirtschaftliche Statistik (DE-588)4006243-0 s DE-604 Erscheint auch als Druck-Ausgabe Anderson, Alan Business Statistics for Dummies Newark : John Wiley & Sons, Incorporated,c2024 9781394219926 |
| spellingShingle | Anderson, Alan Business Statistics for Dummies 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 Commercial statistics Statistics Betriebswirtschaftliche Statistik (DE-588)4006243-0 gnd |
| subject_GND | (DE-588)4006243-0 |
| title | Business Statistics for Dummies |
| title_auth | Business Statistics for Dummies |
| title_exact_search | Business Statistics for Dummies |
| title_full | Business Statistics for Dummies |
| title_fullStr | Business Statistics for Dummies |
| title_full_unstemmed | Business Statistics for Dummies |
| title_short | Business Statistics for Dummies |
| title_sort | business statistics for dummies |
| topic | Commercial statistics Statistics Betriebswirtschaftliche Statistik (DE-588)4006243-0 gnd |
| topic_facet | Commercial statistics Statistics Betriebswirtschaftliche Statistik |
| work_keys_str_mv | AT andersonalan businessstatisticsfordummies |