stats Essay

Due to financial hardship, the Nyke shoe company feels they only need to make one size of shoes, regardless of gender or height. They have collected data on gender, shoe size, and height and have asked you to tell them if they can change their business model to include only one size of shoes – regardless of height or gender of the wearer. In no more 5-10 pages (including figures), explain your recommendations, using statistical evidence to support your findings. The data found are below:

The information in the table below refers to the 2008 model year product line of BMW automobiles. Identify the Individuals, variables, and data corresponding to the variables in the table below. Determine whether each variable is qualitative, continuous, or, discrete. Please refer to problems #51 and #53 on page 13 for examples.

The coefficient is 403.for any increase in size, the credit balance will change by beta-hat

Contingency table results for Q13 What best describes your political philosophy?=Conservative: Rows: Q13 What best describes your political philosophy? Columns: Q9 What is your college major? Business IT Legal Studies Nursing Other Psychology Total Conservative Liberal Moderate Total 4 0 0 4 1 0 0 1 5 0 0 5 12 0 0 12 4 0 0 4 14 0 0 14 40 0 0 40

The given information is related to 30 major League Baseball teams for the 2012 season. Here, the sample size is large (n 230) but the population standard deviation (a) is unknown. So, apply one sample t confidence intervals. Find the 95% confidence interval for the mean number of home runs per team. Using MINITAB, following are the steps to obtain the 95% confidence intervals for the population mean. 1) Import the data into one column named as RUNS, 2) Choose Stat Basic Statistics 21-Sample t... 3) Select the Samples in columns option button, 4) Click in the Samples in columns text box and specify RUNS. 5) Click the Options...button, 6) Enter 95% in the confidence level text box. 7) Click the arrow button at the right of the Alternative drop-down list box and select not equal, 8) Click OK twice. output: One-Sample T Runs Variable 30 164.47 32.97 Mean StDev SE Mean 955 CI Runs 6.02 (152.15, 176.78) From the above output, the 95% confidence interval for the mean number of home runs per team is lies between 152.15 and 176.78

A pharmaceutical company is testing the effectiveness of a new drug for lowering cholesterol. As part of this trial, they wish to determine whether there is a difference between the effectiveness for women and for men. Using   = .05, what is the value the test statistic?

Dear Statistics Students, some advice in succeeding in class is students should attend class to understand the material better. The class is a four-credit class and we go through a lot of material and if you do not attended class it can cause you to miss the material. The power points are an outline so attending class helps to understand the slides and concepts better. I would not be on the phone during class incase of missing examples of concepts. Another key to success would be to practice and study the homework to become prepared for the quizzes. I would definitely work through the problems on the homework several times to understand how to do the math and to fix your past mistakes. When taking the quizzes and tests I would read the questions

The research design was clearly identified as “a cross-sectional descriptive exploratory” study (Chernomas & Shapiro, 2016). The design chosen for this study was appropriate but did employ the limitation that the results between each year of students could not be compared within the program. The independent variables were identified as the following: “demographic data (age, relationship status, year in program) [and] quality of life indicators (satisfaction with sleep and leisure activities)” (Chernomas & Shapiro, 2016). The dependent variables for the study were the depression, stress, and anxiety levels experienced by the students (Chernomas & Shapiro, 2016). Chernomas & Shapiro (2016) acknowledged that no

In How to Lie with Statistics (Huff, 1954), Darrel Huff deciphers statistical examples and explains the means of deception that statistics and statisticians sometimes use to relay false information. Huff also conveys an underlying message of don’t believe everything you’re told, something him and my mother have in common. At first glance, a reader might think that this book will teach people how to actually lie using statistics, but that is not the case. It gives the reader a glimpse or a behind the curtain view of how easily it is to be deceived using numbers and how it is slyly achieved. Ironically he calls the book How to Lie with Statistics almost to tease his audience that the content in this book is not as it appears. To my utmost surprise, I actually rather enjoyed this book. It was a fairly simple read that was filled with new information and showed me how to look closer at statistical figures in the future. The humor was spot on so much, so that I even chuckled aloud occasionally. For the icing on the cake, I even expanded my vocabulary to learn fun words such as rotogravure.

In investigating the impact of the regime changes on league competitiveness, the overall change in league equilibrium is charted based on the standard deviation of the team winning percentages for a particular season. One can compare the winning percentages’ actual standard deviation (ASD) with the ideal standard deviation (ISD) in a given year, assuming a perfectly competitive league. This approach was first used by Roger Noll and Scully in 1988 in their studies where they argued that the dispersion of wins could be measured by comparing the actual performance of a league to the performance that would have occurred if the league had the maximum degree of competitive balance in the sense that all teams were equal in playing strengths. The

Participants: A total of 168 students participated in the survey. The distribution of gender among the Participants were 62.5 %(105) = Female, 36.9% (62) = Male, and .6% (1) = Other. The distribution of year in school was 2.4% (4) = Freshmen, 17.9% (30) = Sophomore, Junior = 50.6% (85) = Junior, and 26.2% (44) = Senior. Convenient sampling was used in this survey as it was easier to get participants based off attending the same university. The survey was administered online, by sending a link to take the survey to the participants. Each student within the class was in charge of getting between 7-10 participants for the survey.

Sabermetrics was first defined by Bill James in 1980 as “the search for objective knowledge about baseball.” I see sabermetrics as the mathematical way of judging a baseball player. The name, sabermetrics, comes from SABR, the Society for American Baseball Research. Sabermetricians look in to baseball records and develop an analysis on each baseball player. This analysis deals with the number of hits, number of at-bats, and more.

With the increased and widespread use of technologies, interest in data mining has increased rapidly. Companies are now utilized data mining techniques to exam their database looking for trends, relationships, and outcomes to enhance their overall operations and discover new patterns that may allow them to better serve their customers. Data mining provides numerous benefits to businesses, government, society as well as individual persons. However, like many technologies, there are negative things that caused by data mining such as invasion of privacy right. This paper tries to explore the advantages as well as the disadvantages of data mining. In addition, the ethical and global issues regarding the use of data mining

Presenting quantities in a research study requires use of statistical methods. Use of statistics help to determine the trend in the studies population. Statistical methods also help to interpret and compare research data and determine significance of the findings. Due to variety of statistical methods, it is crucial to adopt an appropriate statistical approach before the conducting the experiments. Furthermore, the representation of individual data points as well as the general trend (e.g. mean) are critical for data comparison and interpretation in an experiment. Article, “Toxicity Inhibitors Protect Lipid Membranes from Disruption by Aβ42” uses statistics to represents experimental data and quantities (R Malishev et al. 2015). The statistics was used to present the data from thioflavin T (ThT) fluorescence assay, circular dichroism (CD) spectroscopy, and Förster resonance energy transfer (FRET) (R Malishev et al. 2015). Evaluation of the error bars, statistical design, and p-value is essential to determine the appropriateness of statistical method in the R Malishev et al. paper.

attempts are made to identify and define the existing statistical types of students in connection with their