Statistics for Management and Economics10th EditionGerald Keller Publisher: Cengage LearningISBN: 9781285425450
Statistics for Management and Economics
Solutions for Statistics for Management and Economics
Browse All Chapters of This Textbook
Chapter 1 - What Is StatisticsChapter 2 - Graphical Descriptive Techniques IChapter 2.1 - Types Of Data And InformationChapter 2.2 - Describing A Set Of Nominal DataChapter 2.3 - Describing The Relationship Between Two Nominal Variables And Comparing Two Or More Nominal Data SetsChapter 3 - Graphical Descriptive Techniques IiChapter 3.1 - Graphical Techniques To Describe A Set Of Interval DataChapter 3.2 - Describing Time-series DataChapter 3.3 - Describing The Relationship Between Two Interval VariablesChapter 3.4 - Art And Science Of Graphical Presentations
Chapter 4 - Numerical Descriptive TechniquesChapter 4.1 - Measures Of Central LocationChapter 4.2 - Measures Of VariabilityChapter 4.3 - Measures Of Relative StandingChapter 4.4 - Measures Of Linear RelationshipChapter 4.6 - Comparing Graphical And Numerical TechniquesChapter 4.7 - General Guidelines For Exploring DataChapter 5.1 - Methods Of Collecting DataChapter 5.2 - SamplingChapter 5.3 - Sampling PlansChapter 5.4 - Sampling And Nonsampling ErrorsChapter 6 - ProbabilityChapter 6.1 - Assigning Probability To EventsChapter 6.2 - Joint, Marginal, And Conditional ProbabilityChapter 6.3 - Probability Rules And TreesChapter 6.4 - Bayes’s LawChapter 7 - Random Variables And Discrete Probability DistributionsChapter 7.1 - Random Variables And Probability DistributionsChapter 7.2 - Bivariate DistributionsChapter 7.3 - (optional) Applications In Finance: Portfolio Diversification And Asset AllocationChapter 7.4 - Binomial DistributionChapter 7.5 - Poisson DistributionChapter 8.1 - Probability Density FunctionsChapter 8.2 - Normal DistributionChapter 8.3 - (optional) Exponential DistributionChapter 8.4 - Other Continuous DistributionsChapter 9.1 - Sampling Distribution Of The MeanChapter 9.2 - Sampling Distribution Of A ProportionChapter 9.3 - Sampling Distribution Of The Difference Between Two MeansChapter 10.1 - Concepts Of EstimationChapter 10.2 - Estimating The Population Mean When The Population Standard Deviation Is KnownChapter 10.3 - Selecting The Sample SizeChapter 11.1 - Concepts Of Hypothesis TestingChapter 11.2 - Testing The Population Mean When The Population Standard Deviation Is KnownChapter 11.3 - Calculating The Probability Of A Type Ii ErrorChapter 12 - Inference About A PopulationChapter 12.1 - Inference About A Population Mean When The Standard Deviation Is UnknownChapter 12.2 - Inference About A Population VarianceChapter 12.3 - Inference About A Population ProportionChapter 12.4 - (optional) Applications In Marketing: Market SegmentationChapter 13 - Inference About Comparing Two PopulationsChapter 13.1 - Inference About The Difference Between Two Means: Independent SamplesChapter 13.2 - Observational And Experimental DataChapter 13.3 - Inference About The Difference Between Two Means: Matched Pairs ExperimentChapter 13.4 - Inference About The Ratio Of Two VariancesChapter 13.5 - Inference About The Difference Between Two Population ProportionsChapter 13.A - Review Of Chapters 12 And 13Chapter 14 - Analysis Of VarianceChapter 14.1 - One-way Analysis Of VarianceChapter 14.2 - Multiple ComparisonsChapter 14.4 - Randomized Block (two-way) Analysis Of VarianceChapter 14.5 - Two-factor Analysis Of VarianceChapter 14.6 - (optional) Applications In Operations Management: Finding And Reducing VariationChapter 14.A - Review Of Chapters 12 To 14Chapter 15 - Chi-squared TestsChapter 15.1 - Chi-squared Goodness-of-fit TestChapter 15.2 - Chi-squared Test Of A Contingency TableChapter 15.4 - (optional) Chi-squared Test For NormalityChapter 15.A - Review Of Chapters 12 To 15Chapter 16 - Simple Linear Regression And CorrelationChapter 16.2 - Estimating The CoefficientsChapter 16.3 - Error Variable: Required ConditionsChapter 16.4 - Assessing The ModelChapter 16.5 - Using The Regression EquationChapter 16.6 - Regression Diagnostics -iChapter 16.A - Review Of Chapters 12 To 16Chapter 17 - Multiple RegressionChapter 17.2 - Estimating The Coefficients And Assessing The ModelChapter 17.3 - Regression Diagnostics-iiChapter 17.4 - Regression Diagnostics-iii (time Series)Chapter 17.A - Review Of Chapters 12 To 17Chapter 18 - Model BuildingChapter 18.1 - Polynomial ModelsChapter 18.2 - Nominal Independent VariablesChapter 18.3 - (optional) Applications In Human Resources Management: Pay EquityChapter 18.4 - (optional) Stepwise RegressionChapter 19 - Nonparametric StatisticsChapter 19.1 - Wilcoxon Rank Sum TestChapter 19.2 - Sign Test And Wilcoxon Signed Rank Sum TestChapter 19.3 - Kruskal–wallis Test And Friedman TestChapter 19.4 - Spearman Rank Correlation CoefficientChapter 19.A - Review Of Statistical Inference (chapters 12 To 19)Chapter 20.2 - Smoothing TechniquesChapter 20.3 - Trend And Seasonal EffectsChapter 20.4 - Introduction To ForecastingChapter 20.5 - Forecasting ModelsChapter 21.1 - Process VariationChapter 21.2 - Control ChartsChapter 21.3 - Control Charts For Variables: X And S ChartsChapter 21.4 - Control Charts For Attributes: P ChartChapter 22.1 - Decision ProblemChapter 22.2 - Acquiring, Using, And Evaluating Additional Information
Book Details
STATISTICS FOR MANAGEMENT AND ECONOMICS, Tenth Edition, emphasizes applications over calculation. It illustrates how vital statistical methods and tools are for today's managers--and teaches you how to apply them to real business problems. Using a proven three-step "ICI" approach to problem solving, the text teaches you how to IDENTIFY the correct statistical technique by focusing on the problem objective and data type; how to COMPUTE the statistics doing them by hand, using Excel, or using MINITAB; and how to INTERPRET results in the context of the problem. This unique approach enhances your comprehension and practical skills. The text's vast assortment of data-driven examples, exercises, and cases covers the various functional areas of business, demonstrating the statistical applications that marketing managers, financial analysts, accountants, economists, and others use. These comprehensive applications give you hands-on practice, while solid pedagogical elements make the material more accessible and easy to apply to your world. Completely up-to-date, the tenth edition offers comprehensive coverage, current examples, and Excel 2013 and MINITAB 16 content.
Sample Solutions for this Textbook
We offer sample solutions for Statistics for Management and Economics homework problems. See examples below:
A population refers to a group of all items of interest to a statistics practitioner. The overall...Table-1 shows the result of bookstore’s price of textbooks as follows: Table -1...The precious metals include gold, silver, platinum, and others in the economy. The fund made up of...Table-1 shows the price for a 6 year as follows: Table-1YearPrice110214315422530625 The year of...Table–1 shows the sample as follows: Table-1PeopleSample117229312416511...Covariance is a measure that is used to measure the relationship between the movements of two...Covariance is a measure that is used to measure the relationship between the movements of two...Covariance is a measure that is used to measure the relationship between the movements of two...The mean and median of a woman’s bone density loss is calculated using statistical software and...
Sampling error refers to the inaccuracy in the prediction about the population that arises through...The probability of Marketing Acan be calculated as follows: P(marketing A)=0.053+0.237=0.290 Thus,...The probability distribution of random variable X is shown below: Table 1 X-2578P(X)0.590.150.250.01...The probability distribution of the random variable X is shown below: Table 1 X0123P(X)0.40.30.20.1...The probability distribution of refrigerator and stoves sold daily is shown below: Table 1...Probability can be calculated as follows: P=5!5!(5−5)! ×0.7745×(1−0.774)(5−5)=0.2778 Probability is...The probability distribution of the number of viewers of violent and non-violent television program...Suppose the given density function is F(X)={X25; (0<x<5)10−X25; (5<x<10). The density...The table for t value is as follows. Table 1...Sample size (n) can be calculated as follows. Sample size=(Z90%×Standard deviationPopulation...The population proportion of success (PS) is obtained using the statistical software. The result is...The difference between the two population means (DP) can be calculated as follows:...The null hypothesis (H0) is (μ1−μ2)=0, and the alternate hypothesis is (H1) (μ1−μ2)≠0. The t table...The unequal variance estimator (v) can be calculated as follows:...The null hypothesis (H0) is (μ1−μ2)=0, and the alternate hypothesis (H1) is (μ1−μ2)>0. The...Null hypothesis (H0) is (p1−p2)=0 and alternate hypothesis is (H1) (p1−p2)>0. The Z table value...Null hypothesis (H0) is (p1−p2)=0 and alternate hypothesis is (H1) (p1−p2)>0. The Z table value...The null hypothesis (H0) is (p1−p2)=0 and the alternate hypothesis (H1)is (p1−p2)>0. The...Table -1 shows the statistics values as follows: Table -1...The null and alternative hypothesis can be written as follows: H0:μ1=μ2=μ3H1:At least two means...t-value can be written as follows: α=0.05t(α2,n−k)=t(0.025,20)=2.086 LSD can be calculated as...The null and alternative hypothesis can be written as follows: H0:μ1=μ2=μ3=μ4H1:At least two means...Table -1 shows the 3 treatments as follows: Treatment123712810891216139136121011 SST (Total) can be...Table -1 shows the 3 treatments as follows: Treatment 1234654485567656 SST (Total) can be calculated...The null and alternative hypotheses for reading can be written as follows: H0:μ1=μ2=μ3=μ4H1:At least...The given hypothesis and alternative hypothesis are as follows: H0:P1=13,P2=13,P3=13H1: At least one...Figure 1 shows the scatter diagram of television and overweight. In the above figure, the vertical...Figure 1 shows the scatter diagram of Y and X. In the above figure, the position of the scatters...Figure 1 shows the scatter diagram of Y and X. In the above figure, the position of the scatters...The general regression equation can be written as follows: y^=b0+b1x The term y^ is the dependent...The value of coefficient (b1) can be calculated as follows: b1=sxysx2=74.023.47=21.33 The value of...The value of coefficient (b1) can be calculated as follows: b1=sxysx2=936.82378.77=2.47 The value of...The ANOVA table output is obtained using the statistical software. The result is given below: The...The general regression line can be derived as follows: The intercept is denoted by IC and the...Null hypothesis (H0) is ρS=0 and alternate hypothesis is (H1) ρS≠0. The calculated correlation...The null hypothesis (H0) is ρS=0, and the alternate hypothesis (H1) is ρS>0. The Spearman rank...The linear equation is y^=150+3t. Table 1 shows the estimated output (y^) that is obtained using the...The value of centerline (p¯) is 0.035. The lower control limit (LCL) can be calculated as follows:...The EMV value of a0can be calculated as follows: EMVa0=(Payoffa0, s1×P(s1)+Payoffa0,...The EPPI value can be calculated as follows. EPPI=(PayoffMaximum s1 row×P(s1)+PayoffMaximum s2...Table 1 shows that the posterior probabilities for I1. Table 1...The EMV of 25 calls is 50,000, EMV of 50 calls is 45,000((0.5×30,000)+(0.25×60,000)+(0.25×60,000)),...
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