book

Business Statistics For Dummies, 2nd Edition

by Alan Anderson

January 2024

Beginner

400 pages

9h 3m

English

For Dummies

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Copyright
About This BookFoolish AssumptionsIcons Used in This BookBeyond the BookWhere to Go from Here
Representing the Key Properties of DataProbability: The Foundation of All Statistical AnalysisUsing Sampling Techniques and Sampling DistributionsStatistical Inference: Drawing Conclusions from Data
Analyzing the Distribution of Data by Class or CategoryHistograms: Getting a Picture of Frequency DistributionsChecking Out Other Useful Graphs
Looking at Methods for Finding the MeanGetting to the Middle of Things: The Median of a Data SetDetermining the Relationship Between the Mean and MedianDiscovering the Mode: The Most Frequently Repeated ElementComputing the Mean, Median, and Mode with the TI-84 Plus Calculator
Determining Variance and Standard DeviationFinding the Relative Position of DataMeasuring Relative VariationComputing Measures of Dispersion with the TI-84 Plus Calculator
Understanding Covariance and CorrelationInterpreting the Correlation CoefficientComputing Covariance and Correlation with the TI-84 Plus Calculator

Working with SetsBetting on Uncertain OutcomesLooking at Types of ProbabilitiesFollowing the Rules: Computing Probabilities
Defining the Role of the Random VariableAssigning Probabilities to a Random VariableCharacterizing a Probability Distribution with Moments
Looking at Two Possibilities with the Binomial DistributionKeeping the Time: The Poisson DistributionComputing Binomial and Poisson Probabilities with the TI-84 Plus Calculator
Comparing Discrete and Continuous DistributionsUnderstanding the Normal DistributionComputing Probabilities for the Normal Distribution with the TI-84 Plus Calculator
Sampling Techniques: Choosing Data from a PopulationSampling DistributionsThe Central Limit Theorem
Almost Normal: The Student’s t-DistributionGraphing the t-DistributionProbabilities and the t-TablePoint Estimates vs. Interval EstimatesEstimating Confidence Intervals for the Population MeanComputing Confidence Intervals for the Population Mean with the TI-84 Plus Calculator
Applying the Key Steps in Hypothesis Testing for a Single Population MeanTesting Hypotheses About Two Population MeansTesting Hypotheses about Population Means with the TI-84 Plus Calculator
Staying Positive with the Chi-Square DistributionTesting Hypotheses about the Population VariancePracticing the Goodness of Fit TestsConducing a Goodness of Fit Test with the TI-84 Plus Calculator
Getting to Know the F-DistributionTesting Hypotheses about the Equality of Two Population VariancesTesting Hypotheses about Two Population Variances with the TI-84 Plus Calculator
The Fundamental Assumption: Variables Have a Linear RelationshipDefining the Population Regression EquationEstimating the Population Regression EquationTesting the Estimated Regression EquationUsing Statistical SoftwareAssumptions of Simple Linear RegressionConducting Simple Regression Analysis with the TI-84 Plus Calculator
Implementing Excel FunctionsChecking Out Excel’s Key Statistical FunctionsGoing Deeper with the Analysis ToolPak
Designing Misleading GraphsDrawing the Wrong Conclusion from a Confidence IntervalMisinterpreting the Results of a Hypothesis TestPlacing Too Much Confidence in the Coefficient of Determination ()Assuming NormalityThinking Correlation Implies CausalityDrawing Conclusions from a Regression Equation When the Data Do Not Follow the AssumptionsUsing Regression Analysis to Make Predictions About Values Outside the Range of Sample DataPlacing Too Much Confidence in ForecastsUsing the Wrong Distribution
Summary Measures of a Population or a SampleProbabilityDiscrete Probability DistributionsContinuous Probability DistributionsSampling DistributionsConfidence Intervals for the Population MeanTesting Hypotheses about Population MeansTesting Hypotheses about Population VariancesUsing Regression Analysis

Content preview from Business Statistics For Dummies, 2nd Edition

Chapter 15

Simple Regression Analysis

IN THIS CHAPTER

Understanding the assumptions underlying regression analysis

Implementing the simple regression model

Interpreting the regression results

Regression analysis is a statistical methodology that helps you develop a model of the response of a dependent variable (Y) to a change in an independent variable (X). The two types of regression analysis are simple regression analysis (which I discuss in this chapter) and multiple regression. Simple regression analysis is used for cases where there is only one independent variable while multiple regression is used for cases with two or more independent variables.

For example, suppose that a researcher is interested in analyzing the relationship between the annual returns to the Standard & Poor’s 500 (S&P 500) and the annual returns to Apple stock.

The Standard and Poor’s 500 (S&P 500) is a broad-based stock market index; it contains the 500 largest U.S. stocks, based on market capitalization. (The market capitalization of a stock equals the market price of the stock times the number of outstanding ...