Pub Date : 1900-01-01DOI: 10.1079/9781845932756.0251
A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts
Abstract This chapter introduces a technique called analysis of variance, which enables to compare the equality of two or more population means. Analysis of variance, often referred to by the acronym ANOVA, is one of the most powerful and frequently used techniques in statistics. It is used to analyse data obtained through both experimental designs and sampling designs. The application of this technique is exemplified by studying the effects of three different fertilizers on the height growth of Douglas fir seedlings.
{"title":"Analysis of variance: testing differences between several means.","authors":"A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts","doi":"10.1079/9781845932756.0251","DOIUrl":"https://doi.org/10.1079/9781845932756.0251","url":null,"abstract":"Abstract\u0000 This chapter introduces a technique called analysis of variance, which enables to compare the equality of two or more population means. Analysis of variance, often referred to by the acronym ANOVA, is one of the most powerful and frequently used techniques in statistics. It is used to analyse data obtained through both experimental designs and sampling designs. The application of this technique is exemplified by studying the effects of three different fertilizers on the height growth of Douglas fir seedlings.","PeriodicalId":413890,"journal":{"name":"Introductory probability and statistics: applications for forestry and natural sciences","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116669636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1079/9781789243307.0035
A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts
Abstract This chapter introduces the basic theories of probability that are required to appreciate and understand many of the concepts of statistical inference as applied in research in forestry.
本章介绍概率论的基本理论，这些理论是理解和理解林业研究中应用的统计推断的许多概念所必需的。
{"title":"Probability: the foundation of statistics.","authors":"A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts","doi":"10.1079/9781789243307.0035","DOIUrl":"https://doi.org/10.1079/9781789243307.0035","url":null,"abstract":"Abstract\u0000 This chapter introduces the basic theories of probability that are required to appreciate and understand many of the concepts of statistical inference as applied in research in forestry.","PeriodicalId":413890,"journal":{"name":"Introductory probability and statistics: applications for forestry and natural sciences","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123421874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1079/9781845932756.0061
A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts
Abstract The main objectives of this chapter are to show how outcomes of random experiments can be described in real (numerical) terms and how probabilities can be assigned to these real numbers. Numerical descriptions of outcomes and their respective probabilities form what are known as probability distributions or probability density functions. These distributions can be used to compute the means and the variances of the random variables that they describe. All of these tools are useful in helping to provide further information for describing populations, e.g., forest tree seedlings.
{"title":"Random variables and probability distributions: outcomes of random experiments.","authors":"A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts","doi":"10.1079/9781845932756.0061","DOIUrl":"https://doi.org/10.1079/9781845932756.0061","url":null,"abstract":"Abstract\u0000 The main objectives of this chapter are to show how outcomes of random experiments can be described in real (numerical) terms and how probabilities can be assigned to these real numbers. Numerical descriptions of outcomes and their respective probabilities form what are known as probability distributions or probability density functions. These distributions can be used to compute the means and the variances of the random variables that they describe. All of these tools are useful in helping to provide further information for describing populations, e.g., forest tree seedlings.","PeriodicalId":413890,"journal":{"name":"Introductory probability and statistics: applications for forestry and natural sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130011297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1079/9781845932756.0217
A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts
Abstract This chapter examines statistical procedures to derive mathematical relationships between sampled tree volume and sampled dbh, tree height and/or basal area. The tools that will be used to derive these relationships are regression and correlation analyses.
{"title":"Regression and correlation: relationships between variables.","authors":"A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts","doi":"10.1079/9781845932756.0217","DOIUrl":"https://doi.org/10.1079/9781845932756.0217","url":null,"abstract":"Abstract\u0000 This chapter examines statistical procedures to derive mathematical relationships between sampled tree volume and sampled dbh, tree height and/or basal area. The tools that will be used to derive these relationships are regression and correlation analyses.","PeriodicalId":413890,"journal":{"name":"Introductory probability and statistics: applications for forestry and natural sciences","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125626968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1079/9781845932756.0147
A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts
Abstract This chapter discusses statistical estimation used in forestry, which can be classified as either point estimation or interval estimation. A point estimate is a single numeric value calculated from the information in a sample. An interval estimate yields two numeric values, between which we can reliably expect to find the target parameter.
{"title":"Estimation: determining the value of population parameters.","authors":"A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts","doi":"10.1079/9781845932756.0147","DOIUrl":"https://doi.org/10.1079/9781845932756.0147","url":null,"abstract":"Abstract\u0000 This chapter discusses statistical estimation used in forestry, which can be classified as either point estimation or interval estimation. A point estimate is a single numeric value calculated from the information in a sample. An interval estimate yields two numeric values, between which we can reliably expect to find the target parameter.","PeriodicalId":413890,"journal":{"name":"Introductory probability and statistics: applications for forestry and natural sciences","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128989402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1079/9781845932756.0277
A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts
Abstract This chapter briefly discusses completely randomized, randomized complete block and latin square designs. The chapter also discusses factorial experiments, which use these designs in the allocation of treatments. The sampling designs and experimental designs discussed here are a few of the more commonly used methods in forestry applications. The interested reader is directed to advanced texts on the subjects, of which there are many, for more comprehensive overviews.
{"title":"Sampling methods and design of experiments: collecting data.","authors":"A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts","doi":"10.1079/9781845932756.0277","DOIUrl":"https://doi.org/10.1079/9781845932756.0277","url":null,"abstract":"Abstract\u0000 This chapter briefly discusses completely randomized, randomized complete block and latin square designs. The chapter also discusses factorial experiments, which use these designs in the allocation of treatments. The sampling designs and experimental designs discussed here are a few of the more commonly used methods in forestry applications. The interested reader is directed to advanced texts on the subjects, of which there are many, for more comprehensive overviews.","PeriodicalId":413890,"journal":{"name":"Introductory probability and statistics: applications for forestry and natural sciences","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114752872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1079/9781845932756.0287
A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts
Abstract This chapter introduces several other commonly used non-parametric tests: the sign test, the Wilcoxon signed rank test, the Wilcoxon rank sum test, the Kruskal-Wallis test, the runs test, and Spearman's rank correlation test. Non-parametric tests do not require knowledge or estimates of the parameter values. They can be performed without uniquely identifying the distribution, or its parameters. The use of these non-parametric tests in forestry applications are given in this chapter.
{"title":"Non-parametric tests: testing when distributions are unknown.","authors":"A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts","doi":"10.1079/9781845932756.0287","DOIUrl":"https://doi.org/10.1079/9781845932756.0287","url":null,"abstract":"Abstract\u0000 This chapter introduces several other commonly used non-parametric tests: the sign test, the Wilcoxon signed rank test, the Wilcoxon rank sum test, the Kruskal-Wallis test, the runs test, and Spearman's rank correlation test. Non-parametric tests do not require knowledge or estimates of the parameter values. They can be performed without uniquely identifying the distribution, or its parameters. The use of these non-parametric tests in forestry applications are given in this chapter.","PeriodicalId":413890,"journal":{"name":"Introductory probability and statistics: applications for forestry and natural sciences","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121440741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1079/9781845932756.0009
A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts
Abstract To adequately monitor and manage natural resources, such as forests and rangelands, many very large data sets are compiled. In this light, the chapter explores the tools used to make data sets more comprehensible. By organizing variables into tables, charts and graphs, and by calculating numbers that best describe the characteristics of a variable of interest, managers can quickly get information about the natural resources for which they are responsible.
{"title":"Descriptive statistics: making sense of data.","authors":"A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts","doi":"10.1079/9781845932756.0009","DOIUrl":"https://doi.org/10.1079/9781845932756.0009","url":null,"abstract":"Abstract\u0000 To adequately monitor and manage natural resources, such as forests and rangelands, many very large data sets are compiled. In this light, the chapter explores the tools used to make data sets more comprehensible. By organizing variables into tables, charts and graphs, and by calculating numbers that best describe the characteristics of a variable of interest, managers can quickly get information about the natural resources for which they are responsible.","PeriodicalId":413890,"journal":{"name":"Introductory probability and statistics: applications for forestry and natural sciences","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114589625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1079/9781845932756.0201
A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts
Abstract This chapter introduces tests concerning distributions of one or more populations by using data from a large sawmill. The goodness-of-fit test is used to determine whether a population follows a specified theoretical distribution, and the test for independence (or a contingency table) is used to compare two or more distributions.
{"title":"Goodness-of-fit and test for independence: testing distributions.","authors":"A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts","doi":"10.1079/9781845932756.0201","DOIUrl":"https://doi.org/10.1079/9781845932756.0201","url":null,"abstract":"Abstract\u0000 This chapter introduces tests concerning distributions of one or more populations by using data from a large sawmill. The goodness-of-fit test is used to determine whether a population follows a specified theoretical distribution, and the test for independence (or a contingency table) is used to compare two or more distributions.","PeriodicalId":413890,"journal":{"name":"Introductory probability and statistics: applications for forestry and natural sciences","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127867767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1079/9781845932756.0093
A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts
Abstract This chapter discusses normal distribution along with two other continuous distributions: the uniform distribution and the exponential distribution. The use of these techniques are exhibited by analysing data in managing forest plantations and products.
{"title":"Continuous distributions and the normal distribution: describing data that are measured.","authors":"A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts","doi":"10.1079/9781845932756.0093","DOIUrl":"https://doi.org/10.1079/9781845932756.0093","url":null,"abstract":"Abstract\u0000 This chapter discusses normal distribution along with two other continuous distributions: the uniform distribution and the exponential distribution. The use of these techniques are exhibited by analysing data in managing forest plantations and products.","PeriodicalId":413890,"journal":{"name":"Introductory probability and statistics: applications for forestry and natural sciences","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116636151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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