Statistics for the Social Sciences , Using Excel a first course for South African students
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Oxford University Press, Southern Africa
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| 245 | 1 | 0 | |a Statistics for the Social Sciences , Using Excel |b a first course for South African students |c Glyn Davis, Branko Pecar, Leonard Santana, Alban Burke |
| 264 | 1 | |a Cape Town |b Oxford University Press, Southern Africa |c 2014 | |
| 300 | |a 704 Seiten |b Illustrationen, Diagramme | ||
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Datensatz im Suchindex
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| adam_text | Statistics for the
SOCIAL SCIENCES
using Excel®
( A FIRST COURSE FOR SOtJTHI AFRSAN STUDENTS!?) V • • ,_f til-,-ill •Jill TSt- ;
Glyn Davis I Branko Pecar I Leonard Santana I Alban Burke
OXFORD
UNIVERSITY PRESS
SOUTHERNAFRICA
CONTENTS
Preface xi
Authors xiii
How to use this book xv
CHAPTER 1 Introduction to Statistics 1
1 1 Introduction 2
1 2 What is statistics? 3
121 Which fields make use of statistics? 3
1 3 Basic concepts and terms used in statistics 4
131 Populations and samples 4
132 Statistical inference 6
133 Types of sample error 7
1 4 The research cycle 8
Summary 9
Key terms 10
CHAPTER 2 Frequency tables and graphical representations of data 11
2 1 Introduction 12
2 2 Different types of variables and data 12
221 Types of variables and their measurement scales 13
222 Types of data 16
2 3 Frequency tables 16
231 What is a frequency table and what does it look like? 16
232 Creating a frequency table for quantitative data (continuous and discrete) 21
233 Creating a frequency table using Excel 26
234 Creating a frequency distribution for discrete or categorical data 30
235 Principles of table construction 31
2 4 Cumulative frequency tables 32
241 Cumulative frequency tables for discrete data 33
242 Cumulative frequency tables for continuous data 35
2 5 Graphical representation of data 36
251 Bar charts 36
252 Excel Chart Wizard solution 38
253 Pie charts 41
254 Excel spreadsheet solution 43
255 Histograms 45
256 Excel spreadsheet solution 46
257 Frequency polygons 52
258 Excel spreadsheet solution - frequency polygon using data from Example 2 5 54
259 The ogive or cumulative frequency polygon 56
2 5 10 Excel spreadsheet solution 58
Summary 61
Key terms 62
Exercises 62
Techniques in practice 64
CHAPTER 3 Data descriptors 67
3 1 Measures of central tendency 69
311 The mean 69
312 The median 79
313 The mode 85
314 The percentiles and quartiles 88
3 2 Measures of dispersion 95
321 Range 96
322 Interquartile range and semi-interquartile range 99
323 Standard deviation and variance 102
324 The coefficient of variation 108
3 3 Measures of skewness 109
3 4 Measures of kurtosis 113
3 5 Exploratory data analysis 115
351 Five-number summary 115
352 Using the Excel ToolPak add-in 116
Summary 119
Key terms 119
Formula summary 120
Exercises 121
Techniques in practice 127
CHAPTER 4 Probability 131
4 1 Introduction to probability 132
411 Probability theory in practice 133
412 The anatomy of probability problems 134
4 2 Calculating probabilities 137
421 Determining probabilities when outcomes are equally probable 137
422 Determining probabilities when outcomes are not equally probable: relative frequencies 139
4 3 Basic probability rules 140
431 Venn diagrams and set notation: visualising probability 140
432 Probability rules 143
433 Independent events 146
4 4 Conditional probability 147
441 What is conditional probability? 147
442 Using tree diagrams and conditional probabilities to solve more complex probability
problems 149
443 Probability rules involving conditional probabilities 153
Summary 155
Key terms 156
Formula summary 156
Exercises 157
Techniques in practice 158
CHAPTER 5 Random variables 159
5 1 Introduction to random variables 160
511 Random variables 160
512 Random variables and outcomes of experiments 161
5 2 Probability distributions and random variables 162
521 Discrete random variables and probability distributions 163
522 Continuous random variables 166
5 3 Expected values and variance 170
531 Expected value 170
532 Variance 171
Summary 178
Key terms 178
Formula summary 179
Exercises 179
CHAPTER 6 Discrete probability distributions 181
6 1 Introduction 182
6 2 Binomial probability distribution 183
621 Introduction to the binomial probability distribution 183
622 Identifying binomial random variables 183
623 The binomial probability distribution and the binomial mass function 185
624 Using the binomial probability distribution with probability rules 192
6 3 Poisson probability distribution 199
631 Introduction to the Poisson probability distribution 199
632 The Poisson probability distribution and mass function 200
633 Properties of the Poisson distribution and identifying Poisson random variables 200
634 Calculating probabilities associated with Poisson random variables and applying
probability rules 204
6 4 Other discrete probability distributions 211
Summary 211
Key terms 211
Formula summary 211
Exercises : 212
CHAPTER 7 Continuous probability distributions 215
7 1 Introduction to continuous probability distributions 217
711 Introduction 217
712 Continuous probability distributions and density functions 217
713 Probability calculations using density functions 218
7 2 The normal distribution 221
721A description of the normal distribution 221
722 Standardisation of normal random variables 222
723 Calculating probabilities associated with the normal distribution 223
724 Calculating quantiles associated with the normal distribution 238
7 3 The Student s {-distribution 239
731 Calculating probabilities associated with the ^-distribution 240
732 Calculating quantiles associated with the ^-distribution 240
7 4 The chi-squared distribution 241
741 Calculating probabilities associated with the ^-distribution 242
742 Calculating quantiles associated with the ^ -distribution 242
7 5 The F-distribution 243
751 Calculating probabilities associated with the F-distribution 244
752 Calculating quantiles associated with the F-distribution 244
Summary 245
Key terms 245
Formula summary 245
Exercises 246
CHAPTER 8 Sampling and sampling distributions 249
8 1 Sampling 250
811 Probability sampling 251
812 Non-probability sampling 255
8 2 Sampling distributions 257
821 Introduction 257
822 Properties of the sampling distribution of the mean 258^
823 Sampling from a normal population 260
824 Sampling from a non-normal population 269
825 Sampling distribution of the proportion 272
8 3 Using Excel to generate a sample from a sampling distribution 274
Summary 279
Key terms 279
Formula summary 280
Exercises 280
Techniques in practice 282
CHAPTER 9 Estimation: Point estimators and confidence intervals 283
9 1 An Introduction to estimation 284
911 Introduction 284
912 Types of estimates 286
913 Criteria for a good estimator 286
9 2 Point estimators 287
921 Point estimate of the population mean,// 287
922 Point estimate of the population standard deviation, o 287
923 Point estimate of the standard error of A7, = ^ 288
924 Point estimates for the population proportion, n and the standard error of p, *{l-] 290 n
925 Pooled estimates 291
9 3 Confidence intervals 292
931 Introduction to confidence intervals for the population mean 292
932 Confidence interval estimate for the population mean, n (a is known and the data are
normally distributed) 293
933 Confidence interval estimate of the population mean, ^ (a is unknown, the data are
normally distributed and n 30) 296
934 Confidence interval estimate of the population mean, f i (a is unknown and n 30) 301
935 Confidence interval estimate of a population proportion 303
9 4 Calculating sample sizes • 305
Summary 309
Key terms 309
Formula summary 309
Exercises 311
Techniques in practice 312
CHAPTER 10 Parametric hypothesis testing: One population 315
10 1 An introduction to hypothesis testing 316
10 1 1 Hypothesis testing procedure 317
10 2 One sample Z-test for the population mean 328
10 3 One sample f-test for the population mean 332
10 4 One sample Z-test for the population proportion 337
Summary 340
Key terms 341
Formula summary 341
Exercises 341
Techniques in practice 343
CHAPTER 11 Parametric hypothesis testing: Two populations 345
11 1 Tests involving two populations 346
11 1 1 Introduction 346
11 1 2 Independent and dependent populations 347
11 1 3 Notation 347
11 2 Comparing population means from two independent populations
(population variances are known) 348
11 3 Comparing population means from two independent populations
(population variances are unknown, but equal) 354
11 4 Comparing population means for two independent populations
(population variances are unknown and unequal) 361
11 5 Comparing the population means of two dependent populations 366
11 6 Comparing population proportions from two independent populations 373
11 7 F-test for two population variances (variance ratio test) 378
Summary 383
Key terms 384
Formula summary 384
Exercises 385
Techniques in practice 389
CHAPTER 12 Linear correlation and regression analysis 391
12 1 Introduction 392
12 2 Scatter plots 393
12 2 1 Introduction to scatter plots 394
12 2 2 Construction of scatter plots 396
12 2 3 Using scatter plots to detect outliers 400
12 3 Linear correlation analysis 401
12 3 1 Pearson s correlation coefficient, r 401
12 3 2 Testing the significance of Pearson s correlation coefficient, r 408
12 4 Linear regression analysis 412
• 12 4 1 Introduction to linear regression analysis 412
12 4 2 The linear regression model 412
12 4 3 Method of least squares: Estimating/?0 and /?, 413
12 4 4 Fitted values and residuals 420
12 4 5 Sums of squares 423
12 4 6 Test model reliability: Coefficient of determination (ft2) 424
12 4 7 Mest for the parameter B1 427
12 4 8 Prediction interval for an estimate of Y 430
12 4 9 Regression assumptions 434
12 4 10 Procedure for regression analysis 437
12 4 11 Excel ToolPak Regression solution 437
Summary 440
Key terms 440
Formula summary 440
Exercises 442
Techniques in practice 445
CHAPTER 13 Time series data and analysis 447
13 1 Time series data and graphical representation 448
13 1 1 The definition of time series data 448
13 1 2 Representing time series graphically 450
13 1 3 Creating a time series plot in Excel 453
13 1 4 Pitfalls associated with representing time series data 455
13 2 The components of classical time series analysis 457
13 3 Extracting classical time series components and forecasting 465
13 3 1 Extracting the trend component (T) using least squares techniques 465
13 3 2 Extracting the trend and cyclic components (TC) using moving averages 476
13 3 3 Extracting the seasonal component (S) using moving averages, de-seasonalising the
time series and forecasting 485
13 4 Time series smoothing and forecasting 497
13 4 1 Simple moving averages and forecasting 497
13 4 2 Exponential smoothing and forecasting 505
13 5 Forecasting errors 511,
13 5 1 Error measurement 511
13 5 2 Types of errors 514
13 5 3 Interpreting errors 516
Summary 517
Key terms 518
Formula summary 518
Exercises 519
Techniques in practice 521
CHAPTER 14 Parametric hypothesis testing 525
14 1 Chi-squared tests 527
14 1 1 Contingency tables 529
14 1 1 Tests of independence 532
14 1 3 Test differences in proportions for two samples 539
14 2 Non-parametric (or distribution-free) tests 551
14 2 1 Calculating ranks of data 552
14 2 2 The sign test 556
14 2 3 Wilcoxon signed-rank test 564
14 2 4 Mann-Whitney LMest for two independent samples 575
Summary 582
Key terms 583
Formula summary} 583
Exercises 584
Techniques in practice 588
CHAPTER 15 Analysis of variance 591
15 1 Introduction 592
15 2 ANOVA test for the means of multiple populations 593
15 2 1 The hypothesis statement and assumptions of the ANOVA test 594
15 2 2 Calculating the ANOVA F-test statistic 594
15 2 3 The Bonferroni post-hoc tests of individual pairs of means 605
15 2 4 Checking assumptions 612
15 2 5 Steps in testing multiple means using the ANOVA 616
15 3 The non-parametric Kruskal-Wallis test 617
Summary 623
Key terms I : 623
Formula summary 624
Exercises : 626
Techniques in practice 629
APPENDIX A: Mathematical revision 631
APPENDIX B: Microsoft Excel 2010 647
APPENDIX C: Normal distribution tables 674
APPENDIX D: Wilcoxon signed-rank test table 676
Student exercises answers 677
Glossary 689
Index 697
|
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| institution | BVB |
| isbn | 9780199051830 |
| language | English |
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| physical | 704 Seiten Illustrationen, Diagramme |
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| spellingShingle | Davis, Glyn Pecar, Branko Santana, Leonard Burke, Alban Statistics for the Social Sciences , Using Excel a first course for South African students Statistik (DE-588)4056995-0 gnd Empirische Sozialforschung (DE-588)4014606-6 gnd EXCEL (DE-588)4138932-3 gnd |
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| title | Statistics for the Social Sciences , Using Excel a first course for South African students |
| title_auth | Statistics for the Social Sciences , Using Excel a first course for South African students |
| title_exact_search | Statistics for the Social Sciences , Using Excel a first course for South African students |
| title_full | Statistics for the Social Sciences , Using Excel a first course for South African students Glyn Davis, Branko Pecar, Leonard Santana, Alban Burke |
| title_fullStr | Statistics for the Social Sciences , Using Excel a first course for South African students Glyn Davis, Branko Pecar, Leonard Santana, Alban Burke |
| title_full_unstemmed | Statistics for the Social Sciences , Using Excel a first course for South African students Glyn Davis, Branko Pecar, Leonard Santana, Alban Burke |
| title_short | Statistics for the Social Sciences , Using Excel |
| title_sort | statistics for the social sciences using excel a first course for south african students |
| title_sub | a first course for South African students |
| topic | Statistik (DE-588)4056995-0 gnd Empirische Sozialforschung (DE-588)4014606-6 gnd EXCEL (DE-588)4138932-3 gnd |
| topic_facet | Statistik Empirische Sozialforschung EXCEL |
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