Basic Statistics for Business and Economics Canadian 5th Edition Lind Solutions Manual

Basic Statistics for Business and Economics Canadian 5th Edition Lind Solutions Manual

Product details: 

• ISBN-10 ‏ : ‎ 1259103587
• ISBN-13 ‏ : ‎ 978-1259103582
• Author: Douglas Lind, William Marchal, Samuel Wathen, Carol Ann Waite

Basic Statistics for Business & Economics, Fifth Canadian Edition, provides Canadian business students majoring in economics, finance, marketing, accounting, management, human resource management, and other fields of business administration with an introductory survey of the many business applications of descriptive and inferential statistics.

In today’s business environment, people need the skills to understand numerical information from an increasingly wide variety of sources and then be able to reduce the large amounts of data into meaningful formats in order to interpret, judge and make decisions. Lind 5ce introduces students to these business applications skills, while maintaining a student-oriented learning environment with examples and problems designed to teach the essential knowledge of statistics, in order to prepare them for their careers in business.

Table contents:

Chapter

1 What Is Statistics? 1

Introduction 2

Why Study Statistics? 2

What Is Meant by Statistics? 4

Types of Statistics 6

Descriptive Statistics 6

Inferential Statistics 7

Types of Variables 9

Levels of Measurement 9

Nominal-Level Data 10

Ordinal-Level Data 11

Interval-Level Data 12

Ratio-Level Data 12

Exercises 14

Statistics, Graphics, and Ethics 15

Misleading Statistics 15

Association Does Not Necessarily Imply

Causation 15

Graphs Can Be Misleading 16

Become a Better Consumer and a Better

Producer of Information 17

Ethics 17

Software Applications 18

Chapter Outline 19

Chapter Exercises 19

exercises.com 20

Dataset Exercises 21

Answers to Self-Review 22

Chapter

2 Describing Data: Frequency

Distributions and Graphic

Presentation 23

Introduction 24

Constructing a Frequency Distribution 25

Class Intervals and Class Midpoints 29

A Software Example 29

Relative Frequency Distribution 30

Exercises 31

Graphic Presentation of a Frequency

Distribution 32

Histogram 32

Frequency Polygon 34

Exercises 37

Cumulative Frequency Distributions 38

Exercises 41

Other Graphic Presentations of Data 42

Line Graphs 42

Bar Charts 43

Pie Charts 44

Exercises 46

Chapter Outline 47

Chapter Exercises 48

exercises.com 53

Dataset Exercises 53

Software Commands 54

Answers to Self-Review 56

Chapter

3 Describing Data: Numerical

Measures 57

Introduction 58

The Population Mean 59

The Sample Mean 60

Properties of the Arithmetic Mean 61

Exercises 62

The Weighted Mean 63

Exercises 64

The Median 64

The Mode 65

Exercises 67

Software Solution 68

The Relative Positions of the Mean, Median,

and Mode 68

Exercises 70

The Geometric Mean 71

Exercises 72

Why Study Dispersion? 73

Measures of Dispersion 74

Range 74

Mean Deviation 75

Exercises 76

Variance and Standard Deviation 77

Exercises 79

Software Solution 80

Exercises 81

Interpretation and Uses of the Standard

Deviation 82

Chebyshev?s Theorem 82

The Empirical Rule 83

Exercises 84

Chapter Outline 84

Pronunciation Key 86

Chapter Exercises 86

exercises.com 89

Dataset Exercises 90

Software Commands 90

Answers to Self-Review 92

Chapter

4 Describing Data: Displaying and

Exploring Data 93

Introduction 94

Dot Plots 94

Exercises 96

Quartiles, Deciles, and Percentiles 97

Exercises 100

Box Plots 100

Exercises 102

Skewness 103

Exercises 107

Describing the Relationship between Two

Variables 107

Exercises 110

Chapter Outline 112

Pronunciation Key 112

Chapter Exercises 112

exercises.com 116

Dataset Exercises 116

Software Commands 117

Answers to Self-Review 119

Chapter

5 A Survey of Probability

Concepts 120

Introduction 121

What Is a Probability? 122

Approaches to Assigning Probabilities 124

Classical Probability 124

Empirical Probability 125

Subjective Probability 126

Exercises 127

Some Rules for Computing Probabilities 128

Rules of Addition 128

Exercises 133

Rules of Multiplication 134

Contingency Tables 137

Tree Diagrams 139

Exercises 141

Principles of Counting 142

The Multiplication Formula 142

The Permutation Formula 143

The Combination Formula 145

Exercises 146

Chapter Outline 147

Pronunciation Key 148

Chapter Exercises 148

exercises.com 152

Dataset Exercises 152

Software Commands 153

Answers to Self-Review 154

Chapter

6 Discrete Probability

Distributions 156

Introduction 157

What Is a Probability Distribution? 157

Random Variables 159

Discrete Random Variable 159

Continuous Random Variable 160

The Mean, Variance, and Standard Deviation of

a Probability Distribution 160

Mean 160

Variance and Standard Distribution 161

Exercises 163

Binomial Probability Distribution 164

How Is a Binomial Probability Distribution

Computed 165

Binomial Probability Tables 167

Exercises 170

Cumulative Binomial Probability

Distributions 172

Exercises 173

Poisson Probability Distribution 174

Exercises 177

Chapter Outline 177

Chapter Exercises 178

Dataset Exercises 182

Software Commands 182

Answers to Self-Review 184

Chapter

7 Continuous Probability

Distributions 185

Introduction 186

The Family of Uniform Distributions 186

Exercises 189

The Family of Normal Probability

Distributions 190

The Standard Normal Distribution 193

The Empirical Rule 195

Exercises 196

Finding Areas under the Normal

Curve 197

Exercises 199

Exercises 202

Exercises 204

Chapter Outline 204

Chapter Exercises 205

Dataset Exercises 208

Software Commands 209

Answers to Self-Review 210

Chapter

8 Sampling Methods and the

Central Limit Theorem 211

Introduction 212

Sampling Methods 212

Reasons to Sample 212

Simple Random Sampling 213

Systematic Random Sampling 216

Stratified Random Sampling 216

Cluster Sampling 217

Exercises 218

Sampling ?Error? 220

Sampling Distribution of the Sample

Mean 222

Exercises 225

The Central Limit Theorem 226

Exercises 232

Using the Sampling Distribution of the

Sample Mean 233

Exercises 237

Chapter Outline 237

Pronunciation Key 238

Chapter Exercises 238

exercises.com 242

Dataset Exercises 243

Software Commands 243

Answers to Self-Review 244

Chapter

9 Estimation and Confidence

Intervals 245

Introduction 246

Point Estimates and Confidence Intervals 246

Known _ or a Large Sample 246

A Computer Simulation 251

Exercises 253

Unknown Population Standard Deviation and

a Small Sample 254

Exercises 260

A Confidence Interval for a Proportion 260

Exercises 263

Finite-Population Correction Factor 263

Exercises 264

Choosing an Appropriate Sample Size 265

Exercises 267

Chapter Outline 268

Pronunciation Key 269

Chapter Exercises 269

exercises.com 272

Dataset Exercises 273

Software Commands 273

Answers to Self-Review 275

Chapter

10 One-Sample Tests

of Hypothesis 276

Introduction 277

What Is a Hypothesis? 277

What Is Hypothesis Testing? 278

Five-Step Procedure for Testing a

Hypothesis 278

Step 1: State the Null Hypothesis (H0) and

the Alternate Hypothesis (H1) 278

Step 2: Select a Level of Significance 279

Step 3: Select the Test Statistic 279

Step 4: Formulate the Decision Rule 281

Step 5: Make a Decision 282

One-Tailed and Two-Tailed Tests of

Significance 283

Testing for a Population Mean with a Known

Population Standard Deviation 284

A Two-Tailed Test 284

A One-Tailed Test 288

p-Value in Hypothesis Testing 288

Testing for a Population Mean: Large Sample,

Population Standard Deviation Unknown 290

Exercises 291

Tests Concerning Proportions 292

Exercises 295

Testing for a Population Mean: Small Sample,

Population Standard Deviation Unknown 295

Exercises 300

A Software Solution 301

Exercises 303

Chapter Outline 304

Pronunciation Key 305

Chapter Exercises 305

exercises.com 309

Dataset Exercises 309

Software Commands 310

Answers to Self-Review 311

Chapter

11 Two-Sample Tests

of Hypothesis 312

Introduction 313

Two-Sample Tests of Hypothesis: Independent

Samples 313

Exercises 318

Two-Sample Tests about Proportions 319

Exercises 321

Comparing Population Means with Small

Samples 323

Exercises 326

Two-Sample Tests of Hypothesis: Dependent

Samples 327

Comparing Dependent and Independent

Samples 331

Exercises 333

Chapter Outline 334

Pronunciation Key 335

Chapter Exercises 335

exercises.com 340

Dataset Exercises 341

Software Commands 341

Answers to Self-Review 342

Chapter

12 Analysis of Variance 344

Introduction 345

The F Distribution 345

Comparing Two Population Variances 346

Exercises 349

ANOVA Assumptions 350

The ANOVA Test 352

Exercises 359

Inferences about Pairs of Treatment

Means 360

Exercises 362

Chapter Outline 364

Pronunciation Key 365

Chapter Exercises 365

exercises.com 370

Dataset Exercises 370

Software Commands 371

Answers to Self-Review 373

Chapter

13 Linear Regression

and Correlation 374

Introduction 375

What Is Correlation Analysis? 375

lin83965_fm.qxd 11/9/04 12:24 PM Page xiv

Contents xv

The Coefficient of Correlation 377

The Coefficient of Determination 381

Correlation and Cause 382

Exercises 382

Testing the Significance of the Correlation

Coefficient 384

Exercises 386

Regression Analysis 386

Least Squares Principle 386

Drawing the Line of Regression 389

Exercises 390

The Standard Error of Estimate 392

Assumptions Underlying Linear

Regression 395

Exercises 396

Confidence and Prediction Intervals 396

Exercises 400

More on the Coefficient of Determination 400

Exercises 403

The Relationships among the Coefficient of

Correlation, the Coefficient of Determination,

and the Standard Error of Estimate 403

Transforming Data 405

Exercises 407

Chapter Outline 408

Pronunciation Key 410

Chapter Exercises 410

exercises.com 417

Dataset Exercises 417

Software Commands 418

Answers to Self-Review 420

Chapter

14 Multiple Regression and

Correlation Analysis 421

Introduction 422

Multiple Regression Analysis 422

Inferences in Multiple Linear Regression 423

Exercises 426

Multiple Standard Error of Estimate 428

Assumptions about Multiple Regression and

Correlation 429

The ANOVA Table 430

Exercises 432

Evaluating the Regression Equation 432

Using a Scatter Diagram 432

Correlation Matrix 433

Global Test: Testing the Multiple Regression

Model 434

Evaluating Individual Regression

Coefficients 436

Qualitative Independent Variables 439

Exercises 441

Analysis of Residuals 442

Chapter Outline 447

Pronunciation Key 448

Chapter Exercises 448

exercises.com 459

Dataset Exercises 460

Software Commands 461

Answers to Self-Review 463

Chapter

15 Chi-Square Applications 464

Introduction 464

Goodness-of-Fit Test: Equal Expected

Frequencies 465

Exercises 470

Goodness-of-Fit Test: Unequal Expected

Frequencies 471

Limitations of Chi-Square 473

Exercises 475

Contingency Table Analysis 746

Exercises 450

Chapter Outline 481

Pronunciation Key 481

Chapter Exercises 482

exercises.com 484

Dataset Exercises 485

Software Commands 486

Answers to Self-Review 487

CD Chapters

? Statistical Quality Control

? Time Series and Forecasting

Appendixes

Appendixes A?I Tables

Binomial Probability Distribution 489

Critical Values of Chi-Square 494

Poisson Distribution 495

Areas under the Normal Curve 496

Table of Random Numbers 497

Student?s t Distribution 498

Critical Values of the F Distribution 499

Wilcoxon T Values 501

Factors for Control Charts 502

Appendixes J?N Datasets

Real Estate 503

Major League Baseball 506

Wages and Wage Earners 508

CIA International Economic and

Demographic Data 512

Whitner Autoplex 515

Appendix O

Getting Started with Megastat 516

Appendix P

Visual Statistics 520

Answers to Odd-Numbered Exercises 525

Photo Credits 552

Index 553

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