WebAssign for Dielman’s Business Statistics 4th Edition by Terry Dielman – Ebook PDF Instant Download/Delivery: 053446548X , 978-0534465483
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Product details:
ISBN 10: 053446548X
ISBN 13: 978-0534465483
Author: Terry Dielman
APPLIED REGRESSION ANALYSIS applies regression to real data and examples while employing commercial statistical and spreadsheet software. Covering the core regression topics as well as optional topics including ANOVA, Time Series Forecasting, and Discriminant Analysis, the text emphasizes the importance of understanding the assumptions of the regression model, knowing how to validate a selected model for these assumptions, knowing when and how regression might be useful in a business setting, and understanding and interpreting output from statistical packages and spreadsheets.
WebAssign for Dielman’s Business Statistics 4th Table of contents:
Chapter 1: An Introduction to Regression Analysis
Chapter 2: Review of Basic Statistical Concepts
2.1 Introduction
2.2 Descriptive Statistics
2.3 Discrete Random Variables and Probability Distributions
2.4 The Normal Distribution
2.5 Populations, Samples, and Sampling Distributions
2.6 Estimating a Population Mean
2.7 Hypothesis Tests about a Population Mean
2.8 Estimating the Difference between Two Population Means
2.9 Hypothesis Tests about the Difference between Two Population Means
Using the Computer
Chapter 3: Simple Regression Analysis
3.1 Using Simple Regression to Describe a Linear Relationship
3.2 Examples of Regression as a Descriptive Technique
3.3 Inferences from a Simple Regression Analysis
3.4 Assessing the Fit of the Regression Line
3.5 Prediction or Forecasting with a Simple Linear Regression Equation
3.6 Fitting a Linear Trend to Time-Series Data
3.7 Some Cautions in Interpreting Regression Results
Using the Computer
Chapter 4: Multiple Regression Analysis
4.1 Using Multiple Regression to Describe a Linear Relationship
4.2 Inferences from a Multiple Regression Analysis
4.3 Assessing the Fit of the Regression Line
4.4 Comparing Two Regression Models
4.5 Prediction with a Multiple Regression Equation
4.6 Multicollinearity: A Potential Problem in Multiple Regression
4.7 Lagged Variables as Explanatory Variables in Time-Series Regression
Using the Computer
Chapter 5: Fitting Curves to Data
5.1 Introduction
5.2 Fitting Curvilinear Relationships
Using the Computer
Chapter 6: Assessing the Assumptions of the Regression Model
6.1 Introduction
6.2 Assumptions of the Multiple Linear Regression Model
6.3 The Regression Residuals
6.4 Assessing the Assumption That the Relationship Is Linear
6.5 Assessing the Assumption That the Variance around the Regression Line Is Constant
6.6 Assessing the Assumption That the Disturbances Are Normally Distributed
6.7 Influential Observations
6.8 Assessing the Assumption That the Disturbances Are Independent
Using the Computer
Chapter 7: Using Indicator and Interaction Variables
7.1 Using and Interpreting Indicator Variables
7.2 Interaction Variables
7.3 Seasonal Effects in Time-Series Regression
Using the Computer
Chapter 8: Variable Selection
8.1 Introduction
8.2 All Possible Regressions
8.3 Other Variable Selection Techniques
8.4 Which Variable Selection Procedure Is Best?
Using the Computer
Chapter 9: An Introduction to Analysis of Variance
9.1 One-Way Analysis of Variance
9.2 Analysis of Variance Using a Randomized Block Design
9.3 Two-Way Analysis of Variance
9.4 Analysis of Covariance
Using the Computer
Chapter 10: Qualitative Dependent Variables: An Introduction to Discriminant Analysis and Logistic R
10.1 Introduction
10.2 Discriminant Analysis
10.3 Logistic Regression
Using the Computer
Chapter 11: Forecasting Methods for Time-Series Data
11.1 Introduction
11.2 Naive Forecasts
11.3 Measuring Forecast Accuracy
11.4 Moving Averages
11.5 Exponential Smoothing
11.6 Decomposition
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Tags: Terry Dielman, Dielman’s Business