Strategies To Consolidate Global Operations While Keeping Ke

Strategies to consolidate global operations while keeping key management

Final Exam Review chapter 10know The Three Ideas Of Sampling Examine Final Exam Reviewchapter 10know The Three Ideas Of Sampling Examine Final Exam Review Chapter 10 Know the three ideas of sampling. • Examine a part of the whole: A sample can give information about the population. vA parameter is a number used in a model of the population. vA statistic is a number that is calculated from the sample data. vThe sample to sample differences are called the sampling variability (or sampling error). • Randomize to make the sample representative. • The sample size is what matters. • In a simple random sample (SRS), every possible group of n individuals has an equal chance of being our sample. Chapter 13 • Know the general rules of probability and how to apply them. • The General Addition Rule says that : P(A) or P(B) = P(A) + P(B) – P(A and B). • The General Multiplication Rule says that : P(A and B) = P(A) x P(B|A). •Know that the conditional probability of an event B given the event A is P(B|A) = P(A and B)/P(A). •Know how to define and use independence: Events A and B are independent if P(A|B) = P(A) or P(A and B) = P(A) à— P(B) Chapter 14 • The expected value of a (discrete) random variable is: • The variance for a random variable is: • Rules combining RVs: E(X ± c) = E(X) ± c Var(X ± c) = Var(X) E(aX) = aE(X) Var(aX) = a2Var(X) ( ) ( )E X x P xµ = = ⋅∑ ( ) ( ) ( )22 Var X x P xσ µ= = − ⋅∑ Chapter 15 :Geometric Probability Model for Bernoulli Trials: GEOM(n, p) •p = probability of success •q = 1 – p = probability of failure •X = number of trials until the first success occur •Expected value: •Standard deviation: P X = x( ) = qx−1p E X( ) = µ = 1 p σ = q p2 Chapter 15: Binomial Probability Model for Bernoulli Trials: BINOM(n, p) •x = number of trials •p = probability of success •q = 1 – p = probability of failure •X = number of successes in n trials P X = x( ) = nCx pxqn−x, nCx = n! x! n − x( )! Mean: µ = np Standard deviation: σ = npq Chapter 15:Poisson for Small p • For rare events (small p), np may be less than 10. • Use the Poisson instead of the Normal model. • l = np mean number of successes • X = number of successes • • • Good approximation if n ³ 20 with p ≤ 0.05 or n ≤ 100 with p ≤ 0.10 ( ) ! ll- == xe P X x x ( ) , ( )l l= =E X SD X Chapter 16: One-Proportion Z-Interval • Conditions met, find level C confidence interval for p • Confidence interval is • Standard deviation estimated by • z specifies number of SEs needed for C% of random samples to yield confidence intervals that capture the true parameter. Use table below to get z • Interpretation : we are 95% confident that the interval contains the true proportion of X in the population pÌ‚ ± z * à—SE(pÌ‚) SE(pÌ‚) = pÌ‚qÌ‚ n Chapter 16: One-Proportion Z-Interval •Sampling Distribution for Proportions is Normal. • Mean is p. • σ (pÌ‚) = SD(pÌ‚) = pq n Chapter 16: One-Proportion Z-Interval Sample Size and Standard Deviation • • Larger sample size → Smaller standard deviaaon σ ( )=SD y n ˆ( )= pq SD p n Chapter 16: One-Proportion Z-Interval Chapter 17: The Central Limit Theorem •The Central Limit Theorem • The sampling distribution of any mean becomes nearly Normal as the sample size grows. •Requirements • Independent • Randomly collected sample •The sampling distribution of the means is close to Normal if either: • Large sample size • Population close to Normal Chapter 17 : A Practical Sampling Distribution Model •When certain assumptions and conditions are met, the standardized sample mean, follows a Student’s t-model with n – 1 degrees of freedom.

We estimate the standard deviation with t = y − µ SE y( ), SE y( ) = s n . Degrees of Freedom • For every sample size n there is a different Student’s t distribuaon. • Degrees of freedom: df = n – 1. • Similar to the “n – 1†in the formula for sample standard deviaaon • It is the number of independent quanaaes leh aher we’ve esamated the parameters. One Sample t-Interval for the Mean When the assumptions are met (seen later), the confidence interval for the mean is The critical value depends on the confidence level, C, and the degrees of freedom n – 1. y ± tn−1 à— SE(y ) tn−1 Use R to get : abs(qt(0.05/2,df)) tn−1 Chapter 18 :1-Proportion z-Test •Conditions • Same as a 1-Proportion z-Interval •Null Hypothesis • H0: p = p0 •Test Statistics • ( ) 0 ˆ ˆ p p z SD p - = ( ) 0 0ˆ p qSD p n = • Use pnorm(z) to calculate area to the left of z; 1-pnorm() calculates the area to the right of z; If your alternative hypothesis is p different than p0, you can use 2(1-pnorm(z)). This probability is the p-value, and we reject the Null Hypothesis if p-value,df) to calculate area to the leh of t µ = µ0 tn−1 = y − µ0 SE(y ) y : SE(y ) = σ n Chapter 18 • Write clear summaries to interpret a confidence interval or state a hypothesis test’s conclusion. • Understand P-values. • A P-value is the estimated probability of observing a statistic value at least as far from the (null) hypothesized value as the one we have actually observed. • A small P-value indicates that the statistic we have observed would be unlikely were the null hypothesis true.

That leads us to doubt the null. • A large P-value just tells us that we have insufficient evidence to doubt the null hypothesis. In particular, it does not prove the null to be true. Chapter 19: Type I and II Errors •Type I Error • Reject H0 when H0 is true. •Type II Error • Fail to reject H0 when H0 is false. •Medicine: Such as an AIDS test • Type I Error → False positive: Patient thinks he has the disease when he doesn’t. • Type II Error → False negative: Patient is told he is disease - free when in fact he has the disease. Chapter 19 Probabilities of Type I and II Errors • P (Type I Error) = a • This represents the probability that if H0 is true then we will reject H0. • P (Type II Error) = b • We cannot calculate b.

Saying H0 is false does not tell us what the parameter is. • Decreasing a results in an increase of b: By decreasing a, we fail to reject more ( it is harder to reject, e.g. p-value=0.04 we reject at a=0.05) • Reduce b for all alternatives, by increasing a. • The only way to decrease both is to increase the sample size. Chapters 20, 21 and 25: A Two-Proportion z- Test • H0: p1 – p2 = 0 • • • When Condiaons are met and the null hypothesis is true, • This staasac follows the Normal model, which we can use to obtain a P- value. • Use pnorm() to calculate the p-value SE pÌ‚1 − pÌ‚2( ) = pÌ‚1qÌ‚1 n1 + pÌ‚2qÌ‚2 n2 z = (pÌ‚1 − pÌ‚2 ) − 0 SE pÌ‚1 − pÌ‚2( ) Chapters 20, 21 and 25: A Two-Sample t-Test for the Difference Between Means • H0: µ1 – µ2 = D0 (D0 usually 0) • • When the conditions are met and the null hypothesis is true, use the Student’s t-model to find the P-value. • Degrees of freedom will be given ! • Use pt() to calculate the p-value t = y1 − y 2( )− Δ0 SE(y1 − y 2 ) SE(y1 − y 2 ) = s1 2 n1 + s2 2 n2 Chapters 20, 21 and 25: One Way ANOVA F- Test • H0: µ1 = µ2 = … µk • • MST is found from the variance of the means of the treatment groups. • MSE is found by pooling the variances within each of the treatment groups. • If F is large, reject H0. • Use aov() to get the p-value T E MS F MS = Testing Hypothesis • Reject Null Hypothesis if p-value

Evaluation Criteria Marks Awarded Grading Criteria Required Elements Comments Academic journals / excellent 6.1-7.9 good 5-6 satisfactory 1-4.9 below expectation ‘0-Topic absent  inclusion of a minimum of 10 (ten) relevant articles supporting this paper, 3 of which are academic peer reviewed this is not a separate section in project, rather is documented throughout the project Overall structural quality / excellent 15-18 good 11-14 satisfactory 6-10 below expectation 1-5 Off topic 0 Topic absent  Word processed (1)  double spaced (1)  12-point font (1)  paragraph form, essay style (2)  cover page including organizational branding (2)  table of contents (2)  proper grammar, correct spelling, evidence of proofreading (2)  Minimum 1500 words (5)  Complete references of all work of other individuals, including course notes, textbooks, journals, websites, interviews, proprietary documents within APA citation style. (2)  Appendices referenced throughout project (2) this is not a separate section in project, rather is documented throughout the project Introduction / excellent 6.1-7.9 good 5-6 satisfactory 1-4.9 below expectation 0-Topic absent  Section clearly identified (2)  Clearly introduces: Company (1) Brand (1) Purpose of project (overview, future strategies, the importance of employees) (5) Salient points of projects (1) Research Topic Industry Overview / excellent 6.1-7.9 good 5-6 satisfactory 1-4.9 below expectation ‘0-Topic absent  Section clearly identified (2)  Current global overview (2)  2020 Global Overview (2)  Brand country current overview (2)  2020 brand country overview (2) Research Topic Company & Brand Overview / excellent 15-18 good 11-14 satisfactory 6-10 below expectation 1-5 Off topic 0 Topic Clearly identified (2)  Company overview (9)  History Main Office(s) Year Established Number of properties Number of rooms, Locations (countries) Comparison to competition Analysis of current company situation (financial, reputation, positioning)  Brand overview (9) History Main Office(s) Year Established Number of properties Number of rooms, Locations (countries) Comparison to competition Analysis of current brand situation (financial, reputation, positioning) Research topic Strategies to develop the brand until 2020 / excellent 15-18 good 11-14 satisfactory 6-10 below expectation 1-5 Off topic 0 Topic clearly identified (2)  Minimum 5 strategies (5)  General explanation of each strategy (5)  Techniques to implement each strategy (5)  Relevance of strategies to the brand (3) Conclusion / excellent 6.1-7.9 good 5-6 satisfactory 1-4.9 below expectation ‘0-Topic absent  Section clearly identified (2)  Complete conclusion answering purpose and aim of development strategies (3)  Complete conclusion answering purpose and aim of retaining and hiring key employees (3)  Final personal analysis (2)