Title | The manga guide to regression analysis |
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File Size | 13.9 MB |

Total Pages | 235 |

Preface Prologue More Tea? 1 A Refreshing Glass of Math Building a Foundation Inverse Functions exponents and logarithms Rules for exponents and logarithms Differential calculus Matrices Adding Matrices Multiplying Matrices The Rules of Matrix Multiplication Identity and Inverse Matrices Statistical Data Types Hypothesis Testing Measuring Variation Sum of Squared Deviations Variance Standard Deviation Probability Density Functions Normal Distributions Chi-Squared Distributions Probability Density Distribution Tables F Distributions 2 Simple Regression Analysis First Steps Plotting the Data The Regression Equation General Regression Analysis Procedure step 1: Draw a scatter plot of the independent variable versus the dependent variable. If the dots line up, the variables may be correlated. Step 2: Calculate the regression equation. Step 3: Calculate the correlation coefficient (R ) and assess our population and assumptions. Samples and Populations Assumptions of Normality Step 4: Conduct the analysis of variance. Step 5: Calculate the confidence intervals. step 6: Make a prediction! Which Steps Are Necessary? Standardized Residual Interpolation and Extrapolation Autocorrelation Nonlinear Regression Transforming Nonlinear Equations into Linear Equations 3 Multiple Regression Analysis Predicting with Many Variables The multiple regression equation Multiple Regression Analysis Step-by-Step step 1: draw a scatter plot of each predictor variable and the outcome variable to see if they appear to be related. step 2: calculate the multiple regression equation. Step 3: Examine the accuracy of the multiple regression equation. The Trouble with R2 Adjusted R2 hypothesis testing with multiple regression Step 4: Conduct the Analysis of Variance (ANOVA) Test. Finding S11 and S22 Step 5: calculate Confidence intervals for the population. step 6: Make A Prediction! choosing the best combination of Predictor variables Assessing Populations with Multiple Regression Analysis Standardized Residuals Mahalanobis Distance Step 1 Step 2 Step 3 Using Categorical Data in Multiple Regression analysis Multicollinearity determining the Relative Influence of Predictor Variables on the Outcome Variable 4 Logistic Regression Analysis The Final Lesson the maximum likelihood method Finding the maximum likelihood Using the Likelihood Function Choosing Predictor variables Logistic regression analysis in Action! Logistic Regression Analysis Step-by-Step Step 1: draw a scatter plot of the predictor variables and the outcome variable to see whether they appear to be related. Step 2: calculate the logistic regression equation. Step 3: assess the accuracy of the equation Step 4: conduct the hypothesis tests. Step 5: Predict whether the Norns special will sell. Logistic Regression Analysis in the Real World Logit, Odds Ratio, and Relative Risk Logit Odds Ratio Adjusted Odds Ratio Hypothesis Testing with Odds Confidence Interval for an Odds Ratio Relative Risk A Regression Calculations with Excel Euler’s Number Power Natural Logarithm Matrix Multiplication Matrix Inversion Calculating a Chi-Squared Statistic from a p-Value Calculating a p-Value from a Chi-Squared Statistic Calculating an F Statistic from a p-Value Calculating a p-Value for an F Distribution Partial Regression Coefficient of a Multiple Regression Analysis Regression Coefficient of a Logistic Regression Equation Index _GoBack Blank Page

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