Not Just Another Stats Books
It is remarkable that statistics is both one of most useful and powerful tools in data analysis and also one of the most feared and hated subjects of study. The root of this rear is that statistics books are usually filled with equation, mathematical jargon, and pages of derivations and calculations. What sets this book apart from so many others is its focus on understanding the basic logic of statistics rather than the mathematical intricacies. The emphasis is on the meaning of statistics, when to apply tem, how to apply them, and how to interpret results. It reflects the highly successful approach that I have refined in my ten years of teaching statistics. There are three elements to this approach:
First of all, I use logical reasoning rather than mathematical deduction to explain statistical concepts and tests. While statistics is a branch of mathematics, the underlying logic of many statistical tests is very straightforward and requires no background in advanced mathematics to understand. The pages of equations and mathematical explanations commonly found in statistics books often obscure the simplicity of this underlying logic. Therefore, you will not see very many equations in this book. The few you do see will be carefully explained and can be ignored without harming your understanding of the basic principles being discussed.
Secondly, I use an example from information science research to demonstrate the complete process of each statistical test covered. I begin with how to formulate hypotheses, then cover the use of computer software to analyze the data, interpretation of the output, and reaching conclusions. Readers will see the complete picture of how statistical methods are applied rather than getting drowned in technical details. This emphasis on real applications of the statistical methods will enable the reader to see how statistics are a powerful and relevant tool rather than an arcane branch of mathematics irrelevant to them.
Finally, I emphasize the use of computer software to do calculations and other mathematical drudge work. Many statistics books spend a great deal of time on deriving formulae and carrying out calculations. The truth is that almost no one does these calculations manually today. There are a variety of computer software packages that can be used to take over the hard mathematical work so that we can focus on understanding the meaning of the results. In recognition of this, I have dedicated a chapter to issues that are specific to the sue of computer software for doing statistical analysis.
I have used this approach to teach statistics to students in various disciplines at levels from undergraduate to doctoral. Most students in my classes had very little mathematical background and many entered the course with feelings of fear and nervousness. However they were pleasantly surprised at how painless statistics can be and how exciting it is to understand a subject that was once mysterious and foreign to them. Indeed, it is a myth that statistics is complex and beyond people who are not mathematically oriented. I am confident that you will agree with me once you read this book.
About the Auther
Statistical Methods for the Information Professional
Rs. 850
Additional information
| ISBN | 9788170005520 |
|---|---|
| Year of Publication | 2009 |
| Binding | HardBound |
| Pages | |
| Edition | |
| Language | English |
List of Figures...................
List of Table....................
Preface
Not Just Another Stats Book..................
Who should read this Book.....................
How to Use this Book
Acknowledgments
CHAPTER 1
Getting Started----Recognizing the Types of Data
1.1 Nominal Data.............
1.2 Ordinal Data..............
1.3 Interval Data
1.4 Ratio Data
1.5 Data Conversion
CHAPTER 2
Avoiding Manual Calculations and Formula Manipulations----Using Software
2.1 Types of Software
2.2 which Software to Select
2.3 How to Organize Data into a Computer file
2.4 How to Deal with Missing Data
CHAPTER 3
First Look--- using Graphs to See the characteristics of Data
3.1 Variety of Graphs
3.2 A Special Bar Graph---Histogram
CHAPTER 4
Summarizing Messy Data into Neat Figures-----Descriptive Statistics
4.1 Measures of central Tendency
4.1.1 Mean---The Arithmetic Average
4.1.2 Median---The Middle Point
4.1.3 Mode----The Peak of The Histogram
4.1.4 When to Use Which Measure of Central Tendency---A Summary
4.2 Measures of Variability
4.2.1 Range
4.2.2 Inter quartile Range (IQR)
4.2.3 Standard Deviation (SD)
4.2.4 Variance
4.2.5 When to Use Which Measure of Variability-----A Summary
4.3 Tying Together Descriptive Statistics Measures---Examples
CHAPTER 5
What is Statistically Significant?-------Basic Concepts of Inferential statistics
5.1 Descriptive Statistics vs. Inferential Statistics
5.2 Population vs. Sample
5.3 Parameter vs. Statistic
5.4 Probability and Frequency Distribution
5.5 Normal Distribution
5.6 The Z Score
5.7 Standard Normal Distribution
5.8 Confidence Interval
5.9 Hypothesis Testing---Statistically Significant or Not
5.10 Errors of Statistical Testing--Type In and Type II Errors
CHAPTER 6
How to Collect Data-----Sampling Methods
6.1 Simple Random Sample
6.2 Systematic Sample
6.3 Stratified Sample
6.4 Sampling Bias
CHAPTER 7
Examining Relationships for nominal and Ordinal Data------Chi-Square Test
7.1 The Logic of the Chi-Square Test
7.2 Calculation of Expected Frequencies
7.3 Chi-Square Score
7.4 Chi-Square Table
7.5 Examining the Pattern of the Relationship
7.6 An Example of Using Software to Carry Out a Chi-Square Test
7.7 Requirements of Using Chi-Square test
CHAPTER 8
Examining Relationships for Interval and Ratio Data--Correlation and Regression
8.1 Types of Correlation
8.2 Using A scatter Plot to View the pattern of Relationship
8.3 measuring the Strength of a Relationship--Pearson r
8.4 Testing the Significance of Pearson r
8.5 Correlation and Causation
8.6 Regression Equation and Regression Line
8.7 Prediction
8.8 Requirements for Doing Correlation and Regression
CHAPTER 9
Are Two Samples Significantly Different?--- T Test
9.1 Independent T Test vs. Paired T Test
9.2 The Logic of the T Test
9.3 The procedure of the T Test
9.4 Examples of T Tests Using Software
9.5 Requirements for Using a T Test
CHAPTER 10
Are Three of More samples Significantly Different?------Analysis of Variance
10.1 the Logic of ANOVA
10.2 The Procedure for ANOVA
10.3 Example of ANOVA Using Software
10.4 Examining the Pattern of Difference
10.5 Requirements for Using ANOVA
CHAPTER 11
When Data Do Not Behave-------Using Nonparametric Test
11.1 Spearman Correlation Coefficient
11.2 the Mann-Whitney Test
11.3 The Wilcoxon Signed Ranks Test
11.4 Kruskal--Wallis Test
11.5 Advantages and Disadvantages of Nonparametric Tests
11.5.1 Advantages of Nonparametric Tests
11.5.2 Disadvantages of Nonparametric Tests
11.5.3 When to Use a Nonparametric Tests
CHAPTER 12
When Should I Use Which Test?------ A Road Map
CHAPTER 13
Getting Sophisticated----A Taste of Some Advanced Statistical Methods
13.1 Two- Way ANOVA
13.2 Multiple Regression
13.2.1 Why Do We Need multiple Regression?
13.2.2 Multiple Regression Equation
13.2.3 Regression Coefficients
13.2.4 Multiple Correlation Coefficient and Multiple Coefficient of Determination
13.2.5 Partial Correlation Coefficient
13.3 LISREL
Appendices
Appendix 1 Standard Normal Distribution
Appendix 2 Random Number Table
Appendix 3 Critical Values of Chi-Square
Appendix 4 Critical Values of Pearson r
Appendix 5 Critical Values of t
Appendix 6 Critical Values of F for ANOVA
Appendix 7 Critical Values for Tukey's HSD
Bibliography
About the Author
Index
