Practical Statistics for Educators - User contributions [en]

http://practicalstats.labanca.net/api.php?action=feedcontributions&feedformat=atom&user=Aasulliv1 Practical Statistics for Educators - User contributions [en] 2026-09-25T00:14:41Z User contributions MediaWiki 1.31.16 http://practicalstats.labanca.net/index.php?title=Main_Page&diff=185 Main Page 2019-11-30T01:20:14Z

<p>Aasulliv1: /* Modules */</p> <hr /> <div><big>'''Practical Statistics for Educators'''</big><br /> edited and maintained by Frank LaBanca, EdD<br /> <br /> ----<br /> <br /> <br /> == Philosophy ==<br /> <br /> <br /> Quantitative statistical analyses can be intimidating for many educators pursing an advanced academic degree. The thought of computational math can sometimes trigger unwarranted fears. <br /> <br /> Here, we approach statistics from a straightforward conceptually-based perspective. Our goal is to collaborate and provide insight for statistics that make them meaningful tools in the educational arena.<br /> <br /> Each "module" corresponds with the topics presented each week, and will expand as the course progresses. A topical outline can be found @ [http://docs.google.com/Doc?id=dfqvtcqp_46hhzzcsgt ]<br /> <br /> Comments and edits are welcome and encouraged! Please give yourself credit as you contribute. At the end of a section you insert please add the following in italics:<br /> ''contributed by <your name>''<br /> If you are modifying content, add the following under the contribution line:<br /> ''modified by <your name>'' We are glad to accept as many modifications as necessary to give the most meaning to each section. As we asynchronously socially construct knowledge together, we can recognize the accomplishments and contributions of each writer.<br /> <br /> ''contributed by Frank LaBanca, EdD''<br /> <br /> == Contributions ==<br /> <br /> Our contributors [[contributions here]].<br /> <br /> Please submit your contribution at [https://forms.gle/PBaVrxFffbk5CKgg8]<br /> <br /> == Modules ==<br /> <br /> 1.1 [[The Greek Alphabet]] and its significance in statistics<br /> <br /> 1.2 An introduction to probability PowerPoint @[http://docs.google.com/Presentation?id=dfqvtcqp_97wcrbtsn]<br /> <br /> <br /> 2.1 [[Types of Data]]<br /> <br /> 2.2 [[Visualizing Data]]<br /> <br /> 2.3 Visually representing data PowerPoint @ [http://docs.google.com/Presentation?docid=dfqvtcqp_27dwth2zz2#]<br /> <br /> 2.3.1 Table 2 from LaBanca dissertation @ [http://docs.google.com/Doc?id=dfqvtcqp_25cb5pqcfw]<br /> <br /> 2.3.2 Cool graph of movie box office from NY Times [http://www.nytimes.com/interactive/2008/02/23/movies/20080223_REVENUE_GRAPHIC.html#]<br /> <br /> 2.3.4 [[Histograms]]<br /> <br /> 2.3.5 Scatterplots YouTube @ [http://youtu.be/HFuU1uxJ1tQ]<br /> <br /> 2.4 [[Shapes of distribution]]<br /> <br /> 2.5 Survey of Attitudes Toward Statistics (SATS) Data Set @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7PokF_oAoCRUg]<br /> <br /> 2.6 [[Data Screening]]<br /> <br /> <br /> <br /> 3.1 [[Central Tendency]]<br /> <br /> 3.1.1 Central Tendency and Normal Distribution PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_47dfhfr4nw ]<br /> <br /> 3.1.2 Central Tendency YouTube @ [http://youtu.be/Fn4z8RDpwDY]<br /> <br /> 3.2 [[Interquartile ranges]]<br /> <br /> 3.2.1 [[The Box Plot]]<br /> <br /> 3.3 [[Standard deviation]]<br /> <br /> 3.3.1 [[Identifying percentile ranks and scores based on standard deviation]]<br /> <br /> 3.3.1.a [[Practice Identifying percentile ranks and scores based on standard deviation]]<br /> <br /> 3.4 [[z-scores]]<br /> <br /> <br /> <br /> 4.1 [[Percentile Rank]]<br /> 4.1.1 Areas under the standard normal curve for z values @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7NstEjJ40jJOQ]<br /> <br /> 4.1.2 z scores corresponding to divisions of the area under the normal curve @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7P6SVNiBdWbEg]<br /> <br /> 4.2 Conversion of data PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_98chfzg2tf]<br /> <br /> 4.2.1 Descriptive analysis of USRT data @ [http://docs.google.com/Doc?id=dfqvtcqp_61db6smpdm ]<br /> <br /> 4.3 [[Normal Curve Equivalent scores]]<br /> <br /> 4.3 [[Standard Error of Measurement]]<br /> <br /> 4.4 [[Confidence Intervals]]<br /> <br /> 4.4 z score machine @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7N8oLJZwmr3Zw]<br /> <br /> <br /> <br /> 5.1 [[Pearson r]]<br /> <br /> 5.2 [[Rules of thumb for interpreting the size of a correlation coefficient]]<br /> <br /> 5.3 Critical values for the correlation coefficient @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7MkuRpIgceTRQ]<br /> <br /> 5.4 [[Spearman rho]]<br /> <br /> 5.5 Correlation PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_134hsxg7td7]<br /> <br /> 5.6 [[Writing samples for correlations]]<br /> <br /> <br /> 6.1 [[Inferential Statistics Definition]]<br /> <br /> 6.2 [[Sampling]]<br /> <br /> 6.3 [[Sampling distributions]] <br /> <br /> 6.4 t test PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_153gr9f3hgd]<br /> <br /> 6.5 Sample data set @ [http://wolfweb.unr.edu/homepage/liu/stat/help/help.htm]<br /> <br /> 6.6 Critical values for t @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7OZzyZeHg9MIA]<br /> <br /> <br /> 7.1 [[Effect size]]<br /> <br /> 7.1.1 Effect size calculator @ http://www.campbellcollaboration.org/resources/effect_size_input.php<br /> <br /> 7.1.2 [[Rules of thumb for interpreting effect sizes]]<br /> <br /> 7.2 Effect size PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_204f42f67dx ]<br /> <br /> 7.3 [[Hypothesis testing]]<br /> <br /> 7.4 Hypothesis testing PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_23295tm65dx]<br /> <br /> 7.5.1 Hypothesis testing template for a correlation @ [http://docs.google.com/Doc?id=dfqvtcqp_174ccchz4ds]<br /> <br /> 7.5.2 Hypothesis testing template for a t test @ [http://docs.google.com/Doc?id=dfqvtcqp_175hjcdsjff]<br /> <br /> <br /> 8.1 [[Type I and Type II Errors]]<br /> <br /> 8.2 Type I and Type II Errors PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_258wcq56rdv]<br /> <br /> 8.3 [[Levene's p versus the test statistic p]]<br /> <br /> 8.4 [[Analysis of Variance]]<br /> <br /> 8.5 ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_265ckd9j9dv]<br /> <br /> 8.6 [[ANOVA Case study]]<br /> <br /> 8.7 Critical values for the F statistic @ [http://www.sussex.ac.uk/Users/grahamh/RM1web/F-ratio%20table%202005.pdf]<br /> <br /> 8.7 [[Rules of thumb for interpreting effect sizes of ANOVAs]]<br /> <br /> <br /> 9.1 Post Hoc test PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_284dbhghdc9]<br /> <br /> 9.2 [[Selecting a Post Hoc test]]<br /> <br /> 9.3 Hypothesis testing template for ANOVA @ [http://docs.google.com/Doc?id=dfqvtcqp_295ckngxdgj]<br /> <br /> <br /> 10.1 [[Chi square]]<br /> <br /> 10.2 [[Example for calculating chi square]]<br /> <br /> 10.3 Critical values for chi square @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7OhBVHQWoHTIQ]<br /> <br /> 10.4 [[Chi square analysis description/sample writing]]<br /> <br /> 10.5 Chi square PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_297dhg685g8]<br /> <br /> <br /> 11.1 [[Beyond the ANOVA]]<br /> <br /> 11.2 Beyond ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_307cmrtpxg3]<br /> <br /> 11.3 2-way ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_325hrt86ggt]<br /> <br /> 11.4 2-way ANOVA template @ [http://docs.google.com/Doc?id=dfqvtcqp_372599pr6w6]<br /> <br /> <br /> 12.1 [[MANOVA]]<br /> <br /> 12.2 [[Homogeneity vs Homoscedacity]] (Levene vs Box's M)<br /> <br /> 12.3 [[Post Hoc ANOVAs for MANOVA]] (univariate)<br /> <br /> 12.4 [[Post Hoc Discriminant Analysis]] (multivariate)<br /> <br /> 12.5 [[Covariates]]<br /> <br /> 12.6 [[MANCOVA]]<br /> <br /> <br /> 13.1 [[Multiple Regression Analysis]]<br /> <br /> 13.2 [[Multiple Linear Regression]]<br /> <br /> 13.3 Reading the MLR Output: An annotated output [https://drive.google.com/file/d/141RNyYNnuDDTNvHYi4fE8EF8qWmal1aa/view?usp=sharing]<br /> <br /> <br /> 14.1 Internal Consistency[https://docs.google.com/document/d/18K16I8u9sbwpUhW9nIENF4x9wDXAy-4wFuBp6a0AljA/edit]<br /> <br /> 14.2 Cronbach's Alpha[https://docs.google.com/document/d/1_eyXOcFrBcDSctM27a9T2kUlx9D8TidV_YTHk-wvTu0/edit]<br /> <br /> 14.2.1 [[Cronbach's Alpha Values]]<br /> <br /> == Applied Research Designs ==<br /> <br /> 15.1 [[Instrumentation]]<br /> <br /> 15.2 [[Limitations]]<br /> <br /> 15.3 [[Practice determining the stat]]<br /> <br /> == Getting started ==<br /> * [http://www.mediawiki.org/wiki/Manual:Configuration_settings Configuration settings list]<br /> * [http://www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]<br /> * [http://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]</div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=Main_Page&diff=184 Main Page 2019-11-30T01:13:13Z

<p>Aasulliv1: /* Modules */</p> <hr /> <div><big>'''Practical Statistics for Educators'''</big><br /> edited and maintained by Frank LaBanca, EdD<br /> <br /> ----<br /> <br /> <br /> == Philosophy ==<br /> <br /> <br /> Quantitative statistical analyses can be intimidating for many educators pursing an advanced academic degree. The thought of computational math can sometimes trigger unwarranted fears. <br /> <br /> Here, we approach statistics from a straightforward conceptually-based perspective. Our goal is to collaborate and provide insight for statistics that make them meaningful tools in the educational arena.<br /> <br /> Each "module" corresponds with the topics presented each week, and will expand as the course progresses. A topical outline can be found @ [http://docs.google.com/Doc?id=dfqvtcqp_46hhzzcsgt ]<br /> <br /> Comments and edits are welcome and encouraged! Please give yourself credit as you contribute. At the end of a section you insert please add the following in italics:<br /> ''contributed by <your name>''<br /> If you are modifying content, add the following under the contribution line:<br /> ''modified by <your name>'' We are glad to accept as many modifications as necessary to give the most meaning to each section. As we asynchronously socially construct knowledge together, we can recognize the accomplishments and contributions of each writer.<br /> <br /> ''contributed by Frank LaBanca, EdD''<br /> <br /> == Contributions ==<br /> <br /> Our contributors [[contributions here]].<br /> <br /> Please submit your contribution at [https://forms.gle/PBaVrxFffbk5CKgg8]<br /> <br /> == Modules ==<br /> <br /> 1.1 [[The Greek Alphabet]] and its significance in statistics<br /> <br /> 1.2 An introduction to probability PowerPoint @[http://docs.google.com/Presentation?id=dfqvtcqp_97wcrbtsn]<br /> <br /> <br /> 2.1 [[Types of Data]]<br /> <br /> 2.2 [[Visualizing Data]]<br /> <br /> 2.3 Visually representing data PowerPoint @ [http://docs.google.com/Presentation?docid=dfqvtcqp_27dwth2zz2#]<br /> <br /> 2.3.1 Table 2 from LaBanca dissertation @ [http://docs.google.com/Doc?id=dfqvtcqp_25cb5pqcfw]<br /> <br /> 2.3.2 Cool graph of movie box office from NY Times [http://www.nytimes.com/interactive/2008/02/23/movies/20080223_REVENUE_GRAPHIC.html#]<br /> <br /> 2.3.4 [[Histograms]]<br /> <br /> 2.3.5 Scatterplots YouTube @ [http://youtu.be/HFuU1uxJ1tQ]<br /> <br /> 2.4 [[Shapes of distribution]]<br /> <br /> 2.5 Survey of Attitudes Toward Statistics (SATS) Data Set @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7PokF_oAoCRUg]<br /> <br /> 2.6 [[Data Screening]]<br /> <br /> <br /> <br /> 3.1 [[Central Tendency]]<br /> <br /> 3.1.1 Central Tendency and Normal Distribution PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_47dfhfr4nw ]<br /> <br /> 3.1.2 Central Tendency YouTube @ [http://youtu.be/Fn4z8RDpwDY]<br /> <br /> 3.2 [[Interquartile ranges]]<br /> <br /> 3.2.1 [[The Box Plot]]<br /> <br /> 3.3 [[Standard deviation]]<br /> <br /> 3.3.1 [[Identifying percentile ranks and scores based on standard deviation]]<br /> <br /> 3.3.1.a [[Practice Identifying percentile ranks and scores based on standard deviation]]<br /> <br /> 3.4 [[z-scores]]<br /> <br /> <br /> <br /> 4.1 [[Percentile Rank]]<br /> 4.1.1 Areas under the standard normal curve for z values @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7NstEjJ40jJOQ]<br /> <br /> 4.1.2 z scores corresponding to divisions of the area under the normal curve @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7P6SVNiBdWbEg]<br /> <br /> 4.2 Conversion of data PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_98chfzg2tf]<br /> <br /> 4.2.1 Descriptive analysis of USRT data @ [http://docs.google.com/Doc?id=dfqvtcqp_61db6smpdm ]<br /> <br /> 4.3 [[Normal Curve Equivalent scores]]<br /> <br /> 4.3 [[Standard Error of Measurement]]<br /> <br /> 4.4 [[Confidence Intervals]]<br /> <br /> 4.4 z score machine @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7N8oLJZwmr3Zw]<br /> <br /> <br /> <br /> 5.1 [[Pearson r]]<br /> <br /> 5.2 [[Rules of thumb for interpreting the size of a correlation coefficient]]<br /> <br /> 5.3 Critical values for the correlation coefficient @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7MkuRpIgceTRQ]<br /> <br /> 5.4 [[Spearman rho]]<br /> <br /> 5.5 Correlation PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_134hsxg7td7]<br /> <br /> 5.6 [[Writing samples for correlations]]<br /> <br /> <br /> 6.1 [[Inferential Statistics Definition]]<br /> <br /> 6.2 [[Sampling]]<br /> <br /> 6.3 [[Sampling distributions]] <br /> <br /> 6.4 t test PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_153gr9f3hgd]<br /> <br /> 6.5 Sample data set @ [http://wolfweb.unr.edu/homepage/liu/stat/help/help.htm]<br /> <br /> 6.6 Critical values for t @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7OZzyZeHg9MIA]<br /> <br /> <br /> 7.1 [[Effect size]]<br /> <br /> 7.1.1 Effect size calculator @ http://www.campbellcollaboration.org/resources/effect_size_input.php<br /> <br /> 7.1.2 [[Rules of thumb for interpreting effect sizes]]<br /> <br /> 7.2 Effect size PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_204f42f67dx ]<br /> <br /> 7.3 [[Hypothesis testing]]<br /> <br /> 7.4 Hypothesis testing PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_23295tm65dx]<br /> <br /> 7.5.1 Hypothesis testing template for a correlation @ [http://docs.google.com/Doc?id=dfqvtcqp_174ccchz4ds]<br /> <br /> 7.5.2 Hypothesis testing template for a t test @ [http://docs.google.com/Doc?id=dfqvtcqp_175hjcdsjff]<br /> <br /> <br /> 8.1 [[Type I and Type II Errors]]<br /> <br /> 8.2 Type I and Type II Errors PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_258wcq56rdv]<br /> <br /> 8.3 [[Levene's p versus the test statistic p]]<br /> <br /> 8.4 [[Analysis of Variance]]<br /> <br /> 8.5 ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_265ckd9j9dv]<br /> <br /> 8.6 [[ANOVA Case study]]<br /> <br /> 8.7 Critical values for the F statistic @ [http://www.sussex.ac.uk/Users/grahamh/RM1web/F-ratio%20table%202005.pdf]<br /> <br /> 8.7 [[Rules of thumb for interpreting effect sizes of ANOVAs]]<br /> <br /> <br /> 9.1 Post Hoc test PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_284dbhghdc9]<br /> <br /> 9.2 [[Selecting a Post Hoc test]]<br /> <br /> 9.3 Hypothesis testing template for ANOVA @ [http://docs.google.com/Doc?id=dfqvtcqp_295ckngxdgj]<br /> <br /> <br /> 10.1 [[Chi square]]<br /> <br /> 10.2 [[Example for calculating chi square]]<br /> <br /> 10.3 Critical values for chi square @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7OhBVHQWoHTIQ]<br /> <br /> 10.4 [[Chi square analysis description/sample writing]]<br /> <br /> 10.5 Chi square PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_297dhg685g8]<br /> <br /> <br /> 11.1 [[Beyond the ANOVA]]<br /> <br /> 11.2 Beyond ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_307cmrtpxg3]<br /> <br /> 11.3 2-way ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_325hrt86ggt]<br /> <br /> 11.4 2-way ANOVA template @ [http://docs.google.com/Doc?id=dfqvtcqp_372599pr6w6]<br /> <br /> <br /> 12.1 [[MANOVA]]<br /> <br /> 12.2 [[Homogeneity vs Homoscedacity]] (Levene vs Box's M)<br /> <br /> 12.3 [[Post Hoc ANOVAs for MANOVA]] (univariate)<br /> <br /> 12.4 [[Post Hoc Discriminant Analysis]] (multivariate)<br /> <br /> 12.5 [[Covariates]]<br /> <br /> 12.6 [[MANCOVA]]<br /> <br /> <br /> 13.1 [[Multiple Regression Analysis]]<br /> <br /> 13.2 [[Multiple Linear Regression]]<br /> <br /> 13.3 [[Reading the MLR Output: An annotated output]]<br /> <br /> <br /> 14.1 Internal Consistency[https://docs.google.com/document/d/18K16I8u9sbwpUhW9nIENF4x9wDXAy-4wFuBp6a0AljA/edit]<br /> <br /> 14.2 Cronbach's Alpha[https://docs.google.com/document/d/1_eyXOcFrBcDSctM27a9T2kUlx9D8TidV_YTHk-wvTu0/edit]<br /> <br /> 14.2.1 [[Cronbach's Alpha Values]]<br /> <br /> == Applied Research Designs ==<br /> <br /> 15.1 [[Instrumentation]]<br /> <br /> 15.2 [[Limitations]]<br /> <br /> 15.3 [[Practice determining the stat]]<br /> <br /> == Getting started ==<br /> * [http://www.mediawiki.org/wiki/Manual:Configuration_settings Configuration settings list]<br /> * [http://www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]<br /> * [http://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]</div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=Shapes_of_distribution&diff=177 Shapes of distribution 2019-11-18T18:19:57Z

<p>Aasulliv1: </p> <hr /> <div>Distribution of data can take a wide variety of shapes, and ultimately depends on how data points are distributed along the measurement scale. A general "feel" for the data can be achieved by examining the uniformity (or lack thereof) of a distribution. In general, the larger the sample size, the more symmetrical the distribution.<br /> <br /> <br /> == Uniform distribution ==<br /> <br /> <br /> == Normal (bell-shaped) distribution ==<br /> <br /> When the collected data tends to hover around a central value, with no bias to the left or the right, the data creates a Normal distribution. <br /> This Normal distribution is also referred to as the "Bell Curve" because it resembles a bell like shape.<br /> <br /> When stating that data is normally distributed we are identifying that 50% of the values are less than the mean and that 50% of the values are greater than the mean. In a normal distribution the mean, median, and mode are equal to one another.<br /> <br /> Examples of data that follow a normal distribution could include blood pressure and scores on a test.<br /> <br /> ''contributed by Scott Trungadi''<br /> <br /> == Skewness ==<br /> <br /> Skewed right<br /> Skewed left<br /> <br /> Acceptable skewness values: <big>-1.000 < skewness < 1.000 </big><br /> <br /> <br /> Examining the data skewness allows you to see the variability of a data set. Skewness is when a data set does not follow the normal distribution. A normal distribution has a skewness of zero, and will have perfect symmetry. Data that is positively skewed will be skewed to the right and will be a positive number; data that is negatively skewed is skewed to the left of the data mean, and is a negative number. See an example of skewness, below.<br /> <br /> ''contributed by Cassandra Cosentino'' <br /> <br /> [[File:skewness.png]]<br /> <br /> == Kurtosis ==<br /> <br /> Kurtosis describes “the clustering of scores toward the center of the distribution” (Meyers, Gamst, & Guarino, 2017, p. 53).<br /> <br /> <br /> There are three types of kurtosis:<br /> <br /> 1. Mesokurtic: A normal distribution; has a kurtosis value of 0.<br /> <br /> 2. Leptokurtic: Positive values of kurtosis; indicate that the bulk of scores are drawn in toward the middle (sharply peaked with heavy tails, for instance).<br /> <br /> 3. Platykurtic: Negative values of kurtosis; scores are more equally distributed across the entire continuum (a more rectangular distribution). (Meyers, Gamst, & Guarino, 2017, p. 53).<br /> <br /> <br /> Contribution by: Britany Kuslis, WCSU Cohort 8<br /> <br /> Reference:<br /> Meyers, L., Gamst, G., & Guarino, A.J. (2017). ''Applied multivariate research: Design and interpretation''. Thousand Oaks, CA: Sage Publications<br /> <br /> <br /> -----------------------------<br /> Leptokurtic (high, peaky line)<br /> Platykurtic (lower, flatter line)<br /> <br /> Acceptable kurtosis values : <big>-1.000 < kurtosis < 1.000 </big><br /> [[File:kurtosis.jpg]]<br /> <br /> Ashley Brooksbank<br /> </div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=Shapes_of_distribution&diff=176 Shapes of distribution 2019-11-18T18:17:13Z

<p>Aasulliv1: </p> <hr /> <div>Distribution of data can take a wide variety of shapes, and ultimately depends on how data points are distributed along the measurement scale. A general "feel" for the data can be achieved by examining the uniformity (or lack thereof) of a distribution. In general, the larger the sample size, the more symmetrical the distribution.<br /> <br /> <br /> == Uniform distribution ==<br /> <br /> <br /> == Normal (bell-shaped) distribution ==<br /> <br /> When the collected data tends to hover around a central value, with no bias to the left or the right, the data creates a Normal distribution. <br /> This Normal distribution is also referred to as the "Bell Curve" because it resembles a bell like shape.<br /> <br /> When stating that data is normally distributed we are identifying that 50% of the values are less than the mean and that 50% of the values are greater than the mean. In a normal distribution the mean, median, and mode are equal to one another.<br /> <br /> Examples of data that follow a normal distribution could include blood pressure and scores on a test.<br /> <br /> ''contributed by Scott Trungadi''<br /> <br /> == Skewness ==<br /> <br /> Skewed right<br /> Skewed left<br /> <br /> Acceptable skewness values: <big>-1.000 < skewness < 1.000 </big><br /> <br /> <br /> Examining the data skewness allows you to see the variability of a data set. Skewness is when a data set does not follow the normal distribution. A normal distribution has a skewness of zero, and will have perfect symmetry. Data that is positively skewed will be skewed to the right and will be a positive number; data that is negatively skewed is skewed to the left of the data mean, and is a negative number. See an example of skewness, below.<br /> <br /> ''contributed by Cassandra Cosentino'' <br /> <br /> [[File:skewness.png]]<br /> <br /> == Kurtosis ==<br /> <br /> Kurtosis describes “the clustering of scores toward the center of the distribution” (Meyers, Gamst, & Guarino, 2017, p. 53).<br /> <br /> <br /> There are three types of kurtosis:<br /> <br /> 1. Mesokurtic: A normal distribution; has a kurtosis value of 0.<br /> <br /> 2. Leptokurtic: Positive values of kurtosis; indicate that the bulk of scores are drawn in toward the middle (sharply peaked with heavy tails, for instance).<br /> <br /> 3. Platykurtic: Negative values of kurtosis; scores are more equally distributed across the entire continuum (a more rectangular distribution). (Meyers, Gamst, & Guarino, 2017, p. 53).<br /> <br /> <br /> Contribution by: Britany Kuslis, WCSU Cohort 8<br /> <br /> Reference:<br /> Meyers, L., Gamst, G., & Guarino, A.J. (2017). ''Applied multivariate research: Design and interpretation''. Thousand Oaks, CA: Sage Publications<br /> <br /> <br /> -----------------------------<br /> Leptokurtic<br /> Platykurtic<br /> <br /> Acceptable kurtosis values : <big>-1.000 < kurtosis < 1.000 </big><br /> [[File:kurtosis.jpg]]<br /> <br /> Ashley Brooksbank<br /> </div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=Shapes_of_distribution&diff=175 Shapes of distribution 2019-11-18T18:16:04Z

<p>Aasulliv1: /* Kurtosis */</p> <hr /> <div>Distribution of data can take a wide variety of shapes, and ultimately depends on how data points are distributed along the measurement scale. A general "feel" for the data can be achieved by examining the uniformity (or lack thereof) of a distribution. In general, the larger the sample size, the more symmetrical the distribution.<br /> <br /> <br /> == Uniform distribution ==<br /> <br /> <br /> == Normal (bell-shaped) distribution ==<br /> <br /> When the collected data tends to hover around a central value, with no bias to the left or the right, the data creates a Normal distribution. <br /> This Normal distribution is also referred to as the "Bell Curve" because it resembles a bell like shape.<br /> <br /> When stating that data is normally distributed we are identifying that 50% of the values are less than the mean and that 50% of the values are greater than the mean. In a normal distribution the mean, median, and mode are equal to one another.<br /> <br /> Examples of data that follow a normal distribution could include blood pressure and scores on a test.<br /> <br /> ''contributed by Scott Trungadi''<br /> <br /> == Skewness ==<br /> <br /> Skewed right<br /> Skewed left<br /> <br /> Acceptable skewness values: <big>-1.000 < skewness < 1.000 </big><br /> <br /> <br /> Examining the data skewness allows you to see the variability of a data set. Skewness is when a data set does not follow the normal distribution. A normal distribution has a skewness of zero, and will have perfect symmetry. Data that is positively skewed will be skewed to the right and will be a positive number; data that is negatively skewed is skewed to the left of the data mean, and is a negative number. See an example of skewness, below.<br /> <br /> ''contributed by Cassandra Cosentino'' <br /> <br /> [[File:skewness.png]]<br /> <br /> == Kurtosis ==<br /> <br /> Kurtosis describes “the clustering of scores toward the center of the distribution” (Meyers, Gamst, & Guarino, 2017, p. 53).<br /> <br /> <br /> There are three types of kurtosis:<br /> <br /> 1. Mesokurtic: A normal distribution; has a kurtosis value of 0.<br /> <br /> 2. Leptokurtic: Positive values of kurtosis; indicate that the bulk of scores are drawn in toward the middle (sharply peaked with heavy tails, for instance).<br /> <br /> 3. Platykurtic: Negative values of kurtosis; scores are more equally distributed across the entire continuum (a more rectangular distribution). (Meyers, Gamst, & Guarino, 2017, p. 53).<br /> <br /> <br /> Contribution by: Britany Kuslis, WCSU Cohort 8<br /> <br /> Reference:<br /> Meyers, L., Gamst, G., & Guarino, A.J. (2017). ''Applied multivariate research: Design and interpretation''. Thousand Oaks, CA: Sage Publications<br /> <br /> <br /> -----------------------------<br /> Leptokurtic<br /> Platykurtic<br /> <br /> Acceptable kurtosis values : <big>-1.000 < kurtosis < 1.000 </big><br /> [[File:kurtosis.jpeg]]<br /> <br /> Ashley Brooksbank<br /> </div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=Practice_Identifying_percentile_ranks_and_scores_based_on_standard_deviation&diff=118 Practice Identifying percentile ranks and scores based on standard deviation 2019-10-29T18:16:06Z

<p>Aasulliv1: </p> <hr /> <div><br /> == Suppose a test on math anxiety was given to a large group of individuals and the scores are from a normally distributed population in which the mean is 50 and the SD is 10 ==<br /> <br /> [[File:Normal.jpg]]<br /> <br /> 1) Approximately what % of individuals earned scores below 50?<br /> <br /> <br /> 2) Approximately what % of individuals earned scores above 60<br /> <br /> <br /> 3) Approximately what % of individuals earned scores below 30?<br /> <br /> <br /> 4) Approximately what % of individuals earned scores between 60 and 70?<br /> <br /> <br /> 5) Approximately what % of individuals earned scores between 30 and 70?<br /> <br /> <br /> 6) A score of 60 on this test would give an individual a percentile rank of?<br /> <br /> <br /> 7) The average cost of a new car today is $17,500. If this is normally distributed with a SD of $2000 compute the following. If I am only willing to pay up to $15,500, what percentage of the total number of cars will fall inside my budget?<br /> <br /> <br /> == Answers ==<br /> <br /> 1) 50% <br /> 2) 16%<br /> 3) 2.15%<br /> 4) 13.6%<br /> 5) 95.4%<br /> 6) 84%<br /> 7) 16%</div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=Practice_Identifying_percentile_ranks_and_scores_based_on_standard_deviation&diff=117 Practice Identifying percentile ranks and scores based on standard deviation 2019-10-29T18:13:42Z

<p>Aasulliv1: Created page with "File:Normal.jpeg == Suppose a test on math anxiety was given to a large group of individuals and the scores are from a normally distributed population in which the mean..."</p> <hr /> <div>[[File:Normal.jpeg]]<br /> <br /> <br /> == Suppose a test on math anxiety was given to a large group of individuals and the scores are from a normally distributed population in which the mean is 50 and the SD is 10<br /> ==<br /> 1) Approximately what % of individuals earned scores below 50?<br /> <br /> <br /> 2) Approximately what % of individuals earned scores above 60<br /> <br /> <br /> 3) Approximately what % of individuals earned scores below 30?<br /> <br /> <br /> 4) Approximately what % of individuals earned scores between 60 and 70?<br /> <br /> <br /> 5) Approximately what % of individuals earned scores between 30 and 70?<br /> <br /> <br /> 6) A score of 60 on this test would give an individual a percentile rank of?<br /> <br /> <br /> 7) The average cost of a new car today is $17,500. If this is normally distributed with a SD of $2000 compute the following. If I am only willing to pay up to $15,500, what percentage of the total number of cars will fall inside my budget?<br /> <br /> <br /> == Answers ==<br /> <br /> 1) 50% <br /> 2) 16%<br /> 3) 2.15%<br /> 4) 13.6%<br /> 5) 95.4%<br /> 6) 84%<br /> 7) 16%</div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=Identifying_percentile_ranks_and_scores_based_on_standard_deviation&diff=116 Identifying percentile ranks and scores based on standard deviation 2019-10-29T17:51:44Z

<p>Aasulliv1: </p> <hr /> <div>[[File:Normal.jpg]]<br /> <br /> <br /> == '''To identify the values of data points at each standard deviation ''' ==<br /> <br /> 1. Identify the mean and the standard deviation (ex: M=80; SD = 5) <br /> 2. Label the curve beginning with the mean at the 0 value. To identify the data value at each standard deviation (-3 to 3) multiple the label by the standard deviation, and then add that to the mean (ex: [(5 x -3) + 20 = 5] so 3 standard deviations below the mean would be 5<br /> <br /> <br /> == '''To identify what the percentile rank exists at each standard deviation''' ==<br /> <br /> 1. Use the above process to label the mean and standard deviations on the normal curve <br /> 2. Add together the percentage of scores that exist to the left of that data point<br /> <br /> <br /> == '''To identify the percentage scoring between x and y score''' ==<br /> <br /> 1. Use the above process to label the mean and standard deviations on the normal curve <br /> 2. Identify what standard deviation x score aligns with what standard deviation y score aligns with <br /> 3. Add together all percentage of scores that exist between the two data points<br /> **this is only to be used if score x and y fall on a standard deviation**</div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=Identifying_percentile_ranks_and_scores_based_on_standard_deviation&diff=115 Identifying percentile ranks and scores based on standard deviation 2019-10-29T17:49:58Z

<p>Aasulliv1: Created page with "File:Normal.jpg To identify the values of data points at each standard deviation 1. Identify the mean and the standard deviation (ex: M=80; SD = 5) 2. Label the curve b..."</p> <hr /> <div>[[File:Normal.jpg]]<br /> <br /> To identify the values of data points at each standard deviation <br /> 1. Identify the mean and the standard deviation (ex: M=80; SD = 5) <br /> 2. Label the curve beginning with the mean at the 0 value. To identify the data value at each standard deviation (-3 to 3) multiple the label by the standard deviation, and then add that to the mean (ex: [(5 x -3) + 20 = 5] so 3 standard deviations below the mean would be 5<br /> <br /> To identify what the percentile rank exists at each standard deviation <br /> 1. Use the above process to label the mean and standard deviations on the normal curve <br /> 2. Add together the percentage of scores that exist to the left of that data point<br /> <br /> To identify the percentage scoring between x and y score <br /> 1. Use the above process to label the mean and standard deviations on the normal curve <br /> 2. Identify what standard deviation x score aligns with what standard deviation y score aligns with <br /> 3. Add together all percentage of scores that exist between the two data points<br /> <br /> this is only to be used if score x and y fall on a standard deviation**</div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=Main_Page&diff=114 Main Page 2019-10-29T17:44:39Z

<p>Aasulliv1: /* Modules */</p> <hr /> <div><big>'''Practical Statistics for Educators'''</big><br /> edited and maintained by Frank LaBanca, EdD<br /> <br /> ----<br /> <br /> <br /> == Philosophy ==<br /> <br /> <br /> Quantitative statistical analyses can be intimidating for many educators pursing an advanced academic degree. The thought of computational math can sometimes trigger unwarranted fears. <br /> <br /> Here, we approach statistics from a straightforward conceptually-based perspective. Our goal is to collaborate and provide insight for statistics that make them meaningful tools in the educational arena.<br /> <br /> Each "module" corresponds with the topics presented each week, and will expand as the course progresses. A topical outline can be found @ [http://docs.google.com/Doc?id=dfqvtcqp_46hhzzcsgt ]<br /> <br /> Comments and edits are welcome and encouraged! Please give yourself credit as you contribute. At the end of a section you insert please add the following in italics:<br /> ''contributed by <your name>''<br /> If you are modifying content, add the following under the contribution line:<br /> ''modified by <your name>'' We are glad to accept as many modifications as necessary to give the most meaning to each section. As we asynchronously socially construct knowledge together, we can recognize the accomplishments and contributions of each writer.<br /> <br /> ''contributed by Frank LaBanca, EdD''<br /> <br /> == Contributions ==<br /> <br /> Our contributors [[contributions here]].<br /> <br /> Please submit your contribution at [https://forms.gle/PBaVrxFffbk5CKgg8]<br /> <br /> == Modules ==<br /> <br /> 1.1 [[The Greek Alphabet]] and its significance in statistics<br /> <br /> 1.2 An introduction to probability PowerPoint @[http://docs.google.com/Presentation?id=dfqvtcqp_97wcrbtsn]<br /> <br /> <br /> 2.1 [[Types of Data]]<br /> <br /> 2.2 [[Visualizing Data]]<br /> <br /> 2.3 Visually representing data PowerPoint @ [http://docs.google.com/Presentation?docid=dfqvtcqp_27dwth2zz2#]<br /> <br /> 2.3.1 Table 2 from LaBanca dissertation @ [http://docs.google.com/Doc?id=dfqvtcqp_25cb5pqcfw]<br /> <br /> 2.3.2 Cool graph of movie box office from NY Times [http://www.nytimes.com/interactive/2008/02/23/movies/20080223_REVENUE_GRAPHIC.html#]<br /> <br /> 2.3.4 [[Histograms]]<br /> <br /> 2.3.5 Scatterplots YouTube @ [http://youtu.be/HFuU1uxJ1tQ]<br /> <br /> 2.4 [[Shapes of distribution]]<br /> <br /> 2.5 Survey of Attitudes Toward Statistics (SATS) Data Set @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7PokF_oAoCRUg]<br /> <br /> <br /> <br /> 3.1 [[Central Tendency]]<br /> <br /> 3.1.1 Central Tendency and Normal Distribution PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_47dfhfr4nw ]<br /> <br /> 3.1.2 Central Tendency YouTube @ [http://youtu.be/Fn4z8RDpwDY]<br /> <br /> 3.2 [[Interquartile ranges]]<br /> <br /> 3.2.1 [[The Box Plot]]<br /> <br /> 3.3 [[Standard deviation]]<br /> <br /> 3.3.1 [[Identifying percentile ranks and scores based on standard deviation]]<br /> <br /> 3.3.1.a [[Practice Identifying percentile ranks and scores based on standard deviation]]<br /> <br /> 3.4 [[z-scores]]<br /> <br /> <br /> <br /> 4.1 [[Percentile Rank]]<br /> 4.1.1 Areas under the standard normal curve for z values @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7NstEjJ40jJOQ]<br /> <br /> 4.1.2 z scores corresponding to divisions of the area under the normal curve @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7P6SVNiBdWbEg]<br /> <br /> 4.2 Conversion of data PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_98chfzg2tf]<br /> <br /> 4.2.1 Descriptive analysis of USRT data @ [http://docs.google.com/Doc?id=dfqvtcqp_61db6smpdm ]<br /> <br /> 4.3 [[Normal Curve Equivalent scores]]<br /> <br /> 4.3 [[Standard Error of Measurement]]<br /> <br /> 4.4 [[Confidence Intervals]]<br /> <br /> 4.4 z score machine @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7N8oLJZwmr3Zw]<br /> <br /> <br /> <br /> 5.1 [[Pearson r]]<br /> <br /> 5.2 [[Rules of thumb for interpreting the size of a correlation coefficient]]<br /> <br /> 5.3 Critical values for the correlation coefficient @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7MkuRpIgceTRQ]<br /> <br /> 5.4 [[Spearman rho]]<br /> <br /> 5.5 Correlation PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_134hsxg7td7]<br /> <br /> 5.6 [[Writing samples for correlations]]<br /> <br /> <br /> 6.1 [[Inferential Statistics Definition]]<br /> <br /> 6.2 [[Sampling]]<br /> <br /> 6.3 [[Sampling distributions]] <br /> <br /> 6.4 t test PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_153gr9f3hgd]<br /> <br /> 6.5 Sample data set @ [http://wolfweb.unr.edu/homepage/liu/stat/help/help.htm]<br /> <br /> 6.6 Critical values for t @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7OZzyZeHg9MIA]<br /> <br /> <br /> 7.1 [[Effect size]]<br /> <br /> 7.1.1 Effect size calculator @ http://www.campbellcollaboration.org/resources/effect_size_input.php<br /> <br /> 7.1.2 [[Rules of thumb for interpreting effect sizes]]<br /> <br /> 7.2 Effect size PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_204f42f67dx ]<br /> <br /> 7.3 [[Hypothesis testing]]<br /> <br /> 7.4 Hypothesis testing PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_23295tm65dx]<br /> <br /> 7.5.1 Hypothesis testing template for a correlation @ [http://docs.google.com/Doc?id=dfqvtcqp_174ccchz4ds]<br /> <br /> 7.5.2 Hypothesis testing template for a t test @ [http://docs.google.com/Doc?id=dfqvtcqp_175hjcdsjff]<br /> <br /> <br /> 8.1 [[Type I and Type II Errors]]<br /> <br /> 8.2 Type I and Type II Errors PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_258wcq56rdv]<br /> <br /> 8.3 [[Levene's p versus the test statistic p]]<br /> <br /> 8.4 [[Analysis of Variance]]<br /> <br /> 8.5 ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_265ckd9j9dv]<br /> <br /> 8.6 [[ANOVA Case study]]<br /> <br /> 8.7 Critical values for the F statistic @ [http://www.sussex.ac.uk/Users/grahamh/RM1web/F-ratio%20table%202005.pdf]<br /> <br /> 8.7 [[Rules of thumb for interpreting effect sizes of ANOVAs]]<br /> <br /> <br /> 9.1 Post Hoc test PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_284dbhghdc9]<br /> <br /> 9.2 [[Selecting a Post Hoc test]]<br /> <br /> 9.3 Hypothesis testing template for ANOVA @ [http://docs.google.com/Doc?id=dfqvtcqp_295ckngxdgj]<br /> <br /> <br /> 10.1 [[Chi square]]<br /> <br /> 10.2 [[Example for calculating chi square]]<br /> <br /> 10.3 Critical values for chi square @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7OhBVHQWoHTIQ]<br /> <br /> 10.4 [[Chi square analysis description/sample writing]]<br /> <br /> 10.5 Chi square PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_297dhg685g8]<br /> <br /> <br /> 11.1 [[Beyond the ANOVA]]<br /> <br /> 11.2 Beyond ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_307cmrtpxg3]<br /> <br /> 11.3 2-way ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_325hrt86ggt]<br /> <br /> 11.4 2-way ANOVA template @ [http://docs.google.com/Doc?id=dfqvtcqp_372599pr6w6]<br /> <br /> <br /> 12.1 [[MANOVA]]<br /> <br /> 12.2 [[Homogeneity vs Homoscedacity]] (Levene vs Box's M)<br /> <br /> 12.3 [[Post Hoc ANOVAs for MANOVA]] (univariate)<br /> <br /> 12.4 [[Post Hoc Discriminant Analysis]] (multivariate)<br /> <br /> 12.5 [[Covariates]]<br /> <br /> 12.6 [[MANCOVA]]<br /> <br /> <br /> 13.1 [[Multiple Linear Regression]]<br /> <br /> <br /> 14.1 Internal Consistency[https://docs.google.com/document/d/18K16I8u9sbwpUhW9nIENF4x9wDXAy-4wFuBp6a0AljA/edit]<br /> <br /> 14.2 Cronbach's Alpha[https://docs.google.com/document/d/1_eyXOcFrBcDSctM27a9T2kUlx9D8TidV_YTHk-wvTu0/edit]<br /> <br /> 14.2.1 [[Cronbach's Alpha Values]]<br /> <br /> == Applied Research Designs ==<br /> <br /> 15.1 [[Instrumentation]]<br /> <br /> 15.2 [[Limitations]]<br /> <br /> 15.3 [[Practice determining the stat]]<br /> <br /> == Getting started ==<br /> * [http://www.mediawiki.org/wiki/Manual:Configuration_settings Configuration settings list]<br /> * [http://www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]<br /> * [http://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]</div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=File:Normal.jpg&diff=113 File:Normal.jpg 2019-10-29T17:40:41Z

<p>Aasulliv1: Normal Curve</p> <hr /> <div>Normal Curve</div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=Practice_identifying_percentile_ranks_and_scores_based_on_standard_deviation&diff=112 Practice identifying percentile ranks and scores based on standard deviation 2019-10-29T17:38:07Z

<p>Aasulliv1: Created page with "When looking at the normal curve, a predictable percentage of data falls within 1, 2, and 3 standard deviations from the mean. File:Normal.jpg '''To identify the values o..."</p> <hr /> <div>When looking at the normal curve, a predictable percentage of data falls within 1, 2, and 3 standard deviations from the mean. [[File:Normal.jpg]]<br /> <br /> '''To identify the values of data points at each standard deviation'''<br /> 1. Identify the mean and the standard deviation (ex: M=80; SD = 5)<br /> 2. Label the curve beginning with the mean at the 0 value. To identify the data value at each standard deviation (-3 to 3) multiple the label by the standard deviation, and then add that to the mean (ex: [(5 x -3) + 20 = 5] so 3 standard deviations below the mean would be 5<br /> <br /> '''To identify what the percentile rank exists at each standard deviation'''<br /> 1. Use the above process to label the mean and standard deviations on the normal curve<br /> 2. Add together the percentage of scores that exist to the left of that data point<br /> <br /> '''To identify the percentage scoring between x and y score'''<br /> 1. Use the above process to label the mean and standard deviations on the normal curve<br /> 2. Identify what standard deviation x score aligns with what standard deviation y score aligns with<br /> 3. Add together all percentage of scores that exist between the two data points<br /> **this is only to be used if score x and y fall on a standard deviation**</div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=Main_Page&diff=111 Main Page 2019-10-29T17:24:22Z

<p>Aasulliv1: /* Modules */</p> <hr /> <div><big>'''Practical Statistics for Educators'''</big><br /> edited and maintained by Frank LaBanca, EdD<br /> <br /> ----<br /> <br /> <br /> == Philosophy ==<br /> <br /> <br /> Quantitative statistical analyses can be intimidating for many educators pursing an advanced academic degree. The thought of computational math can sometimes trigger unwarranted fears. <br /> <br /> Here, we approach statistics from a straightforward conceptually-based perspective. Our goal is to collaborate and provide insight for statistics that make them meaningful tools in the educational arena.<br /> <br /> Each "module" corresponds with the topics presented each week, and will expand as the course progresses. A topical outline can be found @ [http://docs.google.com/Doc?id=dfqvtcqp_46hhzzcsgt ]<br /> <br /> Comments and edits are welcome and encouraged! Please give yourself credit as you contribute. At the end of a section you insert please add the following in italics:<br /> ''contributed by <your name>''<br /> If you are modifying content, add the following under the contribution line:<br /> ''modified by <your name>'' We are glad to accept as many modifications as necessary to give the most meaning to each section. As we asynchronously socially construct knowledge together, we can recognize the accomplishments and contributions of each writer.<br /> <br /> ''contributed by Frank LaBanca, EdD''<br /> <br /> == Contributions ==<br /> <br /> Our contributors [[contributions here]].<br /> <br /> Please submit your contribution at [https://forms.gle/PBaVrxFffbk5CKgg8]<br /> <br /> == Modules ==<br /> <br /> 1.1 [[The Greek Alphabet]] and its significance in statistics<br /> <br /> 1.2 An introduction to probability PowerPoint @[http://docs.google.com/Presentation?id=dfqvtcqp_97wcrbtsn]<br /> <br /> <br /> 2.1 [[Types of Data]]<br /> <br /> 2.2 [[Visualizing Data]]<br /> <br /> 2.3 Visually representing data PowerPoint @ [http://docs.google.com/Presentation?docid=dfqvtcqp_27dwth2zz2#]<br /> <br /> 2.3.1 Table 2 from LaBanca dissertation @ [http://docs.google.com/Doc?id=dfqvtcqp_25cb5pqcfw]<br /> <br /> 2.3.2 Cool graph of movie box office from NY Times [http://www.nytimes.com/interactive/2008/02/23/movies/20080223_REVENUE_GRAPHIC.html#]<br /> <br /> 2.3.4 [[Histograms]]<br /> <br /> 2.3.5 Scatterplots YouTube @ [http://youtu.be/HFuU1uxJ1tQ]<br /> <br /> 2.4 [[Shapes of distribution]]<br /> <br /> 2.5 Survey of Attitudes Toward Statistics (SATS) Data Set @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7PokF_oAoCRUg]<br /> <br /> <br /> <br /> 3.1 [[Central Tendency]]<br /> <br /> 3.1.1 Central Tendency and Normal Distribution PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_47dfhfr4nw ]<br /> <br /> 3.1.2 Central Tendency YouTube @ [http://youtu.be/Fn4z8RDpwDY]<br /> <br /> 3.2 [[Interquartile ranges]]<br /> <br /> 3.2.1 [[The Box Plot]]<br /> <br /> 3.3 [[Standard deviation]]<br /> <br /> 3.3.1 [[Practice identifying percentile ranks and scores based on standard deviation]]<br /> <br /> 3.4 [[z-scores]]<br /> <br /> <br /> <br /> 4.1 [[Percentile Rank]]<br /> 4.1.1 Areas under the standard normal curve for z values @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7NstEjJ40jJOQ]<br /> <br /> 4.1.2 z scores corresponding to divisions of the area under the normal curve @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7P6SVNiBdWbEg]<br /> <br /> 4.2 Conversion of data PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_98chfzg2tf]<br /> <br /> 4.2.1 Descriptive analysis of USRT data @ [http://docs.google.com/Doc?id=dfqvtcqp_61db6smpdm ]<br /> <br /> 4.3 [[Normal Curve Equivalent scores]]<br /> <br /> 4.3 [[Standard Error of Measurement]]<br /> <br /> 4.4 [[Confidence Intervals]]<br /> <br /> 4.4 z score machine @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7N8oLJZwmr3Zw]<br /> <br /> <br /> <br /> 5.1 [[Pearson r]]<br /> <br /> 5.2 [[Rules of thumb for interpreting the size of a correlation coefficient]]<br /> <br /> 5.3 Critical values for the correlation coefficient @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7MkuRpIgceTRQ]<br /> <br /> 5.4 [[Spearman rho]]<br /> <br /> 5.5 Correlation PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_134hsxg7td7]<br /> <br /> 5.6 [[Writing samples for correlations]]<br /> <br /> <br /> 6.1 [[Inferential Statistics Definition]]<br /> <br /> 6.2 [[Sampling]]<br /> <br /> 6.3 [[Sampling distributions]] <br /> <br /> 6.4 t test PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_153gr9f3hgd]<br /> <br /> 6.5 Sample data set @ [http://wolfweb.unr.edu/homepage/liu/stat/help/help.htm]<br /> <br /> 6.6 Critical values for t @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7OZzyZeHg9MIA]<br /> <br /> <br /> 7.1 [[Effect size]]<br /> <br /> 7.1.1 Effect size calculator @ http://www.campbellcollaboration.org/resources/effect_size_input.php<br /> <br /> 7.1.2 [[Rules of thumb for interpreting effect sizes]]<br /> <br /> 7.2 Effect size PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_204f42f67dx ]<br /> <br /> 7.3 [[Hypothesis testing]]<br /> <br /> 7.4 Hypothesis testing PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_23295tm65dx]<br /> <br /> 7.5.1 Hypothesis testing template for a correlation @ [http://docs.google.com/Doc?id=dfqvtcqp_174ccchz4ds]<br /> <br /> 7.5.2 Hypothesis testing template for a t test @ [http://docs.google.com/Doc?id=dfqvtcqp_175hjcdsjff]<br /> <br /> <br /> 8.1 [[Type I and Type II Errors]]<br /> <br /> 8.2 Type I and Type II Errors PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_258wcq56rdv]<br /> <br /> 8.3 [[Levene's p versus the test statistic p]]<br /> <br /> 8.4 [[Analysis of Variance]]<br /> <br /> 8.5 ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_265ckd9j9dv]<br /> <br /> 8.6 [[ANOVA Case study]]<br /> <br /> 8.7 Critical values for the F statistic @ [http://www.sussex.ac.uk/Users/grahamh/RM1web/F-ratio%20table%202005.pdf]<br /> <br /> 8.7 [[Rules of thumb for interpreting effect sizes of ANOVAs]]<br /> <br /> <br /> 9.1 Post Hoc test PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_284dbhghdc9]<br /> <br /> 9.2 [[Selecting a Post Hoc test]]<br /> <br /> 9.3 Hypothesis testing template for ANOVA @ [http://docs.google.com/Doc?id=dfqvtcqp_295ckngxdgj]<br /> <br /> <br /> 10.1 [[Chi square]]<br /> <br /> 10.2 [[Example for calculating chi square]]<br /> <br /> 10.3 Critical values for chi square @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7OhBVHQWoHTIQ]<br /> <br /> 10.4 [[Chi square analysis description/sample writing]]<br /> <br /> 10.5 Chi square PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_297dhg685g8]<br /> <br /> <br /> 11.1 [[Beyond the ANOVA]]<br /> <br /> 11.2 Beyond ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_307cmrtpxg3]<br /> <br /> 11.3 2-way ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_325hrt86ggt]<br /> <br /> 11.4 2-way ANOVA template @ [http://docs.google.com/Doc?id=dfqvtcqp_372599pr6w6]<br /> <br /> <br /> 12.1 [[MANOVA]]<br /> <br /> 12.2 [[Homogeneity vs Homoscedacity]] (Levene vs Box's M)<br /> <br /> 12.3 [[Post Hoc ANOVAs for MANOVA]] (univariate)<br /> <br /> 12.4 [[Post Hoc Discriminant Analysis]] (multivariate)<br /> <br /> 12.5 [[Covariates]]<br /> <br /> 12.6 [[MANCOVA]]<br /> <br /> <br /> 13.1 [[Multiple Linear Regression]]<br /> <br /> <br /> 14.1 Internal Consistency[https://docs.google.com/document/d/18K16I8u9sbwpUhW9nIENF4x9wDXAy-4wFuBp6a0AljA/edit]<br /> <br /> 14.2 Cronbach's Alpha[https://docs.google.com/document/d/1_eyXOcFrBcDSctM27a9T2kUlx9D8TidV_YTHk-wvTu0/edit]<br /> <br /> 14.2.1 [[Cronbach's Alpha Values]]<br /> <br /> == Applied Research Designs ==<br /> <br /> 15.1 [[Instrumentation]]<br /> <br /> 15.2 [[Limitations]]<br /> <br /> 15.3 [[Practice determining the stat]]<br /> <br /> == Getting started ==<br /> * [http://www.mediawiki.org/wiki/Manual:Configuration_settings Configuration settings list]<br /> * [http://www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]<br /> * [http://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]</div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=Main_Page&diff=110 Main Page 2019-10-29T17:21:52Z

<p>Aasulliv1: /* Modules */</p> <hr /> <div><big>'''Practical Statistics for Educators'''</big><br /> edited and maintained by Frank LaBanca, EdD<br /> <br /> ----<br /> <br /> <br /> == Philosophy ==<br /> <br /> <br /> Quantitative statistical analyses can be intimidating for many educators pursing an advanced academic degree. The thought of computational math can sometimes trigger unwarranted fears. <br /> <br /> Here, we approach statistics from a straightforward conceptually-based perspective. Our goal is to collaborate and provide insight for statistics that make them meaningful tools in the educational arena.<br /> <br /> Each "module" corresponds with the topics presented each week, and will expand as the course progresses. A topical outline can be found @ [http://docs.google.com/Doc?id=dfqvtcqp_46hhzzcsgt ]<br /> <br /> Comments and edits are welcome and encouraged! Please give yourself credit as you contribute. At the end of a section you insert please add the following in italics:<br /> ''contributed by <your name>''<br /> If you are modifying content, add the following under the contribution line:<br /> ''modified by <your name>'' We are glad to accept as many modifications as necessary to give the most meaning to each section. As we asynchronously socially construct knowledge together, we can recognize the accomplishments and contributions of each writer.<br /> <br /> ''contributed by Frank LaBanca, EdD''<br /> <br /> == Contributions ==<br /> <br /> Our contributors [[contributions here]].<br /> <br /> Please submit your contribution at [https://forms.gle/PBaVrxFffbk5CKgg8]<br /> <br /> == Modules ==<br /> <br /> 1.1 [[The Greek Alphabet]] and its significance in statistics<br /> <br /> 1.2 An introduction to probability PowerPoint @[http://docs.google.com/Presentation?id=dfqvtcqp_97wcrbtsn]<br /> <br /> <br /> 2.1 [[Types of Data]]<br /> <br /> 2.2 [[Visualizing Data]]<br /> <br /> 2.3 Visually representing data PowerPoint @ [http://docs.google.com/Presentation?docid=dfqvtcqp_27dwth2zz2#]<br /> <br /> 2.3.1 Table 2 from LaBanca dissertation @ [http://docs.google.com/Doc?id=dfqvtcqp_25cb5pqcfw]<br /> <br /> 2.3.2 Cool graph of movie box office from NY Times [http://www.nytimes.com/interactive/2008/02/23/movies/20080223_REVENUE_GRAPHIC.html#]<br /> <br /> 2.3.4 [[Histograms]]<br /> <br /> 2.3.5 Scatterplots YouTube @ [http://youtu.be/HFuU1uxJ1tQ]<br /> <br /> 2.4 [[Shapes of distribution]]<br /> <br /> 2.5 Survey of Attitudes Toward Statistics (SATS) Data Set @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7PokF_oAoCRUg]<br /> <br /> <br /> <br /> 3.1 [[Central Tendency]]<br /> <br /> 3.1.1 Central Tendency and Normal Distribution PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_47dfhfr4nw ]<br /> <br /> 3.1.2 Central Tendency YouTube @ [http://youtu.be/Fn4z8RDpwDY]<br /> <br /> 3.2 [[Interquartile ranges]]<br /> <br /> 3.2.1 [[The Box Plot]]<br /> <br /> 3.3 [[Standard deviation]]<br /> <br /> 3.4 [[z-scores]]<br /> <br /> <br /> <br /> 4.1 [[Percentile Rank]]<br /> 4.1.1 Areas under the standard normal curve for z values @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7NstEjJ40jJOQ]<br /> <br /> 4.1.2 z scores corresponding to divisions of the area under the normal curve @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7P6SVNiBdWbEg]<br /> <br /> 4.1.3 [[Percentile Rank Practice Questions]]<br /> <br /> 4.2 Conversion of data PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_98chfzg2tf]<br /> <br /> 4.2.1 Descriptive analysis of USRT data @ [http://docs.google.com/Doc?id=dfqvtcqp_61db6smpdm ]<br /> <br /> 4.3 [[Normal Curve Equivalent scores]]<br /> <br /> 4.3 [[Standard Error of Measurement]]<br /> <br /> 4.4 [[Confidence Intervals]]<br /> <br /> 4.4 z score machine @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7N8oLJZwmr3Zw]<br /> <br /> <br /> <br /> 5.1 [[Pearson r]]<br /> <br /> 5.2 [[Rules of thumb for interpreting the size of a correlation coefficient]]<br /> <br /> 5.3 Critical values for the correlation coefficient @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7MkuRpIgceTRQ]<br /> <br /> 5.4 [[Spearman rho]]<br /> <br /> 5.5 Correlation PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_134hsxg7td7]<br /> <br /> 5.6 [[Writing samples for correlations]]<br /> <br /> <br /> 6.1 [[Inferential Statistics Definition]]<br /> <br /> 6.2 [[Sampling]]<br /> <br /> 6.3 [[Sampling distributions]] <br /> <br /> 6.4 t test PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_153gr9f3hgd]<br /> <br /> 6.5 Sample data set @ [http://wolfweb.unr.edu/homepage/liu/stat/help/help.htm]<br /> <br /> 6.6 Critical values for t @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7OZzyZeHg9MIA]<br /> <br /> <br /> 7.1 [[Effect size]]<br /> <br /> 7.1.1 Effect size calculator @ http://www.campbellcollaboration.org/resources/effect_size_input.php<br /> <br /> 7.1.2 [[Rules of thumb for interpreting effect sizes]]<br /> <br /> 7.2 Effect size PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_204f42f67dx ]<br /> <br /> 7.3 [[Hypothesis testing]]<br /> <br /> 7.4 Hypothesis testing PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_23295tm65dx]<br /> <br /> 7.5.1 Hypothesis testing template for a correlation @ [http://docs.google.com/Doc?id=dfqvtcqp_174ccchz4ds]<br /> <br /> 7.5.2 Hypothesis testing template for a t test @ [http://docs.google.com/Doc?id=dfqvtcqp_175hjcdsjff]<br /> <br /> <br /> 8.1 [[Type I and Type II Errors]]<br /> <br /> 8.2 Type I and Type II Errors PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_258wcq56rdv]<br /> <br /> 8.3 [[Levene's p versus the test statistic p]]<br /> <br /> 8.4 [[Analysis of Variance]]<br /> <br /> 8.5 ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_265ckd9j9dv]<br /> <br /> 8.6 [[ANOVA Case study]]<br /> <br /> 8.7 Critical values for the F statistic @ [http://www.sussex.ac.uk/Users/grahamh/RM1web/F-ratio%20table%202005.pdf]<br /> <br /> 8.7 [[Rules of thumb for interpreting effect sizes of ANOVAs]]<br /> <br /> <br /> 9.1 Post Hoc test PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_284dbhghdc9]<br /> <br /> 9.2 [[Selecting a Post Hoc test]]<br /> <br /> 9.3 Hypothesis testing template for ANOVA @ [http://docs.google.com/Doc?id=dfqvtcqp_295ckngxdgj]<br /> <br /> <br /> 10.1 [[Chi square]]<br /> <br /> 10.2 [[Example for calculating chi square]]<br /> <br /> 10.3 Critical values for chi square @ [http://spreadsheets.google.com/pub?key=pmUxljSzLg7OhBVHQWoHTIQ]<br /> <br /> 10.4 [[Chi square analysis description/sample writing]]<br /> <br /> 10.5 Chi square PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_297dhg685g8]<br /> <br /> <br /> 11.1 [[Beyond the ANOVA]]<br /> <br /> 11.2 Beyond ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_307cmrtpxg3]<br /> <br /> 11.3 2-way ANOVA PowerPoint @ [http://docs.google.com/Presentation?id=dfqvtcqp_325hrt86ggt]<br /> <br /> 11.4 2-way ANOVA template @ [http://docs.google.com/Doc?id=dfqvtcqp_372599pr6w6]<br /> <br /> <br /> 12.1 [[MANOVA]]<br /> <br /> 12.2 [[Homogeneity vs Homoscedacity]] (Levene vs Box's M)<br /> <br /> 12.3 [[Post Hoc ANOVAs for MANOVA]] (univariate)<br /> <br /> 12.4 [[Post Hoc Discriminant Analysis]] (multivariate)<br /> <br /> 12.5 [[Covariates]]<br /> <br /> 12.6 [[MANCOVA]]<br /> <br /> <br /> 13.1 [[Multiple Linear Regression]]<br /> <br /> <br /> 14.1 Internal Consistency[https://docs.google.com/document/d/18K16I8u9sbwpUhW9nIENF4x9wDXAy-4wFuBp6a0AljA/edit]<br /> <br /> 14.2 Cronbach's Alpha[https://docs.google.com/document/d/1_eyXOcFrBcDSctM27a9T2kUlx9D8TidV_YTHk-wvTu0/edit]<br /> <br /> 14.2.1 [[Cronbach's Alpha Values]]<br /> <br /> == Applied Research Designs ==<br /> <br /> 15.1 [[Instrumentation]]<br /> <br /> 15.2 [[Limitations]]<br /> <br /> 15.3 [[Practice determining the stat]]<br /> <br /> == Getting started ==<br /> * [http://www.mediawiki.org/wiki/Manual:Configuration_settings Configuration settings list]<br /> * [http://www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ]<br /> * [http://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]</div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=Contributions_here&diff=109 Contributions here 2019-10-29T17:14:21Z

<p>Aasulliv1: /* Student Contributors */</p> <hr /> <div>== Editor ==<br /> Frank LaBanca, EdD<br /> <br /> == Faculty Contributors ==<br /> Karen Burke, EdD<br /> <br /> Patricia Cosentino, EdD<br /> <br /> Deborah Hardy, EdD<br /> <br /> Jennifer Mitchell, EdD<br /> <br /> == Student Contributors ==<br /> David Bozzuto<br /> <br /> Karen Fildes<br /> <br /> Michael Minzloff<br /> <br /> Damien Holst<br /> <br /> Jennifer Eraca<br /> <br /> John Ryan<br /> <br /> Kara Kunst<br /> <br /> Emily Rhew<br /> <br /> Cassandra Cosentino<br /> <br /> Kristina Hislop<br /> <br /> Mary Fernand<br /> <br /> Thomas Fox<br /> <br /> Helen Knudsen<br /> <br /> Ashley Brooksbank</div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=Central_Tendency&diff=108 Central Tendency 2019-10-29T17:10:30Z

<p>Aasulliv1: </p> <hr /> <div>Central Tendency is commonly referred to as the "measure of central tendency". A measure of central tendency is used to describe a data set by identifying the central position within that set of data. In statistics, the three most commonly used measures of central tendency are mean, median, and mode.<br /> <br /> Mean: The mean is the average of all the numbers within a data set. To find the mean value, one would add up all the values in a data set and divide that sum by the total number of data points within the data set.<br /> Example - 3+4+5+6+7 = 25. There are 5 values in this data set. 25/5 = 5. In this scenario the mean, or average, is 5.<br /> <br /> Median: The median is the middle point in a sorted set of data. The median is identified by organizing the data set into order of magnitude (starting with the smallest number).<br /> Once sorted the median is identified as the number directly in the middle of that sorted data.<br /> Example - 6, 9, 23, 15, 2. If we put this data set in order by magnitude it is displayed as: 2, 6, 9, 15, 23. In this data set 9 is the median.<br /> <br /> Mode: The mode is the number that occurs most frequently in a data set.<br /> Example - 2, 3, 15, 3, 5, 7, 8, 3, 2, 1, 10, 9. <br /> In this data set, 3, is the number that occurs most frequently and would be identified as the mode.<br /> <br /> ''contributed by Scott Trungadi''<br /> <br /> <br /> ''Having finally mastered the skills necessary to report my data, and thanks to the help from fellow students in my doctoral program, I was ready to write up a description of what all the numbers meant. I was excited to have reached this point in my central tendency assignment, as there is one thing I love doing, and that is write. Finally, something I might be good at! However, this also meant that I needed to understand and be able to explain what all the numbers meant.''<br /> <br /> In October of 2007 during the kindergarten year, the Letter Naming Fluency<br /> ( LNF) portion of the Dynamic Indicators of Basic Early Literacy Skills (Dibels) was administered to a class of 18 kindergarten students, 10 males and 8 females.<br /> The SPSS helped to process the following data regarding the testing administration. The mean score for the eighteen students for the Beginning LNF portion of Dibels was 32.33 with the median or midpoint being 35.0(Table 1). By gender, the boys in the class scored slightly lower with the mean score of 32.30 and the midpoint or median score of 34.50 as compared to the girls’ mean score of 32.38 and a median of 37.00(Table 5).<br /> <br /> The standard deviation is based on all the scores in the group and is determined by how much each score deviates from the mean, or in other words, it is an estimate of what the range of scores probably was. The standard deviation tells me that in the Beginning and Ending LNF administration, all students tested, in a similar range-16.01 and 16.87(Tables 1 and 2).<br /> However, in looking at the Beginning LNF scores analyzed by gender, there is a large discrepancy between how well the boys did when compared to the girls. There was a higher standard of deviation for the boys than the girls, respectively 16.34 for the boys and 6.71 for the girls. In trying to understand the possible reasons for this, one must consider birthdates. Although birthdates were not considered in this collection of data, it is important to note that there was a higher incidence of younger birth dates for the boys than the girls which might account for this wide range in the boys’ scores.<br /> <br /> The Z scores in this statistical analysis refer to how many standard deviations a particular raw score lies above or below the group means. Table 6 indicates the range of Z scores for the students who had taken the Ending LNF portion of the Dibels test. The score range from 1.44, or 1.44 standard deviations above the group mean of 64.67 to -1.82, or 1.82 below the group mean of 64.67.<br /> <br /> <br /> ''I actually felt that I began to really understand what everything meant as I was scripting my report. It was very helpful. Hope this helps someone!''<br /> <br /> <br /> ''contributed by Debbie Mumford''<br /> <br /> ----<br /> <br /> Skewness and Central Tendency<br /> [[File:Skeweness.jpg]]<br /> <br /> Identifying where the mean, median, and mode are in relation to each other can help to determine the skew of a curve. <br /> <br /> In a normal distribution, the mean, median, and mode will all be very close (mean = median = mode)<br /> <br /> In a distribution that is skewed ''left'', otherwise known as a negatively skewed distribution, it is outliers on the lower end of the number line that is impacting the shape of the curve. In a curve that is skewed left the mean (which is the mathematical average) will be furthest to the left on the number line, the median remains at the mid point of the distribution on the number line, and the mode will be the farthest point to the right. <br /> <br /> In a distribution that is skewed ''right'', otherwise known as a positively skewed distribution, it is outliers on the higher end of the number line that is impacting the shape of the curve. In a curve that is skewed right the mean (which is the mathematical average) will be furthest to the right on the number line, the median remains at the mid point of the distribution on the number line, and the mode will be the farthest point to the left. <br /> <br /> ''Contributed by Ashley Brooksbank'<br /> image citation: Statistics: Basic Statistics II. (n.d.). Retrieved from ''https://guides.douglascollege.ca/c.php?g=408742&p=2970198.'''</div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=File:Skeweness.jpg&diff=107 File:Skeweness.jpg 2019-10-29T16:58:58Z

<p>Aasulliv1: Mean Median and Mode in skewed distributions Taken from https://guides.douglascollege.ca/c.php?g=408742&p=2970198</p> <hr /> <div>Mean Median and Mode in skewed distributions Taken from https://guides.douglascollege.ca/c.php?g=408742&p=2970198</div>

Aasulliv1 http://practicalstats.labanca.net/index.php?title=File:Kurtosis.jpg&diff=106 File:Kurtosis.jpg 2019-10-29T16:57:59Z

<p>Aasulliv1: Kurtosis curves Taken from https://guides.douglascollege.ca/c.php?g=408742&p=2970198</p> <hr /> <div>Kurtosis curves Taken from https://guides.douglascollege.ca/c.php?g=408742&p=2970198</div>

Aasulliv1