Pub Date : 2018-06-13DOI: 10.4324/9781315179803-23
F. Pyrczak, Deborah M. Oh
{"title":"Scattergram","authors":"F. Pyrczak, Deborah M. Oh","doi":"10.4324/9781315179803-23","DOIUrl":"https://doi.org/10.4324/9781315179803-23","url":null,"abstract":"","PeriodicalId":196141,"journal":{"name":"Making Sense of Statistics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133210166","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 : 2018-06-13DOI: 10.4324/9781315179803-38
F. Pyrczak, Deborah M. Oh
{"title":"Effect Size","authors":"F. Pyrczak, Deborah M. Oh","doi":"10.4324/9781315179803-38","DOIUrl":"https://doi.org/10.4324/9781315179803-38","url":null,"abstract":"","PeriodicalId":196141,"journal":{"name":"Making Sense of Statistics","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126119176","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 : 2018-06-13DOI: 10.4324/9781315179803-21
F. Pyrczak, Deborah M. Oh
{"title":"Correlation","authors":"F. Pyrczak, Deborah M. Oh","doi":"10.4324/9781315179803-21","DOIUrl":"https://doi.org/10.4324/9781315179803-21","url":null,"abstract":"","PeriodicalId":196141,"journal":{"name":"Making Sense of Statistics","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116658834","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}
Imagine that you are a psychologist, and you want to do a study to see whether eating breakfast will help kids focus. You think that the students who eat a healthy breakfast will do best on a math quiz, students who eat an unhealthy breakfast will perform in the middle and students who do not eat anything for breakfast will do the worst on a math quiz. So, how do you do your study? Where do you even begin? In research, one of the first things that you have to do is identify your variables, or factors that can change. For example, whether a person eats breakfast or not is a variable it varies from person to person and perhaps from day to day. A person can eat a healthy breakfast, eat an unhealthy breakfast or not eat breakfast at all. If eating breakfast did not vary, every single person would eat the exact same thing for breakfast every single morning. Likewise, performance on a math test is a variable because it varies from person to person. Susie might do great on a math quiz, while Jonas fails it. Or Susie might do well today but not as well tomorrow. Whatever the reason, scores on a math quiz change, and therefore, they are variables. So we know that our variables are eating breakfast and math performance. But how do we measure them? There are four major scales (or types) of measurement of variables: nominal, ordinal, interval and ratio. The scale of measurement depends on the variable itself. Let's look closer at each of the four scales and what types of variables fall into each category.
{"title":"Scales of Measurement","authors":"F. Pyrczak, Deborah M. Oh","doi":"10.4324/9781315179803-5","DOIUrl":"https://doi.org/10.4324/9781315179803-5","url":null,"abstract":"Imagine that you are a psychologist, and you want to do a study to see whether eating breakfast will help kids focus. You think that the students who eat a healthy breakfast will do best on a math quiz, students who eat an unhealthy breakfast will perform in the middle and students who do not eat anything for breakfast will do the worst on a math quiz. So, how do you do your study? Where do you even begin? In research, one of the first things that you have to do is identify your variables, or factors that can change. For example, whether a person eats breakfast or not is a variable it varies from person to person and perhaps from day to day. A person can eat a healthy breakfast, eat an unhealthy breakfast or not eat breakfast at all. If eating breakfast did not vary, every single person would eat the exact same thing for breakfast every single morning. Likewise, performance on a math test is a variable because it varies from person to person. Susie might do great on a math quiz, while Jonas fails it. Or Susie might do well today but not as well tomorrow. Whatever the reason, scores on a math quiz change, and therefore, they are variables. So we know that our variables are eating breakfast and math performance. But how do we measure them? There are four major scales (or types) of measurement of variables: nominal, ordinal, interval and ratio. The scale of measurement depends on the variable itself. Let's look closer at each of the four scales and what types of variables fall into each category.","PeriodicalId":196141,"journal":{"name":"Making Sense of Statistics","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130038901","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 : 2018-06-13DOI: 10.4324/9781315179803-40
F. Pyrczak, Deborah M. Oh
{"title":"Multiple Correlation","authors":"F. Pyrczak, Deborah M. Oh","doi":"10.4324/9781315179803-40","DOIUrl":"https://doi.org/10.4324/9781315179803-40","url":null,"abstract":"","PeriodicalId":196141,"journal":{"name":"Making Sense of Statistics","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131019268","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 : 2018-06-13DOI: 10.4324/9781315179803-32
F. Pyrczak, Deborah M. Oh
{"title":"Reports of the Results of t Tests","authors":"F. Pyrczak, Deborah M. Oh","doi":"10.4324/9781315179803-32","DOIUrl":"https://doi.org/10.4324/9781315179803-32","url":null,"abstract":"","PeriodicalId":196141,"journal":{"name":"Making Sense of Statistics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125124578","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 : 2018-06-13DOI: 10.4324/9781315179803-29
F. Pyrczak, Deborah M. Oh
• With previous tests, we were interested in comparing a single sample with a population • With most research, you do not have knowledge about the population-you don't know the population mean and standard deviation INDEPENDENT SAMPLES T-TEST: • Hypothesis testing procedure that uses separate samples for each treatment condition (between subjects design) • Use this test when the population mean and standard deviation are unknown, and 2 separate groups are being compared Example: Do males and females differ in terms of their exam scores? • Take a sample of males and a separate sample of females and apply the hypothesis testing steps to determine if there is a significant difference in scores between the groups
{"title":"Independent Samples t Test","authors":"F. Pyrczak, Deborah M. Oh","doi":"10.4324/9781315179803-29","DOIUrl":"https://doi.org/10.4324/9781315179803-29","url":null,"abstract":"• With previous tests, we were interested in comparing a single sample with a population • With most research, you do not have knowledge about the population-you don't know the population mean and standard deviation INDEPENDENT SAMPLES T-TEST: • Hypothesis testing procedure that uses separate samples for each treatment condition (between subjects design) • Use this test when the population mean and standard deviation are unknown, and 2 separate groups are being compared Example: Do males and females differ in terms of their exam scores? • Take a sample of males and a separate sample of females and apply the hypothesis testing steps to determine if there is a significant difference in scores between the groups","PeriodicalId":196141,"journal":{"name":"Making Sense of Statistics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126950294","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 : 2018-06-13DOI: 10.4324/9781315179803-25
F. Pyrczak, Deborah M. Oh
{"title":"Introduction to Hypothesis Testing","authors":"F. Pyrczak, Deborah M. Oh","doi":"10.4324/9781315179803-25","DOIUrl":"https://doi.org/10.4324/9781315179803-25","url":null,"abstract":"","PeriodicalId":196141,"journal":{"name":"Making Sense of Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116430875","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 : 2018-06-13DOI: 10.4324/9781315179803-22
F. Pyrczak, Deborah M. Oh
{"title":"Pearson r","authors":"F. Pyrczak, Deborah M. Oh","doi":"10.4324/9781315179803-22","DOIUrl":"https://doi.org/10.4324/9781315179803-22","url":null,"abstract":"","PeriodicalId":196141,"journal":{"name":"Making Sense of Statistics","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123480217","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 : 2018-06-13DOI: 10.4324/9781315179803-39
F. Pyrczak, Deborah M. Oh
As with simple regression, in the multiple linear regression model, we can interpret ! SYY " RSS SYY as the fraction of the variability in Y explained by including the terms u 1 , u 2 , … , u k-1 in the mean function (as compared to the constant mean function). In the multiple regression context, ! SYY " RSS SYY is denoted as R 2 (with capital R). R 2 is called the coefficient of (multiple) determination or (misleadingly) the squared multiple correlation. • R alone (unsquared) has no meaning in multiple regression. • By convention, we use small r for the sample correlation in simple regression. • In multiple regression, we can talk about correlation between two variables (i.e,, just two at once). • In particular, in multiple regression, r ij is often used to denote the sample correlation coefficient between terms u i and u j. • R 2 is sometimes used for comparing models. But caution is needed: o It only makes sense to use for comparing models that are in the same units (e.g., submodels of the same full model). o A submodel of a model will always have a smaller R 2 than the larger model. o As discussed above and below, many other considerations should be taken to account in selecting a model.
与简单回归一样，在多元线性回归模型中，我们可以解释!SYY“RSS SYY是Y中可变性的一部分，通过在平均函数中包含u 1, u 2，…，u k-1来解释(与常数平均函数相比)。在多元回归上下文中，!SYY表示为r2(大写R)。r2称为(倍数)决定系数或(容易引起误解的)平方倍数相关。•R单独(unsquared)在多元回归中没有意义。•按照惯例，我们在简单回归中使用小r表示样本相关性。•在多元回归中，我们可以讨论两个变量之间的相关性(即，一次只有两个变量)。•特别是，在多元回归中，r ij常用于表示u i和u j项之间的样本相关系数。•r 2有时用于比较模型。但是需要注意的是:它只在比较相同单元中的模型(例如，相同完整模型的子模型)时才有意义。一个模型的子模型总是比大模型的r2小。o如上文和下文所讨论的，在选择模式时应考虑许多其他因素。
{"title":"Coefficient of Determination","authors":"F. Pyrczak, Deborah M. Oh","doi":"10.4324/9781315179803-39","DOIUrl":"https://doi.org/10.4324/9781315179803-39","url":null,"abstract":"As with simple regression, in the multiple linear regression model, we can interpret ! SYY \" RSS SYY as the fraction of the variability in Y explained by including the terms u 1 , u 2 , … , u k-1 in the mean function (as compared to the constant mean function). In the multiple regression context, ! SYY \" RSS SYY is denoted as R 2 (with capital R). R 2 is called the coefficient of (multiple) determination or (misleadingly) the squared multiple correlation. • R alone (unsquared) has no meaning in multiple regression. • By convention, we use small r for the sample correlation in simple regression. • In multiple regression, we can talk about correlation between two variables (i.e,, just two at once). • In particular, in multiple regression, r ij is often used to denote the sample correlation coefficient between terms u i and u j. • R 2 is sometimes used for comparing models. But caution is needed: o It only makes sense to use for comparing models that are in the same units (e.g., submodels of the same full model). o A submodel of a model will always have a smaller R 2 than the larger model. o As discussed above and below, many other considerations should be taken to account in selecting a model.","PeriodicalId":196141,"journal":{"name":"Making Sense of Statistics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115212651","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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