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58 results for "regression analysis"
Multiple Linear Regression Analysis By Homework Help Classof1
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In a study of housing demand, the county assessor is interested in developing a regression model to estimate the market value (i.e., selling price) of residential property within her jurisdiction. In... More > addition, the assessor feels that there may be other important variables that will affect the market value of the house such as the size of the house, number of rooms, age and whether the house has an attached garage. These data for 15 randomly selected houses arc shown in the attached table.< Less
Linear Regression By Homework Help Classof1
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Linear regression was the first type o0f regression analysis to be studied rigorously and to be used extensively in practical applications. This is so because the model which depends linearly on... More > their unknown parameters are much easier to fit than the models are non linearly related to their parameters and because their statistical properties of the resulting estimators are much easier to determine.< Less
Simple vs. Multiple Regression By Homework Help Classof1
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The difference between simple and multiple regression is similar to the difference between one way and factorial ANOVA. Like one-way ANOVA, simple regression analysis involves a single independent,... More > or predictor variable and a single dependent, or outcome variable. This is the same number of variables used in a simple correlation analysis.< Less
The Linear Regressions Models By Homework Help Classof1
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Linear regression was the first type o0f regression analysis to be studied rigorously and to be used extensively in practical applications. This is so because the model which depends linearly on... More > their unknown parameters are much easier to fit than the models are non linearly related to their parameters and because their statistical properties of the resulting estimators are much easier to determine.< Less
Simple Linear Regression Model By Homework Help Classof1
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This article considers the relationship between two variables in two ways: (1) by using regression analysis and (2) by computing the correlation coefficient. By using the regression model, we can... More > evaluate the magnitude of change in one variable due to a certain change in another variable. For example, an economist can estimate the amount of change in food expenditure due to a certain change in the income of a household by using the regression model.< Less
Using Linear Regression to Calculate Complex Factors By Homework Help Classof1
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We have looked at break-even calculations, scatter plots for estimating demand information, and trends in revenue so far. In each of these, we simplified the analysis to a straight line. While this... More > is useful, sometimes these data are more complex. They may be a curved line, or instead of only one item for the slope, such as variable costs, there may be several factors involved in the estimate. So demand, for example, may be based on price, convenience, or quality. How does a linear regression allow us to better estimate trends, costs, and other factors in complex situations?< Less
Teach Yourself Cluster Analysis, Conjoint Analysis, and Econometrics Techniques By Hui Liew
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This e-book will address such classification and econometrics techniques as cluster analysis, conjoint analysis, seemingly unrelated regression, and simultaneous equations modeling. Each technique... More > will be explained in lay man’s terms, difficult concepts using illustrative examples that are easily understood. Mathematical prerequisite is generally low; the author assumes her reader has some familiarity with descriptive statistics and multivariate regression. After reading the e-book, the reader will be able to understand each technique and apply it to social science related research without having to know the meaning of Greek symbols and equations. In this e-book, syntax and output for each technique will be discussed and the author will provide a clear explanation of how to interpret the output. Readers will know how to modify the syntax provided in the e-book and apply them to their own programs to use. Programming syntax in SPSS and R are also provided.< Less
Exploratory Data Analysis: An Introduction to Data Analysis Using SAS By Patricia Cerrito
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This is an introductory text on how to investigate datasets. It is intended to be a practical text for those who need to research large datasets. Therefore, it does not follow the standard contents... More > for more typical introductory statistics textbooks. When you complete the material, you will be able to work with your data using data visualization and regression in order to make sense of it, and to use your findings to make decisions. The book makes use of the statistical software, SAS, and its menu system SAS Enterprise Guide. This can be used as a stand alone text, or as a supplementary text to a more standard course. There are some datasets to accompany this text. ID# 1640751, Data for Exploratory Data Analysis.< Less
Teach Yourself Cluster Analysis, Conjoint Analysis, and Econometrics Techniques By Hui Liew
Paperback: $24.99
Ships in 3-5 business days
This book will address such classification and econometrics techniques as cluster analysis, conjoint analysis, seemingly unrelated regression, and simultaneous equations modeling. The purpose and... More > rationale for using each technique will be explained in lay man's terms, using illustrative concepts that are easily understood. Mathematical prerequisites are generally low; the author assumes her reader has some familiarity with descriptive statistics and multivariate analysis. After reading the book, the reader will be able to understand and apply each technique without having to know the meaning of Greek symbols and equations. The syntax and output for each technique will be discussed and the author will provide a clear explanation of how to interpret the output. Readers will know how to modify the syntax provided in the book and apply them to their own programs to use. Programming syntax in SPSS and R are provided.< Less
Exploring Yellowstone National Park Through Statistical Analysis By Charles P. Kost II
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Bring the wonder and excitement of Yellowstone National Park to your statistics classroom with Exploring Yellowstone National Park Through Statistical Analysis. This set of eight activties takes... More > students beyond the mean, median, and mode to predict wildlife populations, classify thermal features, and determine predictability of Yellowstone’s geysers. Statistical analysis techniques include regression, hypothesis testing, percentages, displays of data, data collection, and more.< Less