TAILIEUCHUNG - Lecture Business statistics in practice (7/e): Chapter 3 - Bowerman, O'Connell, Murphree

Chapter 3 - Descriptive statistics: Numerical methods. After mastering the material in this chapter, you will be able to: Compute and interpret the mean, median, and mode; compute and interpret the range, variance, and standard deviation; use the Empirical Rule and Chebyshev's Theorem to describe variation;. | Chapter 3 Descriptive Statistics: Numerical Methods Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Descriptive Statistics Describing Central Tendency Measures of Variation Percentiles, Quartiles and Box-and-Whiskers Displays Covariance, Correlation, and the Least Square Line (Optional) Weighted Means and Grouped Data (Optional) The Geometric Mean (Optional) 3- Describing Central Tendency In addition to describing the shape of a distribution, want to describe the data set’s central tendency A measure of central tendency represents the center or middle of the data Population mean (μ) is average of the population measurements Population parameter: a number calculated from all the population measurements that describes some aspect of the population Sample statistic: a number calculated using the sample measurements that describes some aspect of the sample LO3-1: Compute and interpret the mean, median, and

• Quản trị kinh doanh
• Business Statistics
• Business statistics in practice
• Descriptive statistics
• Numerical methods
• Describing central tendency
• Measures of variation
• Lecture Business statistics
• Bài giảng Thống kê doanh nghiệp
• Descriptive statistics tabular
• Descriptive statistics graphical presentations
• Summarizing quantitative data
• Essentials of modern business statistics
• Modern business statistics
• Graphical presentations
• numerical measures
• Exploratory data analysis
• Interval estimation
• Hypothesis tests
• Simple linear regression
• Basic statistics
• Statistics for business
• Describing data
• Types of statistics
• Quantitative variables
• Ordinal level data
• Data sources
• Statistical inference
• Illustrate sampling
• Graphical methods
• Tabular methods
• Graphically summarizing qualitative data
• Sample spaces
• Elementary probability rules
• Compute conditional probabilities
• Discrete random variables
• Discrete probability distributions
• Binomial distribution
• Continuous random variables
• Continuous probability distributions
• Uniform distribution
• Sampling distributions
• Random sampling
• Stratified random
• Confidence intervals
• Sample size determination
• Hypothesis testing
• Alternative hypotheses
• Sampling distribution
• Equal variance
• Statistical inferences
• Population variances
• Experimental design
• Analysis of variance
• Randomized block design
• Chi square tests
• Chi square goodness of fit tests
• Multinomial experiment
• Simple linear regression analysis
• Simple linear regression model
• Model assumptions
• Multiple regression
• Least squares point estimate
• Multiple regression model
• Times series forecasting
• Index numbers
• Forecast error comparisons
• Statistical process control
• Causes of variation
• Control charts
• Nonparametric methods
• Hypothesis test
• Decision theory
• Utility theory
• Posterior probabilities