Description
Book SynopsisTable of ContentsIntroduction 1
About This Book 2
What’s New in This Edition 2
What’s New in Excel (Microsoft 365) 3
Foolish Assumptions 3
Icons Used in This Book 4
Where to Go from Here 5
Beyond This Book 5
Part 1: Getting Started With Statistical Analysis With Excel: A Marriage Made In Heaven 7
Chapter 1: Evaluating Data in the Real World 9
The Statistical (and Related) Notions You Just Have to Know 9
Samples and populations 10
Variables: Dependent and independent 11
Types of data 12
A little probability 13
Inferential Statistics: Testing Hypotheses 14
Null and alternative hypotheses 15
Two types of error 16
Some Excel Fundamentals 18
Autofilling cells 22
Referencing cells 25
Chapter 2: Understanding Excel’s Statistical Capabilities 29
Getting Started 30
Setting Up for Statistics 32
Worksheet functions 32
Quickly accessing statistical functions 36
Array functions 38
What’s in a name? An array of possibilities 41
Creating Your Own Array Formulas 50
Using data analysis tools 51
Additional data analysis tool packages 56
Accessing Commonly Used Functions 58
The New Analyze Data Tool 59
Data from Pictures! 60
Part 2: Describing Data 63
Chapter 3: Show-and-Tell: Graphing Data 65
Why Use Graphs? 65
Examining Some Fundamentals 67
Gauging Excel’s Graphics (Chartics?) Capabilities 68
Becoming a Columnist 69
Stacking the Columns 73
Slicing the Pie 74
A word from the wise 76
Drawing the Line 77
Adding a Spark 80
Passing the Bar 82
The Plot Thickens 84
Finding Another Use for the Scatter Chart 88
Chapter 4: Finding Your Center 91
Means: The Lore of Averages 91
Calculating the mean 92
AVERAGE and AVERAGEA 93
AVERAGEIF and AVERAGEIFS 95
TRIMMEAN 99
Other means to an end 100
Medians: Caught in the Middle 102
Finding the median 102
MEDIAN 103
Statistics à la Mode 104
Finding the mode 104
MODE.SNGL and MODE.MULT 104
Chapter 5: Deviating from the Average 107
Measuring Variation 108
Averaging squared deviations: Variance and how to calculate it 108
VAR.P and VARPA 111
Sample variance 113
VAR.S and VARA 114
Back to the Roots: Standard Deviation 114
Population standard deviation 115
STDEV.P and STDEVPA 115
Sample standard deviation 116
STDEV.S and STDEVA 116
The missing functions: STDEVIF and STDEVIFS 117
Related Functions 121
DEVSQ 121
Average deviation 122
AVEDEV 123
Chapter 6: Meeting Standards and Standings 125
Catching Some Z’s 126
Characteristics of z-scores 126
Bonds versus the Bambino 127
Exam scores 128
STANDARDIZE 128
Where Do You Stand? 131
RANK.EQ and RANK.AVG 131
LARGE and SMALL 133
PERCENTILE.INC and PERCENTILE.EXC 134
PERCENTRANK.INC and PERCENTRANK.EXC 137
Data analysis tool: Rank and Percentile 138
Chapter 7: Summarizing It All 141
Counting Out 141
COUNT, COUNTA, COUNTBLANK, COUNTIF, COUNTIFS 141
The Long and Short of It 144
MAX, MAXA, MIN, and MINA 144
Getting Esoteric 145
SKEW and SKEW.P 146
KURT 148
Tuning In the Frequency 150
FREQUENCY 150
Data analysis tool: Histogram 152
Can You Give Me a Description? 154
Data analysis tool: Descriptive Statistics 154
Be Quick About It! 156
Instant Statistics 159
Chapter 8: What’s Normal? 161
Hitting the Curve 161
Digging deeper 162
Parameters of a normal distribution 163
NORM.DIST 165
NORM.INV 167
A Distinguished Member of the Family 168
NORM.S.DIST 169
NORM.S.INV 170
PHI and GAUSS 170
Graphing a Standard Normal Distribution 171
Part 3: Drawing Conclusions From Data 173
Chapter 9: The Confidence Game: Estimation 175
Understanding Sampling Distributions 176
An EXTREMELY Important Idea: The Central Limit Theorem 177
(Approximately) simulating the Central Limit Theorem 178
The Limits of Confidence 183
Finding confidence limits for a mean 183
CONFIDENCE.NORM 186
Fit to a t 187
CONFIDENCE.T 188
Chapter 10: One-Sample Hypothesis Testing 189
Hypotheses, Tests, and Errors 190
Hypothesis Tests and Sampling Distributions 191
Catching Some Z’s Again 193
Z.TEST 196
t for One 197
T.DIST, T.DIST.RT, and T.DIST.2T 198
T.INV and T.INV.2T 200
Visualizing a t-Distribution 201
Testing a Variance 203
CHISQ.DIST and CHISQ.DIST.RT 205
CHISQ.INV and CHISQ.INV.RT 206
Visualizing a Chi-Square Distribution 208
Chapter 11: Two-Sample Hypothesis Testing 211
Hypotheses Built for Two 211
Sampling Distributions Revisited 212
Applying the Central Limit Theorem 213
Z’s once more 215
Data analysis tool: z-Test: Two Sample for Means 216
t for Two 219
Like peas in a pod: Equal variances 220
Like p’s and q’s: Unequal variances 221
T.TEST 222
Data analysis tool: t-Test: Two Sample 223
A Matched Set: Hypothesis Testing for Paired Samples 227
T.TEST for matched samples 228
Data analysis tool: t-Test: Paired Two Sample for Means 230
t-tests on the iPad with StatPlus 232
Testing Two Variances 235
Using F in conjunction with t 237
F.TEST 238
F.DIST and F.DIST.RT 240
F.INV and F.INV.RT 241
Data analysis tool: F-test: Two Sample for Variances 242
Visualizing the F-Distribution 244
Chapter 12: Testing More Than Two Samples 247
Testing More than Two 247
A thorny problem 248
A solution 249
Meaningful relationships 253
After the F-test 254
Data analysis tool: Anova: Single Factor 258
Comparing the means 260
Another Kind of Hypothesis, Another Kind of Test 262
Working with repeated measures ANOVA 262
Getting trendy 264
Data analysis tool: Anova: Two-Factor Without Replication 268
Analyzing trend 271
ANOVA on the iPad 272
ANOVA on the iPad: Another Way 274
Repeated Measures ANOVA on the iPad 277
Chapter 13: Slightly More Complicated Testing 281
Cracking the Combinations 281
Breaking down the variances 282
Data analysis tool: Anova: Two-Factor Without Replication 284
Cracking the Combinations Again 286
Rows and columns 286
Interactions 287
The analysis 288
Data analysis tool: Anova: Two-Factor With Replication 289
Two Kinds of Variables — at Once 292
Using Excel with a Mixed Design 293
Graphing the Results 298
After the ANOVA 300
Two-Factor ANOVA on the iPad 300
Chapter 14: Regression: Linear and Multiple 303
The Plot of Scatter 303
Graphing a line 305
Regression: What a Line! 307
Using regression for forecasting 309
Variation around the regression line 309
Testing hypotheses about regression 311
Worksheet Functions for Regression 317
SLOPE, INTERCEPT, STEYX 318
FORECAST.LINEAR 319
Array function: TREND 319
Array function: LINEST 323
Data Analysis Tool: Regression 325
Working with tabled output 327
Opting for graphical output 329
Juggling Many Relationships at Once: Multiple Regression 330
Excel Tools for Multiple Regression 331
TREND revisited 331
LINEST revisited 333
Regression data analysis tool revisited 336
Regression Analysis on the iPad 338
Chapter 15: Correlation: The Rise and Fall of Relationships 341
Scatterplots Again 341
Understanding Correlation 342
Correlation and Regression 345
Testing Hypotheses about Correlation 347
Is a correlation coefficient greater than zero? 348
Do two correlation coefficients differ? 349
Worksheet Functions for Correlation 350
CORREL and PEARSON 350
RSQ 351
COVARIANCE.P and COVARIANCE.S 352
Data Analysis Tool: Correlation 353
Tabled output 354
Multiple correlation 355
Partial correlation 356
Semipartial correlation 357
Data Analysis Tool: Covariance 358
Using Excel to Test Hypotheses about Correlation 358
Worksheet functions: FISHER, FISHERINV 359
Correlation Analysis on the iPad 360
Chapter 16: It’s About Time 363
A Series and Its Components 363
A Moving Experience 364
Lining up the trend 365
Data analysis tool: Moving Average 365
How to Be a Smoothie, Exponentially 368
One-Click Forecasting 369
Working with Time Series on the iPad 374
Chapter 17: Nonparametric Statistics 379
Independent Samples 380
Two samples: Mann-Whitney U test 380
More than two samples: Kruskal-Wallis one-way ANOVA 382
Matched Samples 383
Two samples: Wilcoxon matched-pairs signed ranks 384
More than two samples: Friedman two-way ANOVA 386
More than two samples: Cochran’s Q 387
Correlation: Spearman’s rS 389
A Heads-Up 391
Part 4: Probability 393
Chapter 18: Introducing Probability 395
What Is Probability? 395
Experiments, trials, events, and sample spaces 396
Sample spaces and probability 396
Compound Events 397
Union and intersection 397
Intersection, again 398
Conditional Probability 399
Working with the probabilities 400
The foundation of hypothesis testing 400
Large Sample Spaces 400
Permutations 401
Combinations 402
Worksheet Functions 403
FACT 403
PERMUT and PERMUTIONA 403
COMBIN and COMBINA 404
Random Variables: Discrete and Continuous 405
Probability Distributions and Density Functions 405
The Binomial Distribution 407
Worksheet Functions 409
BINOM.DIST and BINOM.DIST.RANGE 409
NEGBINOM.DIST 411
Hypothesis Testing with the Binomial Distribution 412
BINOM.INV 413
More on hypothesis testing 414
The Hypergeometric Distribution 415
HYPGEOM.DIST 416
Chapter 19: More on Probability 419
Discovering Beta 419
BETA.DIST 421
BETA.INV 423
Poisson 424
POISSON.DIST 425
Working with Gamma 427
The gamma function and GAMMA 427
The gamma distribution and GAMMA.DIST 428
GAMMA.INV 430
Exponential 431
EXPON.DIST 431
Chapter 20: Using Probability: Modeling and Simulation 433
Modeling a Distribution 434
Plunging into the Poisson distribution 434
Visualizing the Poisson distribution 435
Working with the Poisson distribution 436
Using POISSON.DIST again 437
Testing the model’s fit 437
A word about CHISQ.TEST 440
Playing ball with a model 441
A Simulating Discussion 444
Taking a chance: The Monte Carlo method 444
Loading the dice 444
Data analysis tool: Random Number Generation 445
Simulating the Central limit Theorem 448
Simulating a business 452
Chapter 21: Estimating Probability: Logistic Regression 457
Working Your Way Through Logistic Regression 458
Mining with XLMiner 460
Part 5: The Part of Tens 465
Chapter 22: Ten (12, Actually) Statistical and Graphical Tips and Traps 467
Significant Doesn’t Always Mean Important 467
Trying to Not Reject a Null Hypothesis Has a Number of Implications 468
Regression Isn’t Always Linear 468
Extrapolating Beyond a Sample Scatterplot Is a Bad Idea 469
Examine the Variability Around a Regression Line 469
A Sample Can Be Too Large 470
Consumers: Know Your Axes 470
Graphing a Categorical Variable as a Quantitative Variable Is Just Plain Wrong 471
Whenever Appropriate, Include Variability in Your Graph 472
Be Careful When Relating Statistics Textbook Concepts to Excel 472
It’s Always a Good Idea to Use Named Ranges in Excel 472
Statistical Analysis with Excel on the iPad Is Pretty Good! 473
Chapter 23: Ten Topics (Thirteen, Actually) That Just Don’t Fit Elsewhere 475
Graphing the Standard Error of the Mean 475
Probabilities and Distributions 479
PROB 479
WEIBULL.DIST 479
Drawing Samples 480
Testing Independence: The True Use of CHISQ.TEST 481
Logarithmica Esoterica 484
What is a logarithm? 484
What is e? 486
LOGNORM.DIST 489
LOGNORM.INV 490
Array Function: LOGEST 491
Array Function: GROWTH 494
The logs of Gamma 497
Sorting Data 498
Part 6: Appendices 501
Appendix A: When Your Data Live Elsewhere 503
Appendix B: Tips for Teachers (and Learners) 507
Augmenting Analyses Is a Good Thing 507
Understanding ANOVA 508
Revisiting regression 510
Simulating Data Is Also a Good Thing 512
When All You Have Is a Graph 514
Appendix C: More on Excel Graphics 515
Tasting the Bubbly 515
Taking Stock 516
Scratching the Surface 518
On the Radar 519
Growing a Treemap and Bursting Some Sun 520
Building a Histogram 521
Ordering Columns: Pareto 522
Of Boxes and Whiskers 523
3D Maps 524
Filled Maps 527
Appendix D: The Analysis of Covariance 529
Covariance: A Closer Look 529
Why You Analyze Covariance 530
How You Analyze Covariance 531
ANCOVA in Excel 532
Method 1: ANOVA 533
Method 2: Regression 537
After the ANCOVA 540
And One More Thing 542
Index 545
