business statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar

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1 business statistics using Excel Glyn Davis & Branko Pecar OXFORD UNIVERSITY PRESS

2 Detailed contents Introduction to Microsoft Excel 2003 Overview Learning Objectives 1.1 Introduction to Microsoft Excel Components of an Excel spreadsheet Loading Excel Taskpanes Help Saving a workbook Opening an existing workbook 1.1. Closing a workbook Switch to a new worksheet Creating a new worksheet Opening a new workbook l.l.ii Printing and print preview What to do if you encounter a damaged Excel ffle 1.2 Entering Data and Formatting Entering data in a cell Modifying data in a cell Entering data into a range Cancelling a cell entry Undoing a cell entry Entering numbers 1.2. Entering dates or times Entering text Formatting a worksheet Column widths and row heights Naming a worksheet Inserting and deleting rows and columns The clear command Spell checking AutoFormat 1.3 Performing Calculations Entering formulae Understanding operators Selecting cells and moving around worksheet Reference operators Editing a formula l.3.6 Creating and applying names 1.3. Copying formulae to adjacent cells Using absolute and relative references

3 Detailed contents IF function Adding a column of numbers using AutoSum What if analysis Insert function Copying formulae and values Excel arrays Install Excel ToolPakadd-ins Presenting Results Inserting Excel features into Microsoft Word Inserting a chart from an Excel worksheet into Microsoft Word 25 Techniques in Practice 28 Summary 29 Key Terms 29 Further Reading 30 2 Numerical Skills Revision 31 Overview 31 Learning Obj ectives Excel and Solving Algebraic Problems Excel scientific notation Squares Square roots Indices or the power of a number Excel Mathematical and Statistical Functions Mathematical functions Excel arrays Excel statistical functions Excel statistical macro functions Excel Financial Functions Simple interest Compound interest Increasing the sum invested Sinking funds or future value of an ordinary annuity The concept of present value Trust funds and loan repayments or present value of an ordinary annuity The present value of a stream of earnings Compound annual growth rate Coordinate Geometry The coordinates of a point Excel and plotting straight line graphs from an equation Linear equation parameters m and с Use Excel to calculate the equation of the line given data points 0 Techniques in Practice 1 Summary 2 Key Terms 2 Further Reading 3 Formula Summary 3

4 3 Visualizing and Presenting Data Overview Learning Objectives 3.1 The Different Types of Data Variable 3.2 Tables What a table looks like Creating a frequency distribution Types of data Creating a table using Excel Pivot Table Principles of table construction 3.3 Graphical Representation of Data Bar charts Pie charts Histograms Histograms with unequal class intervals Frequency polygon Scatter and time series plots 3.3. Superimposing two sets of data onto one graph Techniques in Practice Summary Key Terms Further Reading 4 Data Descriptors Overview Learning Objectives 4.1 Measures of Central Tendency Mean, median, and mode The percentile and quartile Averages from frequency distributions Weighted averages 4.2 Measures of Dispersion The range The interquartile range and semi interquartile range The standard deviation and variance The coefficient of variation Measures of skewness and kurtosis 4.3 Exploratory Data Analysis Five-number summary Box plots Using the Excel ToolPak add-in Techniques in Practice Summary Key Terms Further Reading Formula Summary

5 Probability Distributions 1 Overview 1 Learning Objectives Introduction to Probability Basic ideas Relative frequency The probability laws Probability tree diagram Introduction to probability distributions Expectation and variance for a probability distribution Continuous Probability Distributions Introduction The normal distribution The standard normal distribution (Z distribution) Checking for normality Other continuous probability distributions Discrete Probability Distributions Introduction Binomial probability distribution Poisson probability distribution Poisson approximation to the binomial distribution Normal approximation to the binomial distribution Normal approximation to the Poisson distribution Other discrete probability distributions 233 Techniques in Practice 234 Summary 235 Key Terms 235 Further Reading 236 Formula Summary 236 Sampling Distributions and Estimating 238 Overview 238 Learning Objectives Introduction to the Concept of a Sample Why sample? Sampling terminology Types of samples Types of error Sampling from a Population Introduction Population vs. sample Sampling distributions Sampling distribution of the mean Sampling from a normal population Sampling from a non-normal population 259

6 6.2. Sampling distribution ofthe proportion Using Excel to generate a sample from a sampling distribution Population Point Estimates introduction Types of estimate Criteria of a good estimator Point estimate ofthe population mean and variance Point estimate for the population proportion and variance Pooled estimates Population Confidence Intervals Introduction Confidence interval estimate ofthe population mean, i(o" known) Confidence interval estimate of the population mean, (l (a unknown, n < 30) Confidence interval estimate ofthe population mean, \i (a unknown, n > 30) Confidence interval estimate of a population proportion Calculating Sample Sizes 288 Techniques in Practice 291 Summary 292 Key Terms 293 Further Reading 293 Formula Summary 294 Introduction to Parametric Hypothesis Testing 29 Overview 29 Learning Objectives 29.1 Hypothesis Testing Rationale Hypothesis statements H 0 and Hj Parametric vs. non-parametric tests of difference One and two sample tests Choosing an appropriate test Significance level Sampling distributions One and two tail tests Checkt test model assumptions Types of error P-values Critical test statistic One Sample Z Test for the Population Mean 30.3 One Sample T Test for the Population Mean Two Sample Z Test for the Population Mean 31.5 Two Sample Z Test for the Population Proportion Two Sample T Test for Population Mean (Independent Samples, Equal Variances) 326

7 . Two Sample Tests for Population Mean (Independent Samples, Unequal Variances) Two sample t test for independent samples (unequal variances) Equivalent non-parametric test: Mann-Whitney U test Two Sample Tests for Population Mean (Dependent or Paired Samples) Two sample t test for dependent samples Equivalent non-parametric test: Wilcoxon matched pairs test F Test for Two Population Variances (Variance Ratio Test) 341 Techniques in Practice 346 Summary 348 Key Terms 349 Further Reading 349 Formula Summary 350 Chi Square and Non-Parametric Hypothesis Testing 352 Overview Learning Objectives 8.1 Chi Square Tests Tests of association Test differences in proportions for two samples 8.2 Non-Parametric (or Distribution Free) Tests The sign test Wilcoxon signed rank sum (or matched pairs) test Mann-Whitney U test for two independent samples Techniques in Practice Summary Key Terms Further Reading Formula Summary Factorial Experiments 393 Overview 393 Learning Objectives Introduction Single-Factor Experiments Single-factor ANOVA (or one-way ANOVA) Kruskal-Wallis test Two-Factor Experiments with No Replication 418

8 9.3.1 Two-way ANOVA with no repeated measures (or one-factor with repeated measures) Friedman's test Two-Factor Experiments with Equal Replication Two-way ANOVA with equal replication Friedman's test with equal replicates ineachcell 443 Techniques in Practice 445 Summary 44 Key Terms 448 Further Reading 448 Formula Summary 449 Linear Correlation and Regression Analysis 454 Overview 454 Learning Objectives Linear Correlation Analysis Scatter plot Covariance Pearson's correlation coefficient, r Testing the significance of Pearson's correlation coefficient, r Spearman's rank correlation coefficient, r s Testing the significance of Spearman's rank correlation coefficient, r Linear Regression Analysis Construct scatter plot to identify model Fit line to sample data Sum of squares defined Regression assumptions Test model reliability Test whether the predictor variable is a significant contributor, t test Test whether the predictor variable is a significant contributor, F test Confidence interval estimate for slope ßj Prediction interval for an estimate of Y Excel ToolPak Regression solution Some Advanced Topics in Regression Analysis Introduction to non-linear regression Introduction to multiple regression analysis 505 Techniques in Practice 509 Summary 512 KeyTerms 513 Further Reading 514 Formula Summary 514

9 Detailed contents 11 Time Series Data and Analysis 518 Overview 518 Learning Obj ectives Introduction to Time Series Data Stationary and non-stationary time series Seasonal time series Univariate and multivariate methods Scaling the time series Index Numbers Simple indices Aggregate indices Deflating values Trend Extrapolation A trend component Fitting a trend to a time series Types of trends Using a trend chart function to forecast time series Trend parameters and calculations Moving Averages and Time Series Smoothing Forecasting with moving averages Exponential smoothing concept Forecasting with exponential smoothing Seasonal Models Forecasting Errors Error measurement Types of errors Interpreting errors Error inspection Confidence Intervals Population and sample standard errors Standard errors in time series 565 Techniques in Practice 569 Summary 51 Key Terms 51 Further Reading 52 Formula Summary 53 Student Exercises Answers 5 Glossary 595 Index 601

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