Basic Business Statistics Australian 4th Edition Berenson Solutions Manual

1. Part 1: Presenting and Describing Information
2. Chapter 1: Introduction and data collection
3. 1.1: Basic concepts of statistics
4. 1.2: The growth of statistics and information technology
5. 1.3: Collecting data
6. Identifying Sources of Data
7. 1.4: Types of variables
8. Levels of Measurement and Types of Measurement Scales
9. Summary
10. Key terms
11. Chapter review problems
12. Appendix 1: Introduction to using Microsoft Windows and Excel
13. Chapter 2: Presenting data in tables and charts
14. 2.1: Tables and charts for categorical data
15. Summary Tables
16. Bar Charts
17. Pie Charts
18. 2.2: Organising numerical data
19. Ordered Arrays
20. Stem-and-Leaf Displays
21. 2.3: Tables and graphs for numerical data
22. Frequency Distributions
23. Relative Frequency Distributions and Percentage Distributions
24. Cumulative Distributions
25. Histograms
26. Polygons
27. Cumulative Percentage Polygons (Ogives)
28. 2.4: Cross-tabulations
29. Contingency Tables
30. Side-by-Side Bar Charts
31. 2.5: Scatter diagrams and time-series plots
32. Scatter Diagrams
33. Time-series Plots
34. 2.6: Misusing graphs and ethical issues
35. Ethical Concerns
36. Summary
37. Key terms
38. Chapter review problems
39. Appendix 2: Using statistical software for tables and charts
40. Chapter 3: Numerical descriptive measures
41. 3.1: Measures of central tendency, variation and shape
42. Mean
43. Median
44. Mode
45. Quartiles
46. Geometric Mean
47. Range
48. Interquartile Range
49. Variance and Standard Deviation
50. Coefficient of Variation
51. Z Scores
52. Shape
53. Microsoft Excel Descriptive Statistics Output
54. 3.2: Numerical descriptive measures for a population
55. Population Mean
56. Population Variance and Standard Deviation
57. The Empirical Rule
58. The Chebyshev Rule
59. 3.3: Calculating numerical descriptive measures from a frequency distribution
60. 3.4: Five-number summary and box-and-whisker plots
61. Five-Number Summary
62. Box-and-Whisker Plots
63. 3.5: Covariance and the coefficient of correlation
64. Covariance
65. Coefficient of Correlation
66. 3.6: Pitfalls in numerical descriptive measures and ethical issues
67. Ethical Issues
68. Summary
69. Key formulas
70. Key terms
71. Chapter review problems
72. Appendix 3: Using statistical software for descriptive statistics
73. End of Part 1 problems
74. Part 2: Measuring Uncertainty
75. Chapter 4: Basic probability
76. 4.1: Basic probability concepts
77. Events and Sample Spaces
78. Contingency Tables and Venn Diagrams
79. Marginal Probability
80. Joint Probability
81. General Addition Rule
82. 4.2: Conditional probability
83. Calculating Conditional Probabilities
84. Decision Trees
85. Statistical Independence
86. Multiplication Rules
87. Marginal Probability Using the General Multiplication Rule
88. 4.3: Bayes’ theorem
89. 4.4: Counting rules
90. 4.5: Ethical issues and probability
91. Summary
92. Key formulas
93. Key terms
94. Chapter review problems
95. Appendix 4: Using statistical software for basic probability
96. Chapter 5: Some important discrete probability distributions
97. 5.1: Probability distribution for a discrete random variable
98. Expected Value of a Discrete Random Variable
99. Variance and Standard Deviation of a Discrete Random Variable
100. 5.2: Covariance and its application in finance
101. Covariance
102. Expected Value, Variance and Standard Deviation of the Sum of Two Random Variables
103. Portfolio Expected Return and Portfolio Risk
104. 5.3: Binomial distribution
105. 5.4: Poisson distribution
106. 5.5: Hypergeometric distribution
107. 5.6: (Online Topic) Using the poisson distribution to approximate the binomial distribution
108. Summary
109. Key formulas
110. Key terms
111. Chapter review problems
112. Appendix 5: Using statistical software for discrete probability distributions
113. Chapter 6: The normal distribution and other continuous distributions
114. 6.1: Continuous probability distributions
115. 6.2: The normal distribution
116. 6.3: Evaluating normality
117. Evaluating the Properties
118. Constructing a Normal Probability Plot
119. 6.4: The uniform distribution
120. 6.5: The exponential distribution
121. 6.6: The normal approximation to the binomial distribution
122. Need for a Continuity Correction
123. Approximating the Binomial Distribution
124. Calculating a Probability Approximation for an Individual Value
125. Summary
126. Key formulas
127. Key terms
128. Chapter review problems
129. Appendix 6: Using statistical software for continuous probability distributions
130. Chapter 7: Sampling distributions
131. 7.1: Sampling distributions
132. 7.2: Sampling distribution of the mean
133. The Unbiased Property of the Sample Mean
134. Standard Error of the Mean
135. Sampling from Normally Distributed Populations
136. Sampling from Non-normally Distributed Populations – The Central Limit Theorem
137. 7.3: Sampling distribution of the proportion
138. 7.4: Types of survey sampling methods
139. Simple Random Sample
140. Systematic Sample
141. Stratified Sample
142. Cluster Sample
143. 7.5: Evaluating survey worthiness
144. Survey Errors
145. Ethical Issues
146. Summary
147. Key formulas
148. Key terms
149. Chapter review problems
150. Appendix 7: Using statistical software for sampling distributions
151. End of Part 2 problems
152. Part 3: Drawing Conclusions about Populations Based only on Sample Information
153. Chapter 8: Confidence interval estimation
154. 8.1: Confidence interval estimation for the mean (σ known)
155. 8.2: Confidence interval estimation for the mean (σ unknown)
156. Student’s t Distribution
157. Properties of the t Distribution
158. The Concept of Degrees of Freedom
159. The Confidence Interval Statement
160. 8.3: Confidence interval estimation for the proportion
161. 8.4: Determining sample size
162. Sample Size Determination for the Mean
163. Sample Size Determination for the Proportion
164. 8.5: Applications of confidence interval estimation in auditing
165. Estimating the Population Total Amount
166. Difference Estimation
167. One-Sided Confidence Interval Estimation of the Rate of Non-Compliance with Internal Controls
168. 8.6: More on confidence interval estimation and ethical issues
169. Summary
170. Key formulas
171. Key terms
172. Chapter review problems
173. Appendix 8: Using statistical software for confidence intervals and sample size determination
174. Chapter 9: Fundamentals of hypothesis testing: One-sample tests
175. 9.1: Hypothesis-testing methodology
176. The Null and Alternative Hypotheses
177. The Critical Value of the Test Statistic
178. Regions of Rejection and Non-Rejection
179. Risks in Decision Making Using Hypothesis Testing
180. 9.2: Z test of hypothesis for the mean (σ known)
181. The Critical Value Approach to Hypothesis Testing
182. The p-Value Approach to Hypothesis Testing
183. A Connection between Confidence Interval Estimation and Hypothesis Testing
184. 9.3: One-tail tests
185. The Critical Value Approach
186. The p-Value Approach
187. 9.4: t test of hypothesis for the mean (σ unknown)
188. The Critical Value Approach
189. The p-Value Approach
190. Checking Assumptions
191. 9.5: Z test of hypothesis for the proportion
192. The Critical Value Approach
193. The p-Value Approach
194. 9.6: The power of a test
195. 9.7: Potential hypothesis-testing pitfalls and ethical issues
196. Data-Collection Method – Randomisation
197. Informed Consent from the Human Respondents
198. Type of Test: Two-Tail or One-Tail
199. Choice of Level of Significance, 𝛂
200. Data Snooping
201. Cleansing and Discarding of Data
202. Reporting of Findings
203. Statistical Significance Versus Practical Significance
204. Summary
205. Key formulas
206. Key terms
207. Chapter review problems
208. Appendix 9: Using statistical software for one-sample tests of hypothesis
209. Chapter 10: Hypothesis testing: Two-sample tests

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