Business analytics data analysis and decision making 6th edition albright test bank

... tendency? a mean, median, and mode b mean, variance, and standard deviation c mean, median, and variance d mean, median, and standard deviation e first quartile, second quartile, and third quartile ... following data values: 7, 5, 6, 4, 7, 8, and 12? a The mean, median, and mode are all equal b Only the mean and median are equal c Only the mean and mode are equal d Only the median and mode are ... that the histogram of a data set is symmetric and bell shaped, with a mean of 75 and standard deviation of 10 Then, approximately 95% of the data values were between 55 and 95 a True b False ANSWER:...

Business analytics data analysis and decision making 5th by wayne l winston apendix a

... English  There is no single best way to write a statistical report  However, there are some bad habits that practically all readers will object to, and there are some good habits that will make ... presents a great statistical analysis, might never be read at all © 2015 Cengage Learning All Rights Reserved May not be scanned, copied or duplicated, or posted to a publicly accessible website, ... sentence, and even each word has a purpose, and eliminate everything that is extraneous  Remember that many professionals have a one-page rule  Let the charts the talking  A well-constructed chart...

Business analytics data analysis and decision making 5th by wayne l winston chapter 01

... given more people the power and responsibility to analyze data and make decisions  A large amount of data already exists and will only increase in the future © 2015 Cengage Learning All Rights Reserved ... problem  There are different types of models, and each can be a valuable aid in solving a real problem:  Graphical models  Algebraic models  Spreadsheet models © 2015 Cengage Learning All Rights ... modeling as a seven-step process, but not all problems require all seven steps Define the problem Collect and summarize data Develop a model Verify the model Select one or more suitable decisions...

Business analytics data analysis and decision making 5th by wayne l winston chapter 02

... empirical rules should be applied with caution, especially when the data are clearly skewed, as illustrated by the calculations for baseball salaries below © 2015 Cengage Learning All Rights Reserved ... to a publicly accessible website, in whole or in part Example 2.7: Catalog Marketing.xlsx (slide of 2)  Objective: To illustrate Excel tables for analyzing the HyTex data  Solution: Data set ... duplicated, or posted to a publicly accessible website, in whole or in part Example 2.3: Baseball Salaries 2011.xlsx (slide of 2)  Objective: To learn how salaries are distributed across all...

Business analytics data analysis and decision making 5th by wayne l winston chapter 03

... Objective: To learn methods in StatTools for breaking down baseball salaries by various categorical variables  Solution: Data set contains the same 2011 baseball data examined previously, as well as ... in whole or in part Example 3.2: Baseball Salaries 2011 Extra.xlsx  (slide of 2) Create side -by- side boxplots, by selecting Box-Whisker Plot from the Summary Graphs dropdown list and filling ...  You will occasionally see data in unstacked format, when there are two “short” variables, such as Male Salary and Female Salary  StatTools is capable of dealing with either format and can...

Business analytics data analysis and decision making 5th by wayne l winston chapter 04

... particularly in games of chance, can be calculated by using an equally likely argument  However, many other probabilities, especially those in business situations, cannot be calculated by equally likely ... to Simulation (slide of 2)  Random numbers generated with Excel’s RAND function are said to be uniformly distributed between and because all decimal values between and are equally likely  These ... publicly accessible website, in whole or in part Probability Distribution of a Single Random Variable (slide of 3)  A cumulative probability is the probability that the random variable is less...

Business analytics data analysis and decision making 5th by wayne l winston chapter 05

... mean  Almost all fall within three standard deviations of the mean  For these rules to hold with real data, the distribution of the data must be at least approximately symmetric and bell-shaped ... duplicated, or posted to a publicly accessible website, in whole or in part Example 5.12: Basketball Simulation.xlsx  Objective: To formulate a nonbinomial model of basketball shooting, and ... (for standardized) applies only to N(0, 1) calculations, whereas NORMDIST applies to any normal distribution  Percentile calculations that take a probability and return a value are often called...

Business analytics data analysis and decision making 5th by wayne l winston chapter 06

... particularly useful for more complex decision problems  It clearly shows the sequence of events (decisions and outcomes), as well as probabilities and monetary values © 2015 Cengage Learning All ... duplicated, or posted to a publicly accessible website, in whole or in part Sensitivity Analysis  It is important, especially in real-world business problems, to accompany any decision analysis ... all tests on drug-free athletes yield false positives, and 7% of all tests on drug users yield false negatives  Let D and ND denote that a randomly chosen athlete is or is not a drug user, and...

Business analytics data analysis and decision making 5th by wayne l winston chapter 07

... customer was billed), and Amount (of the bill)  Generate 25 random samples of size 15 each from the small customers only, calculate the average amount owed in each random sample, and construct ... example  Then a simple random sample of clusters can be chosen  Once the clusters are selected, it is typical to sample all of the population members in each selected cluster © 2015 Cengage Learning ... duplicated, or posted to a publicly accessible website, in whole or in part Example 7.5: Wheel of Fortune Simulation.xlsx (slide of 2)  Objective: To illustrate the central limit theorem by...

Business analytics data analysis and decision making 5th by wayne l winston chapter 08

... example, two assumptions lead to the confidence interval:  First, you might question whether the sample is really a random sample It is likely a convenience sample, not really a random sample ... are robust to violations of normality, and the normal population assumption is less crucial for larger sample sizes because of the central limit theorem © 2015 Cengage Learning All Rights Reserved ... multiplied by N, and the standard error of this point estimate is the standard error of the sample mean multiplied by N  As a result, a confidence interval for T can be formed with the following...

Business analytics data analysis and decision making 5th by wayne l winston chapter 09

... the null hypothesis, specifically, the borderline value between the null and alternative hypotheses  This value is usually labeled μ0  To run the test, referred to as the t test for a population ... Example 9.7: Testing Normality.xlsx (slide of 5)  To run the Lilliefors test for the Width variable in Example 9.7, select Lilliefors Test from the StatTools Normality Tests dropdown list  ... accessible website, in whole or in part Example 9.7: Testing Normality.xlsx (slide of 5)  A popular, but informal, test of normality is the quantile-quantile (QQ) plot  It is basically a scatterplot...

Business analytics data analysis and decision making 5th by wayne l winston chapter 10

... the multiplicative learning model is reasonable by creating a scatterplot of Log(Time) versus Log(Batch) The multiplicative model implies that it should be approximately linear  The relationship ... several, usually equally spaced, points in time  Time series variables are usually related to their own past values—a property called autocorrelation—which adds complications to the analysis ... several explanatory variables, it is called multiple regression  Regression can be linear (straight-line relationships) or nonlinear (curved relationships)  Many nonlinear relationships can be linearized...

Business analytics data analysis and decision making 5th by wayne l winston chapter 11

... copied or duplicated, or posted to a publicly accessible website, in whole or in part Example 11. 2: Heights Simulation.xlsx (slide of 2)  Objective: To illustrate the problem of multicollinearity ... much larger or smaller than predicted by the regression line, and its residual is abnormally large in magnitude  An example of this type of outlier is shown below © 2015 Cengage Learning All Rights ... for all such values  For any values of the explanatory variables, the dependent variable is normally distributed  The errors are probabilistically independent © 2015 Cengage Learning All Rights...

Business analytics data analysis and decision making 5th by wayne l winston chapter 12

... sometimes substantially  The length of a seasonal cycle is generally one year, while the length of a business cycle is generally longer than one year and its actual length is difficult to predict ... quarters and a Log(Sales) variable  Then use multiple regression, with Log(Sales) as the dependent variable, and Time, Q1, Q2, and Q3 as the explanatory variables © 2015 Cengage Learning All Rights ... related to their own past values  In positive autocorrelation, large observations tend to follow large observations, and small observations tend to follow small observations  The autocorrelation...

Business analytics data analysis and decision making 5th by wayne l winston chapter 13

... cell, contains the value of the objective Solver systematically varies the values in the changing cells to optimize the value in the objective cell The cell must be linked to the changing cells ... problem algebraically  Model it in Excel  Find its optimal solution with Solver  Solve it graphically  Ask a number of what-if questions about the completed model © 2015 Cengage Learning All Rights ... model: Relate all variables with appropriate cell formulas Optimize—systematically choose the values of the decision variables that make the objective as large (for maximization) or small (for...

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