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Basic Business Statistics Concepts and Applications 5th Edition Berenson Solutions Manual

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Basic Business Statistics Concepts and Applications 5th Edition Berenson Solutions Manual.

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Product Details:

  • ISBN-10 ‏ : ‎ 1488617244
  • ISBN-13 ‏ : ‎ 978-1488617249
  • Author:   Mark L. Berenson

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Table of Content:

  1. Part 1 Presenting and describing information
  2. Chapter 1 Defining and collecting data
  3. Learning objectives
  4. 1.1 Basic concepts of data and statistics
  5. The meaning of ‘data’
  6. The meaning of ‘statistics’
  7. Other important definitions
  8. 1.2 Types of variables
  9. Levels of measurement and types of measurement scales
  10. Learning objective 1
  11. Nominal and ordinal scales
  12. Interval and ratio scales
  13. 1.3 Collecting data
  14. Learning objective 2
  15. Identifying sources of data
  16. Learning objective 3
  17. ‘Big data’
  18. Data formatting
  19. Data cleaning
  20. Recoding variables
  21. 1.4 Types of survey sampling methods
  22. Learning objective 4
  23. Simple random sample
  24. Systematic sample
  25. Stratified sample
  26. Cluster sample
  27. 1.5 Evaluating survey worthiness
  28. Learning objective 5
  29. Survey errors
  30. Coverage error
  31. Non-response error
  32. Sampling error
  33. Measurement error
  34. Ethical issues
  35. 1.6 The growth of statistics and information technology
  36. 1 Assess your progress
  37. Summary
  38. Chapter review problems
  39. Checking your understanding
  40. Applying the concepts
  41. References
  42. Chapter 1 Excel guide
  43. EG1.1 Getting started with microsoft excel
  44. EG1.2 Opening and saving workbooks
  45. EG1.3 Entering data
  46. EG1.4 Using formulas in excel worksheets
  47. EG1.5 Creating charts
  48. EG1.6 Printing workbooks
  49. EG1.7 How using excel for mac differs
  50. EG1.8 Defining data
  51. Establishing the variable type
  52. EG1.9 COLLECTING DATA
  53. Recoding variables
  54. Key technique
  55. Example
  56. Excel how-to
  57. EG1.10 Types of sampling methods
  58. Simple random sample
  59. Key technique
  60. Example
  61. Excel how-to
  62. Analysis Toolpak
  63. Example
  64. PHStat
  65. Excel how-to
  66. Chapter 2 Organising and visualising data
  67. Learning objectives
  68. 2.1 Organising and visualising categorical data
  69. Learning objective 1
  70. Organising categorical data: Summary table
  71. Visualising categorical data: Bar charts
  72. Pie charts
  73. 2.2 Organising numerical data
  74. Learning objective 2
  75. Ordered arrays
  76. Stem-and-leaf displays
  77. 2.3 Summarising and visualising numerical data
  78. Learning objective 2
  79. Summarising numerical data: Frequency distributions
  80. Relative frequency and percentage distributions
  81. Cumulative distributions
  82. Histograms
  83. Polygons
  84. Cumulative percentage polygons (ogives)
  85. 2.4 Organising and visualising two categorical variables
  86. Learning objective 3
  87. Organising two categorical variables: Contingency tables
  88. Visualising two categorical variables: Side-by-side bar charts
  89. 2.5 Visualising two numerical variables
  90. Learning objective 4
  91. Scatter diagrams
  92. Time-series plots
  93. 2.6 Business analytics applications – descriptive analytics
  94. Learning objective 5
  95. Dashboards
  96. Data discovery
  97. 2.7 Misusing graphs and ethical issues
  98. Learning objective 6
  99. Ethical concerns
  100. 2 Assess your progress
  101. Summary
  102. Chapter review problems
  103. Checking your understanding
  104. Applying the concepts
  105. References
  106. Chapter 2 Excel guide
  107. EG2.1 Organising and visualising categorical data
  108. Organising categorical data
  109. The summary table
  110. Key technique
  111. Example
  112. PHStat
  113. In-depth excel (untallied data)
  114. In-depth excel (tallied data)
  115. Visualising categorical variables
  116. The bar chart and the pie chart
  117. Key technique
  118. Example
  119. PHStat
  120. In-depth excel
  121. EG2.2 Organising numerical data
  122. EG2.3 Summarising and visualising numerical data
  123. Summarising numerical data
  124. The frequency distribution
  125. Key technique
  126. Example
  127. Defining classes using bins
  128. PHStat (untallied data)
  129. In-depth excel (untallied data)
  130. Analysis toolpak (untallied data)
  131. The relative frequency, percentage and cumulative distributions
  132. Key technique
  133. Example
  134. PHStat (untallied data)
  135. In-depth excel (untallied data)
  136. Analysis toolpak
  137. Visualising numerical data
  138. The histogram
  139. Key technique
  140. Example
  141. PHStat
  142. PHStat defining classes bins and mid-points
  143. In-depth excel
  144. Analysis toolpak
  145. The percentage polygon and the cumulative percentage polygon (ogive)
  146. Key technique
  147. Example
  148. PHStat
  149. In-depth excel
  150. EG2.4 Organising and visualising two categorical variables
  151. Organising two categorical variables
  152. The contingency table
  153. Key technique
  154. Example
  155. PHStat (untallied data)
  156. In-depth excel (untallied data)
  157. In-depth excel (tallied data)
  158. Visualising two categorical variables
  159. The side-by-side chart
  160. Key technique
  161. Example
  162. PHStat
  163. In-depth excel
  164. EG2.5 Visualising two numerical variables
  165. EG2.6 Descriptive analytics
  166. Chapter 3 Numerical descriptive measures
  167. Learning objectives
  168. 3.1 Measures of central tendency, variation and shape
  169. Learning objective 1
  170. Measures of central tendency
  171. Mean
  172. Median
  173. Mode
  174. Quartiles
  175. Geometric mean
  176. Measures of variation
  177. Range
  178. Interquartile range
  179. Variance and standard deviation
  180. Coefficient of variation
  181. Z scores
  182. Shape
  183. Microsoft excel descriptive statistics output
  184. 3.2 Numerical descriptive measures for a population
  185. Learning objective 2
  186. Population mean
  187. Population variance and standard deviation
  188. The empirical rule
  189. The chebyshev rule
  190. 3.3 Calculating numerical descriptive measures from a frequency distribution
  191. Learning objective 1
  193. 3.4 Five-number summary and box-and-whisker plots
  194. Learning objective 3
  195. Five-number summary
  196. Box-and-whisker plots
  197. 3.5 Covariance and the coefficient of correlation
  198. Learning objective 4
  199. Covariance
  200. Coefficient of Correlation
  201. 3.6 Pitfalls in numerical descriptive measures and ethical issues
  202. Ethical issues
  203. 3 Assess your progress
  204. Summary
  205. Key formulas
  206. Chapter review problems
  207. Checking your understanding
  208. Applying the concepts
  209. Report writing exercise
  210. Chapter 3 Excel guide
  211. EG3.1 Measures of central tendency, variation and shape
  212. Central tendency
  213. The mean, median and mode
  214. Key technique
  215. Example
  216. PHStat
  217. In-depth excel
  218. Analysis Toolpak
  219. Quartiles
  220. Key technique
  221. Example
  222. PHStat
  223. In-depth excel
  224. The geometric mean
  225. Key technique
  226. Example
  227. In-depth excel
  228. Variation and shape
  229. The range
  230. Key technique
  231. Example
  232. PHStat
  233. In-depth excel
  234. Analysis Toolpak
  235. The interquartile range
  236. Key technique
  237. Example
  238. In-depth excel
  239. The variance, standard deviation, coefficient of variation and Z scores
  240. Key technique
  241. Example
  242. PHStat
  243. In-depth excel
  244. Analysis toolpak
  245. Shape: skewness and kurtosis
  246. Key technique
  247. Example
  248. PHStat
  249. In-depth excel
  250. Analysis toolpak
  251. EG3.2 Numerical descriptive measures for a population
  252. The population mean, population variance and population standard deviation
  253. Key technique
  254. Example
  255. In-depth excel
  256. The empirical rule and the chebyshev rule
  257. EG3.3 Five-number summary and box-and-whisker plots
  258. Key technique
  259. Example
  260. PHStat
  261. In-depth excel
  262. EG3.4 The covariance and the coefficient of correlation
  263. The covariance
  264. Key technique
  265. Example
  266. In-depth excel
  267. The coefficient of correlation
  268. Key technique
  269. Example
  270. In-depth excel
  271. End of part 1 problems
  272. Part 2 Measuring uncertainty
  273. Chapter 4 Basic probability
  274. Learning objective
  275. 4.1 Basic probability concepts
  276. Learning objective 1
  277. Events and sample spaces
  278. Contingency tables and venn diagrams
  279. Marginal probability
  280. Learning objective 2
  281. Joint probability
  282. General addition rule
  283. 4.2 Conditional probability
  284. Learning objective 3
  285. Calculating conditional probabilities
  286. Decision trees
  287. Statistical independence
  288. Multiplication rules
  289. Marginal probability using the general multiplication rule
  290. 4.3 Bayes’ theorem
  291. Learning objective 4
  292. 4.4 Counting rules
  293. Learning Objective 5
  294. 4.5 Ethical issues and probability
  295. 4 Assess your progress
  296. Summary
  297. Key formulas
  298. Chapter review problems
  299. Checking your understanding
  300. Applying the concepts
  301. Chapter 4 Excel guide
  302. EG4.1 Basic probability concepts
  303. Simple and Joint Probability and the General Addition Rule
  304. Key technique
  305. Example
  306. PHStat
  307. In-depth Excel
  308. EG4.2 Conditional Probability
  309. In-depth Excel
  310. EG4.3 Bayes’ theorem
  311. Key technique
  312. Example
  313. In-depth Excel
  314. EG4.4 Counting Rules
  315. Counting Rule 1
  316. In-depth Excel
  317. Counting Rule 2
  318. In-depth Excel
  319. Counting Rule 3
  320. In-depth Excel
  321. Counting Rule 4
  322. In-depth Excel
  323. Counting Rule 5
  324. In-depth Excel
  325. Chapter 5 Some important discrete probability distributions
  326. Learning objectives
  327. 5.1 Probability distribution for a discrete random variable
  328. Learning objectives 1
  329. Expected Value of a Discrete Random Variable
  330. Learning objectives 2
  331. Variance and Standard Deviation of a Discrete Random Variable
  332. 5.2 Covariance and its application in finance
  333. Learning objectives 3
  334. Covariance
  335. Expected Value, Variance and Standard Deviation of the Sum of Two Random Variables
  336. Portfolio Expected Return and Portfolio Risk
  337. 5.3 Binomial distribution
  338. Learning objectives 4
  339. 5.4 Poisson distribution
  340. Learning objectives 5
  341. 5.5 Hypergeometric distribution
  342. Learning objectives 6
  343. 5 Assess your progress
  344. Summary
  345. Key formulas
  346. Chapter review problems
  347. Checking your understanding
  348. Applying the concepts
  349. Chapter 5 Excel guide
  350. EG5.1 The probability distribution for a discrete variable
  351. Key technique
  352. Example
  353. In-depth excel
  354. EG5.2 Covariance of a probability distribution and its application in finance
  355. Key technique
  356. Example
  357. PHStat
  358. In-depth excel
  359. EG5.3 Binomial distribution
  360. Key technique
  361. Example
  362. PHStat
  363. In-depth Excel
  364. EG5.4 Poisson distribution
  365. Key technique
  366. Example
  367. PHStat
  368. In-depth Excel
  369. EG5.5 Hypgeometric distribution
  370. Key technique
  371. Example
  372. PHStat
  373. In-depth Excel
  374. Chapter 6 The normal distribution and other continuous distributions
  375. Learning objectives
  376. 6.1 Continuous probability distributions
  377. 6.2 The normal distribution
  378. Learning objective 1
  379. 6.3 Evaluating normality
  380. Learning objective 2
  381. Evaluating the properties
  382. Constructing a normal probability plot
  383. 6.4 The uniform distribution
  384. Learning objective 3
  385. 6.5 The exponential distribution
  386. Learning objective 4
  387. 6.6 The normal approximation to the binomial distribution
  388. Learning objective 5
  389. Need for a continuity correction
  390. Approximating the binomial distribution
  391. Calculating a probability approximation for an individual value
  392. 6 Assess your progress
  393. Summary
  394. Key formulas
  395. Chapter review problems
  396. Checking your understanding
  397. Applying the concepts
  398. Chapter 6 Excel guide
  399. EG6.1 Continuous probability distributions
  400. EG6.2 The Normal Distribution
  401. Key technique
  402. Example
  403. PHStat
  404. In-depth excel
  405. EG6.3 Evaluating normality
  406. Comparing Data Characteristics to Theoretical Properties
  407. Constructing the normal probability plot
  408. Key technique
  409. Example
  410. PHStat
  411. In-depth excel
  412. EG6.4 The uniform distribution
  413. EG6.5 The exponential distribution
  414. Key technique
  415. Example
  416. PHStat
  417. In-depth excel
  418. Chapter 7 Sampling distributions
  419. Learning objectives
  420. 7.1 Sampling distributions
  421. Learning objective 1
  422. 7.2 Sampling distribution of the mean
  423. The unbiased property of the sample mean
  424. Standard Error of the Mean
  425. Sampling from normally distributed populations
  426. Learning objective 2
  427. Sampling from non-normally distributed populations – the central limit theorem
  428. Learning objective 3
  429. 7.3 Sampling distribution of the proportion
  430. Learning objective 4
  431. 7 Assess your progress
  432. Summary
  433. Key formulas
  434. References
  435. Chapter review problems
  436. Checking your understanding
  437. Applying the concepts
  438. Chapter 7 Excel guide
  439. EG7.1 Sampling distribution of the mean
  440. Key technique
  441. Example
  442. Analysis ToolPak
  443. EG7.2 Central limit theorem
  444. End of part 2 problems
  445. Part 3 Drawing conclusions about populations based only on sample information
  446. Chapter 8 Confidence interval estimation
  447. Learning objectives
  448. 8.1 Confidence interval estimation for the mean (σ known)
  449. Learning objective 1
  450. 8.2 Confidence interval estimation for the mean (σ unknown)
  451. Student’s t distribution
  452. Properties of the t distribution
  453. The concept of degrees of freedom
  454. The confidence interval statement
  455. Learning objective 1
  456. 8.3 Confidence interval estimation for the proportion
  457. Learning objective 2
  458. 8.4 Determining sample size
  459. Learning objective 3
  460. Sample size determination for the mean
  461. Sample size determination for the proportion
  462. Learning objective 3
  463. 8.5 Applications of confidence interval estimation in auditing
  464. Estimating the population total amount
  465. Learning objective 4
  466. Difference estimation
  467. One-Sided confidence interval estimation of the rate of non-compliance with internal controls
  468. Learning objective 4
  469. 8.6 More on confidence interval estimation and ethical issues
  470. 8 Assess your progress
  471. Summary
  472. Key formulas
  473. References
  474. Chapter review problems
  475. Checking your understanding
  476. Applying the concepts
  477. Report writing exercise
  478. Chapter 8 Excel guide
  479. EG8.1 Confidence interval estimate for the mean (σ known)
  480. EG8.2 Confidence interval estimate for the mean (σ unknown)
  481. EG8.3 Confidence interval estimate for the proportion
  482. EG8.4 Sample size determination for the mean
  483. EG8.5 Sample size determination for the proportion
  484. EG8.6 Confidence interval estimate for the population total
  485. EG8.7 Confidence interval estimate for the total difference
  486. Chapter 9 Fundamentals of hypothesis testing: One-sample tests
  487. Learning objectives
  488. 9.1 Hypothesis-testing methodology
  489. The null and alternative hypotheses
  490. Learning objective 1
  491. Determining the test statistic
  492. Regions of rejection and non-rejection
  493. Risks in decision making using hypothesis testing
  494. The level of significance (α)
  495. The confidence coefficient
  496. The risk of type II error (β)
  497. Learning objective 2
  498. The power of a test
  499. Risks in decision making: A delicate balance
  500. 9.2 Z test of hypothesis for the mean (σ known)
  501. Learning objective 2
  502. Learning objective 3
  503. The critical value approach to hypothesis testing
  504. The p-value approach to hypothesis testing
  505. A connection between confidence interval estimation and hypothesis testing
  506. 9.3 One-tail tests
  507. Learning objective 2
  508. The critical value approach
  509. The p-value approach
  510. Learning objective 1
  511. 9.4 t Test of hypothesis for the mean (σ unknown)
  512. The critical value approach
  513. Learning objective 2
  514. The p-value approach
  515. Checking assumptions
  516. Learning objective 2
  517. 9.5 Z test of hypothesis for the proportion
  518. Learning objective 3
  519. The critical value approach
  520. The p-value approach
  521. 9.6 The power of a test
  522. Learning objective 2
  523. 9.7 Potential hypothesis-testing pitfalls and ethical issues
  524. Learning objective 4
  525. Data-collection method – randomisation
  526. Learning objective 5
  527. Informed consent from human respondents
  528. Type of test: Two-tail or one-tail
  529. Choice of level of significance, ?
  530. Data snooping
  531. Cleansing and discarding of data
  532. Reporting of findings
  533. Statistical significance versus practical significance
  534. Statistical insignificance versus importance
  535. 9 Assess your progress
  536. Summary
  537. Key formulas
  538. References
  539. Chapter review problems
  540. Checking your understanding
  541. Applying the concepts
  542. Report writing exercise
  543. Chapter 9 Excel guide
  544. EG9.1 Z test for the mean, σ known
  545. EG9.2 t test for the mean, σ unknown
  546. EG9.3 Z test for the proportion
  547. Chapter 10 Hypothesis testing: two-sample tests
  548. Learning objectives
  549. 10.1 Comparing the means of two independent populations
  550. Learning objective 1
  551. Z test for the difference between two means
  552. Pooled-variance t test for the difference between two means
  553. Confidence interval estimate for the difference between the means of two independent populations
  554. Separate-variance t test for the difference between two means
  555. 10.2 Comparing the means of two related populations
  556. Learning objective 2
  557. Paired t test
  558. Confidence interval estimate for the mean difference
  559. 10.3 F test for the difference between two variances
  560. Learning objective 3
  561. Finding lower-tail critical values
  562. 10.4 Comparing two population proportions
  563. Learning objective 4
  564. Z test for the difference between two proportions
  565. Confidence interval estimate for the difference between two proportions
  566. 10 Assess your progress
  567. Summary
  568. Key formulas
  569. Chapter review problems
  570. Checking your understanding
  571. Applying the concepts
  572. Report writing exercise
  573. References
  574. Chapter 10 Excel guide
  575. EG10.1 Comparing the means of two independent populations
  576. Pooled-variance t test for the difference between two means
  577. Key technique
  578. Example
  579. PHStat
  580. Analysis ToolPak
  581. Confidence interval estimate for the difference between two means
  582. PHStat
  583. t test for the difference between two means, assuming unequal variances
  584. Key technique
  585. Example
  586. PHStat
  587. Analysis ToolPak
  588. EG10.2 Comparing the means of two related populations
  589. Paired t test
  590. Key technique
  591. Example
  592. PHStat
  593. Analysis ToolPak
  594. EG10.3 f test for the difference between two variances
  595. Key technique
  596. Example
  597. PHStat
  598. Analysis ToolPak
  599. EG10.4 Comparing the proportions of two independent populations
  600. Z test for the difference between two proportions
  601. Key technique
  602. Example
  603. PHStat
  604. Confidence interval estimate for the difference between two proportions
  605. PHStat
  606. Chapter 11 Analysis of variance
  607. Learning objectives
  608. 11.1 The completely randomised design: One-way analysis of variance
  609. Learning objective 1
  610. Learning objective 2
  611. F test for differences between more than two means
  612. Multiple comparisons: the tukey–kramer procedure
  613. ANOVA Assumptions
  614. The levene test for homogeneity of variance
  615. 11.2 The randomised block design
  616. Learning objective 3
  617. Tests for the treatment and block effects
  618. Multiple comparisons: The tukey procedure
  619. 11.3 The factorial design: Two-way analysis of variance
  620. Learning objective 4
  621. Testing for factor and interaction effects
  622. Interpreting interaction effects
  623. Multiple comparisons: The tukey procedure
  624. 11 Assess your progress
  625. Summary
  626. Key formulas
  627. Chapter review problems
  628. Checking your understanding
  629. Applying the concepts
  630. Report writing exercise
  631. References
  632. Chapter 11 Excel guide
  633. EG11.1 The completely randomised design: One-way ANOVA
  634. Analysing variation in one-way ANOVA
  635. Key technique
  636. F test for differences among more than two means
  637. Key technique
  638. Example
  639. PHStat
  640. Analysis ToolPak
  641. Multiple comparisons: The tukey–kramer procedure
  642. Key technique
  643. Example
  644. PHStat
  645. Analysis ToolPak
  646. Levene test for homogeneity of variance
  647. Key technique
  648. Example
  649. PHStat
  650. Analysis ToolPak
  651. EG11.2 The randomised block design
  652. Key technique
  653. Example
  654. PHStat
  655. Analysis ToolPak
  656. EG11.3 The factorial design: Two-way ANOVA
  657. Key technique
  658. Example
  659. PHStat
  660. Analysis ToolPak
  661. Visualising interaction effects: The cell means plot
  662. Key technique
  663. Example
  664. PHStat
  665. End of part 3 problems
  666. Part 4 Determining cause and making reliable forecasts
  667. Chapter 12 Simple linear regression
  668. Learning objectives
  669. 12.1 Types of regression models
  670. Learning objective 1
  671. 12.2 Determining the simple linear regression equation
  672. The least-squares method
  673. 12.3 Measures of variation
  674. Calculating the sum of squares
  675. The coefficient of determination
  676. Learning objective 3
  677. Standard error of the estimate
  678. 12.4 Assumptions
  679. Learning objective 4
  680. 12.5 Residual analysis
  681. Evaluating the assumptions
  682. Linearity
  683. Independence
  684. Normality
  685. Equal variance
  686. 12.6 Measuring autocorrelation – The Durbin–Watson statistic
  687. Learning objective 4
  688. Residual plots to detect autocorrelation
  689. The durbin-watson statistic
  690. 12.7 Inferences about the slope and correlation coefficient
  691. t test for the slope
  692. Learning objective 5
  693. F Test for the slope
  694. Confidence interval estimate of the slope (β1)
  695. t test for the correlation coefficient
  696. Learning objective 6
  697. 12.8 Estimation of mean values and prediction of individual values
  698. Learning objectives 7
  699. The confidence interval estimate
  700. The prediction interval
  701. 12.9 Pitfalls in regression and ethical issues
  702. Learning objective 8
  703. 12 Assess your progress
  704. Summary
  705. Key formulas
  706. Chapter review problems
  707. Checking your understanding
  708. Applying the concepts
  709. References
  710. Chapter 12 Excel guide
  711. EG12.1 Types of regression models
  712. EG12.2 Determining the simple linear regression equation
  713. Key technique
  714. Example
  715. PHStat
  716. Analysis ToolPak
  717. EG12.3 Measures of variation
  718. EG12.4 Assumptions of regression
  719. EG12.5 Residual analysis
  720. Key technique
  721. Example
  722. PHStat
  723. Analysis ToolPak
  724. EG12.6 Measuring autocorrelation: The durbin-watson statistic
  725. Key technique
  726. Example
  727. PHStat
  728. EG12.7 Inferences about the slope and correlation coefficient
  729. EG12.8 Estimation of mean values and prediction of individual values
  730. Key technique
  731. Example
  732. PHStat
  733. Chapter 13 Introduction to multiple regression
  734. Learning objectives
  735. 13.1 Developing the multiple regression model
  736. Learning objective 1
  737. Interpreting the regression coefficients
  738. Predicting the dependent variable, Y
  739. 13.2 R2, adjusted R2 and the overall F test
  740. Coefficients of multiple determination
  741. Test for the significance of the overall multiple regression model
  742. 13.3 Residual analysis for the multiple regression model
  743. Learning objective 1
  744. 13.4 Inferences concerning the population regression coefficients
  745. Learning objective 2
  746. Tests of hypothesis
  747. Confidence interval estimation
  748. 13.5 Testing portions of the multiple regression model
  749. Learning objective 2
  750. Coefficients of partial determination
  751. 13.6 Using dummy variables and interaction terms in regression models
  752. Learning objectives 3
  753. Interactions
  754. 13.7 Collinearity
  755. Learning objective 4
  756. 13 Assess your progress
  757. Summary
  758. Key formulas
  759. Chapter review problems
  760. Checking your understanding
  761. Applying the concepts
  762. References
  763. Chapter 13 Excel guide
  764. EG13.1 Developing a multiple regression model
  765. Interpreting the regression coefficients
  766. Key technique
  767. Example
  768. PHStat
  769. Analysis toolpak
  770. Predicting the dependent variable Y
  771. Key technique
  772. Example
  773. PHStat
  774. EG13.2 r2, adjusted r2 and the overall F test
  775. EG13.3 Residual analysis for the multiple regression model
  776. Key technique
  777. Example
  778. PHStat
  779. Analysis toolpak
  780. EG13.4 Inferences concerning the population regression coefficients
  781. EG13.5 Testing portions of the multiple regression model
  782. Key technique
  783. Example
  784. PHStat
  785. Chapter 14 Time-series forecasting and index numbers
  786. Learning objectives
  787. 14.1 The importance of business forecasting
  788. Learning objective 1
  789. 14.2 Component factors of the classical multiplicative time-series model
  790. Learning objective 2
  791. 14.3 Smoothing the annual time series
  792. Learning objective 3
  793. Moving averages
  794. Exponential smoothing
  795. 14.4 Least-squares trend fitting and forecasting
  796. Learning objective 4
  797. Linear trend model
  798. Quadratic trend model
  799. Exponential trend model
  800. Model selection using first, second and percentage differences
  801. Applying the concepts
  802. 14.5 The holt–winters method for trend fitting and forecasting
  803. Learning objective 5
  804. Applying the concepts
  805. 14.6 Autoregressive modelling for trend fitting and forecasting
  806. Learning objective 6
  807. Second-order autoregressive model
  808. 14.7 Choosing an appropriate forecasting model
  809. Learning objective 7
  810. Performing a residual analysis
  811. Measuring the magnitude of the residual error with squared or absolute differences
  812. Principle of parsimony
  813. Comparison of five forecasting methods
  814. 14.8 Time-series forecasting of seasonal data
  815. Learning objective 8
  816. Least-squares forecasting with monthly or quarterly data
  817. 14.9 Index numbers
  818. Learning objective 9
  819. The price index
  820. Aggregate price indices
  821. Weighted aggregate price indices
  822. Laspeyres price index
  823. Paasche price index
  824. Some common price indices
  825. 14.10 Pitfalls in Time-series Forecasting
  826. 14 Assess your progress
  827. Summary
  828. Key formulas
  829. Chapter review problems
  830. Checking your understanding
  831. Applying the concepts
  832. References
  833. Chapter 14 Excel guide
  834. EG14.1 The importance of business forecasting
  835. EG14.2 Component factors of time-series models
  836. EG14.3 Smoothing an annual time series
  837. Moving averages
  838. Key technique
  839. Example
  840. Exponential smoothing
  841. Key technique
  842. Example
  843. Analysis ToolPak
  844. EG14.4 Least-squares trend fitting and forecasting
  845. The linear trend model
  846. The quadratic trend model
  847. The exponential trend model
  848. Key technique
  849. Model selection using first, second, and percentage differences
  850. EG14.5 Autoregressive modelling for trend fitting and forecasting
  851. Creating lagged predictor variables
  852. Autoregressive modelling
  853. EG14.6 Choosing an appropriate forecasting model
  854. Performing a residual analysis
  855. Measuring the magnitude of the residuals through squared or absolute differences
  856. A comparison of five forecasting methods
  857. EG14.7 Time-series forecasting of seasonal data
  858. Least-squares forecasting with monthly or quarterly data
  859. Chapter 15 Chi-square tests
  860. Learning objectives
  861. 15.1 Chi-square test for the difference between two proportions (independent samples)
  862. Learning objective 1
  863. 15.2 Chi-Square test for differences between more than two proportions
  864. The marascuilo procedure
  865. Learning objective 2
  866. 15.3 Chi-Square test of independence
  867. Learning objective 3
  868. 15.4 Chi-Square goodness-of-fit tests
  869. Learning objective 4
  870. Chi-Square goodness-of-fit test for a poisson distribution
  871. Chi-Square goodness-of-fit test for a normal distribution
  872. 15.5 Chi-Square test for a variance or standard deviation
  873. Learning objective 5
  875. 15 Assess your progress
  876. Summary
  877. Key formulas
  878. Chapter review problems
  879. Checking your understanding
  880. Applying the concepts
  881. Team project
  882. References
  883. Chapter 15 Excel guide
  884. EG15.1 For χ2 test for the difference between two proportions
  885. EG15.2 For χ2 test for the differences in more than two proportions and for the χ2 test of independence
  886. End of Part 4 problems
  887. Part 5 Further topics in stats
  888. Chapter 16 Multiple regression model building
  889. Learning objectives
  890. 16.1 Quadratic regression model
  891. Learning objectives 1
  892. Finding the regression coefficients and predicting Y
  893. Testing for the significance of the quadratic model
  894. Testing the quadratic effect
  895. The coefficient of multiple determination
  896. 16.2 Using transformations in regression models
  897. Learning objectives 2
  898. The square-root transformation
  899. The log transformation
  900. 16.3 Influence analysis
  901. Learning objectives 3
  902. The hat matrix elements hi
  903. The studentised deleted residuals ti
  904. Cook’s distance statistic Di
  905. Overview
  906. 16.4 Model building
  907. Learning objectives 4
  908. The stepwise regression approach to model building
  909. The best-subsets approach to model building
  910. Model validation
  911. 16.5 Pitfalls in multiple regression and ethical issues
  912. Learning objectives 5
  913. Pitfalls in multiple regression
  914. Ethical considerations
  915. 16 Assess your progress
  916. Summary
  917. Key formulas
  918. Chapter review problems
  919. Checking your understanding
  920. Applying the concepts
  921. References
  922. Chapter 6 Excel guide
  923. EG16.1 The quadratic regression model
  924. Key technique
  925. Example
  926. EG16.2 Using transformations in regression models
  927. The square-root transformation
  928. The log transformation
  929. EG16.3 Collinearity
  930. PHStat
  931. EG16.4 Model building
  932. The stepwise regression approach to model building
  933. Key technique
  934. Example
  935. PHStat
  936. The best-subsets approach to model building
  937. Key technique
  938. Example
  939. PHStat
  940. Chapter 17 Decision making
  941. Learning objectives
  942. 17.1 Payoff tables and decision trees
  943. Learning objectives 1
  945. 17.2 Criteria for decision making
  946. Learning objectives 2
  947. Expected monetary value
  948. Expected opportunity loss
  949. Return-to-risk ratio
  950. 17.3 Decision making with sample information
  951. Learning objectives 2
  953. 17.4 Utility
  954. Learning objectives 4
  956. 17 Assess your progress
  957. Summary
  958. Key formulas
  959. Chapter review problems
  960. Checking your understanding
  961. Applying the concepts
  962. References
  963. Chapter 17 Excel guide
  964. EG17.1 Opportunity loss
  965. EG17.2 Expected monetary value
  966. EG17.3 Expected opportunity loss
  967. Chapter 18 Statistical applications in quality management
  968. Learning objectives
  969. 18.1 Total quality management
  970. 18.2 Six sigma management
  971. The DMAIC model
  972. 18.3 The theory of control charts
  973. Learning objectives 1
  974. 18.4 Control chart for the proportion — The p chart
  975. Learning objectives 2
  976. Learning objectives 3
  978. 18.5 The red bead experiment — understanding process variability
  979. Learning objectives 1
  981. 18.6 Control chart for an area of opportunity — the c chart
  982. Learning objectives 2
  983. Learning objectives 3
  985. 18.7 Control charts for the range and the mean
  986. Learning objectives 2
  987. Learning objectives 3
  988. The R chart
  989. The X¯ Chart
  990. 18.8 Process capability
  991. Learning objectives 4
  992. Customer satisfaction and specification limits
  993. Capability indices
  994. CPL, CPU and Cpk
  995. 18 Assess your progress
  996. Summary
  997. Key formulas
  998. Chapter review problems
  999. Checking your understanding
  1000. Applying the concepts
  1001. References
  1002. Chapter 18 Excel guide
  1003. EG18.1 Total quality management
  1004. EG18.2 Six sigma management
  1005. EG18.3 The theory of control charts
  1006. EG18.4 Control chart for the proportion: the p chart
  1007. Example
  1008. PHStat
  1009. In-depth excel
  1010. EG18.5 The red bead experiment: understanding process variability
  1011. EG18.6 Control chart for an area of opportunity: the c chart
  1012. Example
  1013. PHStat
  1014. In-depth excel
  1015. EG18.7 Control charts for the range and the mean
  1016. The R Chart and the X¯ chart
  1017. Example
  1018. PHStat
  1019. In-depth excel
  1020. EG18.8 Process capability
  1021. Chapter 19 Further non-parametric tests
  1022. Learning objectives
  1023. 19.1 McNemar test for the difference between two proportions (related samples)
  1024. Learning objectives 1
  1025. 19.2 Wilcoxon rank sum test — non-parametric analysis for two independent populations
  1026. 19.3 Wilcoxon signed ranks test — non-parametric analysis for two related populations
  1028. 19.4 Kruskal—Wallis rank test — non-parametric analysis for the one-way anova
  1030. 19.5 Friedman rank test — non-parametric analysis for the randomised block design
  1032. 19 Assess your progress
  1033. Summary
  1034. Key formulas
  1035. Chapter review problems
  1036. Checking your understanding
  1037. Chapter 19 Excel guide
  1038. EG19.1 McNemar test for the difference between two proportions (related samples)
  1039. Key technique
  1040. Example
  1041. PHStat
  1042. In-depth excel
  1043. EG19.2 Wilcoxon rank sum test: non-parametric analysis for two independent populations
  1044. Key technique
  1045. Example
  1046. PHStat
  1047. In-depth excel
  1048. EG19.3 Wilcoxon signed ranks test: non-parametric analysis for two related populations
  1049. Key technique
  1050. Example
  1051. PHStat
  1052. In-depth excel
  1053. EG19.4 Kruskal—wallis rank test: non-parametric analysis for the one-way anova
  1054. Key technique
  1055. Example
  1056. PHStat
  1057. In-depth excel
  1058. EG19.5 Friedman rank test: non-parametric analysis for the randomised block design
  1059. Key technique
  1060. Example
  1061. PHStat
  1062. In-depth excel
  1063. Chapter 20 Business analytics
  1064. Learning objectives
  1065. 20.1 Predictive analytics
  1066. Learning objective 1
  1067. 20.2 Classification and regression trees
  1068. Learning objective 1
  1069. Regression tree example
  1070. 20.3 Neural networks
  1071. Learning objective 2
  1072. Multilayer perceptrons
  1073. 20.4 Cluster analysis
  1074. Learning objective 3
  1075. 20.5 Multidimensional scaling
  1076. Learning objective 4
  1077. 20 Assess your progress
  1078. Summary
  1079. Key formulas
  1080. Chapter review problems
  1081. Checking your understanding
  1082. Applying the concepts
  1083. References
  1084. Chapter 20 Software guide
  1085. Introduction
  1086. Jmp
  1087. Tableau public
  1088. Data Discovery
  1089. In-depth Excel
  1090. JMP
  1091. SG20.1 Predictive analytics
  1092. SG20.2 Classification and regression trees
  1093. Classification Tree
  1094. JMP
  1095. Regression Tree
  1096. JMP
  1097. SG20.3 Neural networks
  1098. JMP
  1099. SG20.4 Cluster analysis
  1100. JMP 10
  1101. SG20.5 Multidimensional scaling
  1102. JMP
  1103. Chapter 21 Data analysis: the big picture
  1104. Learning objectives
  1105. 21.1 Analysing numerical variables
  1106. Learning objectives 1
  1107. Learning objectives 2
  1108. Describing the characteristics of a numerical variable
  1109. Reaching conclusions about the population mean and/or standard deviation
  1110. Determining whether the mean and/or standard deviation differs depending on the group
  1111. If the grouping variable defines two independent groups and you are interested in central tendency
  1112. If the grouping variable defines two groups of matched samples or repeated measurements and you are interested in central tendency
  1113. If the grouping variable defines two independent groups and you are interested in variability
  1114. If the grouping variable defines more than two independent groups and you are interested in central tendency
  1115. If the grouping variable defines more than two groups of matched samples or repeated measurements and you are interested in central tendency
  1116. Determining which factors affect the value of a variable
  1117. Predicting the value of a variable based on the values of other variables
  1118. Determining whether the values of a variable are stable over time
  1119. 21.2 Analysing categorical variables
  1120. Learning objectives 1
  1121. Describing the proportion of items of interest in each category
  1122. Reaching conclusions about the proportion of items of interest
  1123. Determining whether the proportion of items of interest differs depending on the group
  1124. For two categories and two independent groups
  1125. For two categories and two groups of matched or repeated measurements
  1126. For two categories and more than two independent groups
  1127. For more than two categories and more than two groups
  1128. Determining whether the proportion of items of interest is stable over time
  1129. 21.3 Predictive analytics
  1130. Learning objectives 2
  1131. 21 Assess your progress
  1132. Chapter review problems
  1133. End of part 5 problems
  1134. Appendices
  1135. A Review of arithmetic, algebra and logarithms
  1136. A.1 Rules for arithmetic operations
  1137. A.2 Rules for algebra: Exponents and square roots
  1138. A.3 Rules for logarithms
  1139. Base 10
  1140. Solution
  1141. Base e
  1142. Solution
  1143. B Summation Notation
  1144. Problem
  1145. Answer
  1146. References
  1147. C Statistical Symbols and Greek Alphabet
  1148. C.1 Statistical Symbols
  1149. C.2 Greek Alphabet
  1150. D PHStat User’s Guide
  1151. About this appendix
  1152. D.1 Sampling distributions simulation
  1153. D.2 Confidence interval estimate for the mean, sigma known
  1154. D.3 Confidence interval estimate for the mean, sigma unknown
  1155. D.4 Confidence interval estimate for the proportion
  1156. D.5 Sample size determination for the mean
  1157. D.6 Sample size determination for the proportion
  1158. D.7 Confidence interval estimate for the population total
  1159. D.8 Confidence interval estimate for the total difference
  1160. D.9 Z Test for the mean, sigma known
  1161. D.10 t Test for the mean, sigma unknown
  1162. D.11 Z Test for the proportion
  1163. D.12 Chi-square test for differences in two proportions
  1164. D.13 Chi-square test
  1165. D.14 Opportunity loss
  1166. D.15 Expected opportunity loss
  1167. D.16 Expected monetary value
  1168. E Tables
  1169. F Using Microsoft Excel Analysis ToolPak
  1170. F.1 Configuring microsoft excel
  1171. F.2 Using the data analysis tools
  1172. Glossary

 

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