TAILIEUCHUNG - Lecture Business statistics in practice (7/e): Chapter 14 - Bowerman, O'Connell, Murphree

Chapter 14 - Simple linear regression analysis. After mastering the material in this chapter, you will be able to: Explain the simple linear regression model, find the least squares point estimates of the slope and y-intercept, describe the assumptions behind simple linear regression and calculate the standard error,. | Simple Linear Regression Analysis Chapter 14 Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Simple Linear Regression Analysis The Simple Linear Regression Model and the Least Square Point Estimates Model Assumptions and the Standard Error Testing the Significance of Slope and y-Intercept Confidence and Prediction Intervals Simple Coefficients of Determination and Correlation 14- Simple Linear Regression Analysis Continued Testing the Significance of the Population Correlation Coefficient An F Test for the Model The QHIC Case Residual Analysis Some Shortcut Formulas (Optional) 14- The Simple Linear Regression Model and the Least Squares Point Estimates The dependent (or response) variable is the variable we wish to understand or predict The independent (or predictor) variable is the variable we will use to understand or predict the dependent variable Regression .

• Quản trị kinh doanh
• Business Statistics
• Business statistics in practice
• Descriptive statistics
• Simple linear regression analysis
• Simple linear regression model
• Model assumptions
• 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
• Numerical methods
• Describing central tendency
• Measures of variation
• 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
• 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