Linear Relationship and F Tests Its Significance 146ĥ.7 Assess Residuals to Learn Whether Assumptions Are Met 149ĥ.8 Recalibrate to Update a Valid Model 151ĥ.9 Present Regression Results in Concise Format 153ĥ.10 Assumptions We Make When We Use Linear Regression 154ĥ.11 Correlation Reflects Linear Association 154ĥ.12 Correlation Coefficients Are Key Components of Regression Slopesĥ.13 Correlation Complements Regression 158ĥ.14 Linear Regression Is Doubly Useful 158Įxcel 5.1 Build a Simple Linear Regression Model 159Įxcel 5.3 Construct Prediction Intervals to ValidateĮxcel 5.4 Recalibrate and Present Fit and Forecast in a ScatterplotĮxcel 5.5 Find Correlations etween Variable Pairs
The Line Relating an Independent Variable to Performanceĥ.2 Hide the Two Most Recent Datapoints to Validate a Time Series Modelĥ.4 The Regression Standard Error Reflects Model Precisionĥ.5 Prediction Intervals Estimate Average Population Responseĥ.6 Rsquare Summarizes Strength of the Hypothesized Simple Regression for Long Range Forecasts 137ĥ.1 The Simple Linear Regression Equation Describes 147Ħ.8 Printing Only Part of a Spreadsheet Instead of the Entire Spreadsheet 147Ħ.8.1 Printing Only the Table and the Chart on a Separate Ħ.8.2 Printing Only the Chart on a Separate Ħ.8.3 Printing Only the SUMMARY OUTPUT of the Regression Analysis on a Separate PageĮ stats- Excel 2016 for Biological and Life Sciences Statistics - A Guide to Solving Practical Problems (2016).pdf (downloaded)Ħ Correlation and Simple Linear Regression 109ħ Multiple Correlation and Multiple Regressionħ.2 Finding the Multiple Correlation and the Multiple Regression Equationħ.3 Using the Regression Equation to Predict FRUIT PRODUCEDħ.4 Using Excel to Create a Correlation Matrix in Multiple Regression 160Į stats- Excel 2016 for Business Statistics_ A Guide to Solving Practical Problems-Springer International Publishing
143Ħ.6 Adding the Regression Equation to the Chart 144Ħ.7 How to Recognize Negative Correlations in the SUMMARY OUTPUT Table. 143Ħ.5.4 Using the Regression Line to Predict the y-Value for a Given x-Value.
115Ħ.1.2 Understanding the Nine Steps for Computing a Correlation, r 116Ħ.2 Using Excel to Compute a Correlation Between Two Variables 118Ħ.3 Creating a Chart and Drawing the Regression Line onto the Chart 123Ħ.3.1 Using Excel to Create a Chart and the Regression Line Through the Data PointsĦ.4 Printing a Spreadsheet So That the Table and Chart Fit onto One Ħ.5.1 Installing the Data Analysis ToolPak into Excel 136Ħ.5.2 Using Excel to Find the SUMMARY OUTPUT of Regression 139Ħ.5.3 Finding the Equation for the Regression Line. Quirk, Julie Palmer-Schuyler (auth.)-Ħ Correlation and Simple Linear RegressionĦ.1.1 Understanding the Formula for Computing a Correlation. Springer International Publishing.pdf (2016) (downloaded) 265pg Thomas J. ġ4 Nonlinear Explanatory Multiple Regression ModelsĮ stats- Excel 2013 for Human Resource Management Statistics_ A Guide to Solving Practical Problems
pdf (2007) 478pg (downloaded) Brian_D._Bissett_Ĥ.2 Creating and Utilizing VBA Functions in Code 113Ĥ.4 Adding a Function to a Worksheet Cell Using VBA CodeĤ.5 Creating Additional Built-in Functions for ExcelĤ.6 Dynamic Formatting of Worksheets Using FunctionsĤ.7 Applying Dynamic Formatting Using VBAĤ.8 Using the Macro Recorder to Capture a ProcessĤ.9 Creating a Linear Regression Tool Using the VBA Analysis Toolpak 138Ĥ.10 Creating a Polynomial Regression Tool Using the VBA Analysis Toolpak 147Į stats- Business Statistics for Competitive Advantage with Excel 2016 _ Basics, Model Building, Simulation and Cases-Springer International Publishing.pdf 482pg (2016) Cynthia Fraser (auth.)- (downloaded)ġ Statistics for Decision Making and Competitive Advantageģ Hypothesis Tests, Confidence Intervals to Infer Population Characteristics and DifferencesĤ Simulation to Infer Future Performance Levels Given Assumptionsĥ Simple Regression for Long Range ForecastsĦ Consolidating Multiple Naïve Forecasts with Monte Carloħ Presenting Statistical Analysis Results to ManagementĨ Finance Application: Portfolio Analysis with a Market Index as a Leading Indicator in Simple Linear Regressionĩ Association Between Two Categorical Variables: Contingency Analysis with Chi Squareġ2 Model Building and Forecasting with Multicollinear Time Seriesġ3 Nonlinear Multiple Regression Models.
This free book download has several sections on linear regressionĢ003- Automated Data Analysis Using Excel 2003.