Statistics are used throughout businesses to present and analyse data and decide on best practice. Simplified Business Statistics Using SPSS provides a practical approach to these concepts and their applications in business, economics and other areas of data analytics. This book guides the reader though these concepts without assuming prior knowledge and is an ideal reference for business analytics students and researchers in related fields.
Features
Includes simplified statistical contents and a step-by-step guide on how to apply statistical concepts by perform analysis using Statistical Package for Social Sciences together with an interpretation of the statistical analysis output
Provides a wide range of data sets to be used for examples and illustrations
Designed to be accessible to readers with varied backgrounds
Author(s): Gabriel Otieno Okello
Publisher: CRC Press/Chapman & Hall
Year: 2022
Language: English
Pages: 468
City: Boca Raton
Cover
Half Title
Title Page
Copyright Page
Contents
Part I: Introduction to Statistical Analysis
1. Introduction to Statistics, Data and Analysis
1.1. Basic Statistical Concepts
1.2. Application of Statistics
1.3. Data and Classification of Data
1.3.1. Scales of Measurement
1.3.2. Numeric and Non-numeric Data
1.3.3. Cross-Sectional and Time Series Data
1.3.4. Primary and Secondary Data
1.4. Data Sources
Practice Exercise
2. Collecting and Preparing Data for Analysis
2.1. Data Collection Methods
2.2. Preparing Data for Analysis
2.3. Data Analysis
Practice Exercise
3. Getting Started with SPSS
3.1. Starting SPSS
3.1.1. Menus and Icons in SPSS
3.2. Data Entry and Manipulation in SPSS
3.2.1. Data Entry
3.2.1.1. Define Variables in Variable View
3.2.2. Menu for Data Management and Analysis
3.4. Saving, Printing and Exiting SPSS
3.5. Where to Find Help/Resources
3.6. Extracting Inbuilt SPSS Data
Practice Exercise
4. Descriptive Statistics Methods
4.1. Descriptive Statistics
4.1.1. Tabular Presentation of Data Using SPSS
4.1.2. Frequency Table without Intervals
4.1.3. Frequency Table with Intervals
4.1.3.1. Steps for Constructing Frequency Distribution Table with Intervals
4.2. Graphical Presentation of Data
4.2.1. Bar Graph
4.2.2. Pie Chart
4.2.3. Histogram
4.2.4. Line Graph
4.2.5. Stem and Leaf Plot
4.2.6. Box Plot
4.2.7. Frequency Polygon
4.2.8. Scatter Plot
4.2.9. Cumulative Frequency Curve/Ogive
4.3. Numerical Measures
4.3.1. Measures of Central Tendencies
4.3.2. Measures of Dispersion/Spread/Variability
4.3.3. Measures of Shape
4.3.4. Measures of Association
4.4. Exploratory Data Analysis
Practice Exercise
Part II: Probability Concepts
5. Introduction to Probability
5.1. Basic Probability Concepts
5.2. Assigning Probabilities
5.3. Tree Diagrams
5.4. Some Basic Relationships of Probability
5.4.1. Union of Two Events
5.4.2. Intersection of Two Events
5.4.3. Complement of an Event
5.4.4. Null/Empty
5.5. Laws of Probability
5.5.1. Additive Law
5.5.1.1. Additive Law for Mutually Exclusive Events
5.5.1.2. Additive Law for Not-Mutually Exclusive Events
5.5.2. Conditional Probability
5.5.3. Independent Events
Practice Exercise
6. Random Variables and Probability Distributions
6.1. Random Variables
6.2. Types of Random Variables
6.3. Describing Random Variables
6.3.1. Describing Discrete Random Variable
6.3.2. Describing Continuous Random Variable
6.4. Common Distributions
6.4.1. Binomial Distribution
6.4.2. Poisson Distribution
6.4.3. Uniform Distribution
6.4.4. Normal Probability Distribution
6.4.4.1. Checking for Normality
6.4.5. Normal Approximation of Binomial Probabilities
6.4.6. Student’s t Distribution
6.4.7. F-Distribution
6.4.8. Chi-Square Distribution
Practice Exercise
7. Sampling and Sampling Distribution
7.1. Sampling Terminologies and Concepts
7.2. Sampling Methods
7.2.1. Sampling Error: Need for Sampling Distribution
7.3. Sampling Distribution
7.3.1. Sampling Distribution of the Sample Mean (x)
7.3.1.1. The Mean and Standard Deviation of the Sample Mean
7.3.1.2. Sampling Distribution of the Sample Mean for Normally Distributed Variables
7.3.1.3. Central Limit Theorem
7.3.2. Sampling Distribution of the Sample Proportion (p)
7.3.2.1. The Mean of Sample Proportion
7.3.2.2. Standard Deviation of Sample Proportion
Practice Exercise
Part III: Introduction to Statistical Inference Concepts and Methods
8. Statistical Inference: Estimation
8.1. Statistical Inference
8.2. Estimation
8.2.1. Relating P.E. and I.E.
8.2.2. Computing P.E.
8.2.3. Computing M.E.
8.2.4. Computing Za/2 values
8.2.5. Computing ta/2 Values
8.2.6. Computing Standard Errors (S.E.)
8.2.7. Computing Confidence Interval
8.2.8. Computing Confidence Interval Estimate for Two Populations (Two Groups)
8.3. Sample Size Determination
Practice Exercise
9. Statistical Inference: Hypothesis Testing
9.1. Testing of Hypotheses
9.1.1. The Nature of Hypothesis Testing
9.1.2. Hypothesis
9.2. Forms for Null and Alternative Hypothesis
9.3. Types of Errors in Hypothesis Testing
9.4. Decision Rules for Rejecting Null Hypothesis
9.4.1. Critical-Value Approach to Hypothesis Testing
9.4.2. p-Value Approach to Hypothesis Testing
9.4.3. Steps in Hypothesis Testing
9.4.3.1. Steps in the Critical-Value Approach to Hypothesis Testing
9.4.3.2. Steps in the p-Value Approach to Hypothesis Testing
Practice Exercise
10. Testing Hypothesis about One Population
10.1. Application of Hypothesis Testing
10.1.1. Testing of Hypothesis about One Population
10.2. Practice Exercise
11. Testing Hypothesis about Two Populations
11.1. Application of Hypothesis Testing
11.1.1. Testing of Hypothesis about Two Populations
Practice Exercise
12. Testing Hypothesis about More Than Two Populations
12.1. Introduction to Experimental Designs
12.2. The Completely Randomized Design (One-Way ANOVA)
12.3. Hypothesis Testing Procedure for One-Way ANOVA Using SPSS
12.4. Multiple Comparison Tests
Practice Exercise
13. Testing Relationships about Categorical Data
13.1. The Chi-Square Distribution
13.1.1. The Chi-Square Test
13.2. Chi-Square Goodness-of-Fit Test
13.2.1. Testing Hypothesis Steps for Chi-Square Goodness-of-Fit Test Using SPSS
13.3. Chi-Square Test for Independence
13.3.1. Hypothesis Testing Steps for Chi-Square Test for Independence
Practice Exercise
14. Testing Relationships about Numerical Data
14.1. Inferences in Correlation Analysis Using SPSS
14.2. Regression Analysis Using SPSS
14.2.1. Simple Linear Regression Analysis Using SPSS
14.2.2. Multiple Linear Regression Analysis Using SPSS
14.2.2.1. Estimating Regression Parameters
14.2.2.2. Multiple Coefficient of Determination
14.2.2.3. Hypothesis Tests for the Slope/Regression Coefficient
14.2.2.4. Hypothesis Tests for the Overall Significant Relationship
Practice Exercise
Part IV: Special Topics in Statistical Analysis
15. Nonparametric Tests
15.1. Nonparametric Test
15.2. One-Sample Sign Test Using SPSS
15.2.1. Hypothesis Tests for the One-Sample Wilcoxon Signed Rank Test
15.3. Wilcoxon Signed Rank Test for Matched/Paired Samples Using SPSS
15.3.1. Hypothesis Tests for the Paired or Matched Sample Signed Rank Test
15.3.1.1. For Sign Test
15.3.1.2. For Wilcoxon Singed Rank Test
15.4. Mann-Whitney U Test Using SPSS
15.4.1. Hypothesis Tests for Mann-Whitney U test
15.5. Kruskal-Wallis H Test Using SPSS
15.5.1. Hypothesis Tests for Kruskal-Wallis H Test
Practice Exercise
16. Analysis of Time Series Data
16.1. Introduction to Time Series Analysis and Forecasting
16.1.1. Time Series Components
16.1.2. Understanding Time Series
16.1.3. Stationary Time Series
16.1.4. Creating Lags of Time Series
16.1.5. The Measurement of Forecasting Error
16.1.5.1. Error
16.1.5.2. Mean Absolute Error (MAE)
16.1.5.3. Mean Square Error (MSE)
16.1.6. Reading Time Series Data
16.1.7. Plotting Time Series
16.2. Decomposing Time Series
16.2.1. Decomposing Nonseasonal Data
16.2.2. Decomposing Seasonal Data
16.3. Forecasts Using Exponential Smoothing
16.3.1. Simple Exponential Smoothing
16.3.2. Holt’s Linear Exponential Smoothing
16.3.3. Holt-Winters Exponential Smoothing
16.4. ARIMA Models
16.4.1. Formulation of ARIMA Models
16.4.2. Differencing a Time Series
16.4.3. Selecting a Candidate ARIMA Model
16.4.4. Forecasting Using an ARIMA Model
16.5. Autocorrelation and Partial Autocorrelation
16.5.1. Autocorrelation Function
16.5.2. Partial Autocorrelation Function
Practice Exercise
17. Statistical Quality Control
17.1. Introduction
17.2. Objectives of Statistical Process Control
17.3. SPC Control Charts Using SPSS
17.3.1. Continuous/Variable Control Charts
17.3.1.1. X-Bar and R Control Charts
17.3.1.2. Run Charts
17.3.1.3. X–MR Charts (I–MR, Individual Moving Range)
17.3.1.4. X-Bar – S Charts
17.3.1.5. EWMA Chart
17.3.2. Discrete/Attributes Control Charts
17.3.2.1. p Charts
17.3.2.2. np Charts
17.3.2.3. c Charts
17.3.2.4. u Chart
Practice Exercise
Index