How Is Your Productivity Affected Based On Your App Usage?, 2017 Chapman University

#### How Is Your Productivity Affected Based On Your App Usage?, Colette Noghreian

*Student Research Day Abstracts and Posters*

As technology becomes more prominent in society, it is crucial to investigate its effect on day to day life. The purpose of this study is to determine how the amount of time spent on iPhone applications affects how productive students feel in the span of one week. Results are tested through a survey which first determines general information about the student, and then guides students to navigate their phone settings and record the battery usage of the top three applications which use up the most battery. It is hypothesized that productivity decreases as battery usage increases due to the substantial ...

Making Models With Bayes, 2017 California State University, San Bernardino

#### Making Models With Bayes, Pilar Olid

*Electronic Theses, Projects, and Dissertations*

Bayesian statistics is an important approach to modern statistical analyses. It allows us to use our prior knowledge of the unknown parameters to construct a model for our data set. The foundation of Bayesian analysis is Bayes' Rule, which in its proportional form indicates that the posterior is proportional to the prior times the likelihood. We will demonstrate how we can apply Bayesian statistical techniques to fit a linear regression model and a hierarchical linear regression model to a data set. We will show how to apply different distributions to Bayesian analyses and how the use of a prior affects ...

On Variance Balanced Designs, 2017 Saurashtra University Rajkot, Gujarat, India

#### On Variance Balanced Designs, Dilip Kumar Ghosh, Sangeeta Ahuja

*Journal of Modern Applied Statistical Methods*

Balanced incomplete block designs are not always possible to construct because of their parametric relations. In such a situation another balanced design, the variance balanced design, is required. This construction of binary, equal replicated variance balanced designs are discussed using the half fraction of the 2n factorial designs with smaller block sizes. This method was also extended to construct another variance balanced design by deleting the last block of the resulting variance balanced designs. Its efficiency factor compared with randomized block designs was compared and found to be highly efficient.

Unit Root Test For Panel Data Ar(1) Time Series Model With Linear Time Trend And Augmentation Term: A Bayesian Approach, 2017 Central University of Rajasthan, India

#### Unit Root Test For Panel Data Ar(1) Time Series Model With Linear Time Trend And Augmentation Term: A Bayesian Approach, Jitendra Kumar, Anoop Chaturvedi, Umme Afifa, Shafat Yousuf, Saurabh Kumar

*Journal of Modern Applied Statistical Methods*

The univariate time series models, in the case of unit root hypothesis, are more biased towards the acceptance of the Unit Root Hypothesis especially in a short time span. However, the panel data time series model is more appropriate in such situation. The Bayesian analysis of unit root testing for a panel data time series model is considered. An autoregressive panel data AR(1) model with linear time trend and augmentation term has been considered and derived the posterior odds ratio for testing the presence of unit root hypothesis under appropriate prior assumptions. A simulation study and real data analysis ...

Performance Evaluation Of Confidence Intervals For Ordinal Coefficient Alpha, 2017 Dallas Independent School District

#### Performance Evaluation Of Confidence Intervals For Ordinal Coefficient Alpha, Heather J. Turner, Prathiba Natesan, Robin K. Henson

*Journal of Modern Applied Statistical Methods*

The aim of this study was to investigate the performance of the Fisher, Feldt, Bonner, and Hakstian and Whalen (HW) confidence intervals methods for the non-parametric reliability estimate, ordinal alpha. All methods yielded unacceptably low coverage rates and potentially increased Type-I error rates.

'Parallel Universe' Or 'Proven Future'? The Language Of Dependent Means T-Test Interpretations, 2017 Université Laval, Quebec City

#### 'Parallel Universe' Or 'Proven Future'? The Language Of Dependent Means T-Test Interpretations, Anthony M. Gould, Jean-Etienne Joullié

*Journal of Modern Applied Statistical Methods*

Of the three kinds of two-mean comparisons which judge a test statistic against a critical value taken from a Student t-distribution, one – the repeated measures or dependent-means application – is distinctive because it is meant to assess the value of a parameter which is not part of the natural order. This absence forces a choice between two interpretations of a significant test result and the meaning of the test hypothesis. The parallel universe view advances a conditional, backward-looking conclusion. The more practical proven future interpretation is a non-conditional proposition about what will happen if an intervention is (now) applied to each ...

Power And Sample Size Estimation For Nonparametric Composite Endpoints: Practical Implementation Using Data Simulations, 2017 University of Alberta, Edmonton

#### Power And Sample Size Estimation For Nonparametric Composite Endpoints: Practical Implementation Using Data Simulations, Paul M. Brown, Justin A. Ezekowitz

*Journal of Modern Applied Statistical Methods*

Composite endpoints are a popular outcome in controlled studies. However, the required sample size is not easily obtained due to the assortment of outcomes, correlations between them and the way in which the composite is constructed. Data simulations are required. A macro is developed that enables sample size and power estimation.

A Monte Carlo Study Of The Effects Of Variability And Outliers On The Linear Correlation Coefficient, 2017 University of Tabuk, Saudi Arabia

#### A Monte Carlo Study Of The Effects Of Variability And Outliers On The Linear Correlation Coefficient, Hussein Yousif Eledum

*Journal of Modern Applied Statistical Methods*

Monte Carlo simulations are used to investigate the effect of two factors, the amount of variability and an outlier, on the size of the Pearson correlation coefficient. Some simulation algorithms are developed, and two theorems for increasing or decreasing the amount of variability are suggested.

Modeling Agreement Between Binary Classifications Of Multiple Raters In R And Sas, 2017 Boston University

#### Modeling Agreement Between Binary Classifications Of Multiple Raters In R And Sas, Aya A. Mitani, Kerrie P. Nelson

*Journal of Modern Applied Statistical Methods*

Cancer screening and diagnostic tests often are classified using a binary outcome such as diseased or not diseased. Recently large-scale studies have been conducted to assess agreement between many raters. Measures of agreement using the class of generalized linear mixed models were implemented efficiently in four recently introduced R and SAS packages in large-scale agreement studies incorporating binary classifications. Simulation studies were conducted to compare the performance across the packages and apply the agreement methods to two cancer studies.

Missing Data In Longitudinal Surveys: A Comparison Of Performance Of Modern Techniques, 2017 University College London

#### Missing Data In Longitudinal Surveys: A Comparison Of Performance Of Modern Techniques, Paola Zaninotto, Amanda Sacker

*Journal of Modern Applied Statistical Methods*

Using a simulation study, the performance of complete case analysis, full information maximum likelihood, multivariate normal imputation, multiple imputation by chained equations and two-fold fully conditional specification to handle missing data were compared in longitudinal surveys with continuous and binary outcomes, missing covariates, and an interaction term.

Detection Of Outliers In Univariate Circular Data Using Robust Circular Distance, 2017 University Putra Malaysia, Serdang, Malaysia

#### Detection Of Outliers In Univariate Circular Data Using Robust Circular Distance, Ehab A. Mahmood, Sohel Rana, Habshah Midi, Abdul Ghapor Hussin

*Journal of Modern Applied Statistical Methods*

A robust statistic to detect single and multi-outliers in univariate circular data is proposed. The performance of the proposed statistic was tested by applying it to a simulation study and to three real data sets, and was demonstrated to be robust.

Jmasm 47: Anova_Hov: A Sas Macro For Testing Homogeneity Of Variance In One-Factor Anova Models (Sas), 2017 University of South Florida, Tampa, FL

#### Jmasm 47: Anova_Hov: A Sas Macro For Testing Homogeneity Of Variance In One-Factor Anova Models (Sas), Isaac Li, Yi-Hsin Chen, Yan Wang, Patricia RodríGuez De Gil, Thanh Pham, Diep Nguyen, Eun Sook Kim, Jeffrey D. Kromrey

*Journal of Modern Applied Statistical Methods*

Variance homogeneity (HOV) is a critical assumption for ANOVA whose violation may lead to perturbations in Type I error rates. Minimal consensus exists on selecting an appropriate test. This SAS macro implements 14 different HOV approaches in one-way ANOVA. Examples are given and practical issues discussed.

Bayesian Hypothesis Testing Of Two Normal Samples Using Bootstrap Prior Technique, 2017 Universiti Tun Hussein Onn Malaysia, Muar, Johor, Malaysia

#### Bayesian Hypothesis Testing Of Two Normal Samples Using Bootstrap Prior Technique, Oyebayo Ridwan Olaniran, Waheed Babatunde Yahya

*Journal of Modern Applied Statistical Methods*

The most important ingredient in Bayesian analysis is prior or prior distribution. A new prior determination method was developed under the framework of parametric empirical Bayes using bootstrap technique. By way of example, Bayesian estimations of the parameters of a normal distribution with unknown mean and unknown variance conditions were considered, as well as its application in comparing the means of two independent normal samples with several scenarios. A Monte Carlo study was conducted to illustrate the proposed procedure in estimation and hypothesis testing. Results from Monte Carlo studies showed that the bootstrap prior proposed is more efficient than the ...

Inferential Procedures For Log Logistic Distribution With Doubly Interval Censored Data, 2017 Universiti Putra Malaysia, Seri Kembangan, Malaysia

#### Inferential Procedures For Log Logistic Distribution With Doubly Interval Censored Data, Yue Fang Loh, Jayanthi Arasan, Habshah Midi, M. R. Abu Bakar

*Journal of Modern Applied Statistical Methods*

The log logistic model with doubly interval censored data is examined. Three methods of constructing confidence interval estimates for the parameter of the model were compared and discussed. The results of the coverage probability study indicated that the Wald outperformed the likelihood ratio and jackknife inferential procedures.

Front Matter, 2017 Wayne State University

The Regression Smoother Lowess: A Confidence Band That Allows Heteroscedasticity And Has Some Specified Simultaneous Probability Coverage, 2017 University of Southern California

#### The Regression Smoother Lowess: A Confidence Band That Allows Heteroscedasticity And Has Some Specified Simultaneous Probability Coverage, Rand Wilcox

*Journal of Modern Applied Statistical Methods*

Many nonparametric regression estimators (smoothers) have been proposed that provide a more flexible method for estimating the true regression line compared to using some of the more obvious parametric models. A basic goal when using any smoother is computing a confidence band for the true regression line. Let M(Y|X) be some conditional measure of location associated with the random variable Y, given X and let x be some specific value of the covariate. When using the LOWESS estimator, an extant method that assumes homoscedasticity can be used to compute a confidence interval for M(Y|X = x). A ...

The Impact Of Predictor Variable(S) With Skewed Cell Probabilities On Wald Tests In Binary Logistic Regression, 2017 University of British Columbia

#### The Impact Of Predictor Variable(S) With Skewed Cell Probabilities On Wald Tests In Binary Logistic Regression, Arwa Alkhalaf, Bruno D. Zumbo

*Journal of Modern Applied Statistical Methods*

A series of simulation studies are reported that investigated the impact of a skewed predictor(s) on the Type I error rate and power of the Wald test in a logistic regression model. Five simulations were conducted for three different regression models. A detailed description of the impact of skewed cell predictor probabilities and sample size provide guidelines for practitioners wherein to expect the greatest problems.

Experimental Design And Data Analysis In Computer Simulation Studies In The Behavioral Sciences, 2017 University of Minnesota - Twin Cities

#### Experimental Design And Data Analysis In Computer Simulation Studies In The Behavioral Sciences, Michael Harwell, Nidhi Kohli, Yadira Peralta

*Journal of Modern Applied Statistical Methods*

Treating computer simulation studies as statistical sampling experiments subject to established principles of experimental design and data analysis should further enhance their ability to inform statistical practice and a program of statistical research. Latin hypercube designs to enhance generalizability and meta-analytic methods to analyze simulation results are presented.

Using Pratt's Importance Measures In Confirmatory Factor Analyses, 2017 University of British Columbia

#### Using Pratt's Importance Measures In Confirmatory Factor Analyses, Amrey D. Wu, Bruno D. Zumbo

*Journal of Modern Applied Statistical Methods*

When running a confirmatory factor analysis (CFA), users specify and interpret the pattern (loading) matrix. It has been recommended that the structure coefficients, indicating the factors’ correlation with the observed indicators, should also be reported when the factors are correlated (Graham, Guthrie, & Thompson, 2003; Thompson, 1997). The aims of this article are: (1) to note the structure coefficient should be interpreted with caution if the factors are specified to correlate. Because the structure coefficient is a zero-order correlation, it may be partially or entirely a reflection of factor correlations. This is elucidated by the matrix algebra of the structure coefficients based on ...

Robust Measures Of Variable Importance For Multivariate Group Designs, 2017 University of Calgary, AB, Canada

#### Robust Measures Of Variable Importance For Multivariate Group Designs, Tolulope T. Sajobi, Lisa M. Lix

*Journal of Modern Applied Statistical Methods*

Variable importance measures based on discriminant analysis and multivariate analysis of variance are useful for identifying variables that discriminate between two groups in multivariate group designs. Variable importance measures are developed based on trimmed and Winsorized estimators for describing group differences in multivariate non-normal populations.