Descriptive and Inferential Statistics

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:

 Inferential statistics helps us to analyze predictions, inferences, or samples about a specific population from the observations that they make. “With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone” (Trochim, 2006). The goal for this type of data is to review the sample data to be able to infer what the test group may think. It does this by making judgment of the chance that a difference that is observed between the groups is indeed one that can be counted on that could have otherwise happened by coincidence. In order to help solve the issue of generalization, tests of significance are used. For example, a chi-square test or T-test provides a person with the probability that the analysis’ sample results may or may not represent the respective population. In other words, the tests of significance provides us the likelihood of how the analysis results might have happened by chance in a scenario that a relationship may not exist between the variables in regards to the population that is being studied.

Biostatistics. I applied basic informatics techniques with vital statistics and public health records in the description of public health characteristics and in public health research and evaluation. [A.8] I used my knowledge of basic biostatistics to compare positive cases, test submissions, and human population to see if there was any correlation. Understanding biostatistics allowed me to make a valid and meaningful comparison of this data. The information gathered from the biostatistics gives a limited estimate of risk, and should be further evaluated to quantify this risk. The main goal of my project was to interpret the results of statistical analyses in terms of epidemiological human risk factors. [A.9]

• Provide at least two examples or problem situations in which statistics was used or could be used.

The last few weeks we covered descriptive statistic: the central tendency, variability, correlation and Z-score. Today’s session is a little bit different, we will be talking about statistical significance. Statistical significance is the level of risk one is willing to take to reject or accept a null hypothesis while it is true and it separate random error from systematic error. When doing a study or research, the statistical significance shows that the difference obtained were not caused by chance. Inferential statistics, the T-test, partition noise from bias by studying a random sample than the population in which we are interested and from the results we infer. The advantage of using sample than a population, it is convenient, saves time, energy and money because n is smaller than population and above all it helps to control systematic and random errors. When we are making a conclusion, we should have a certain confidence or probability of being right and that is called the alpha level; which the risk you are willing to

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.

A theory often used in the research of individual health behavior and behavioral intentions is the theory of planned behavior (Ajzen, 1991). According to this theory, the performance of any behavior depends on behavioral intention. Behavioral intention is viewed as being dependent on behavioral beliefs (e.g., attitude towards the behavior), normative beliefs, and control beliefs. Normative beliefs are beliefs about the expectations of others, and control beliefs are beliefs about the factors that may help or hinder the performance of the behavior. The framework for the study of physical activity discussed earlier by Armitage (2005) is based on this theory. That study found that behavioral beliefs, normative beliefs, and control beliefs all contribute to physical activity behavior.

In population-based studies, instead of looking at a small group of individuals to make an assumption on the entire population, we are taking numbers that represent the population and determining

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

Damned Lies and Statistics Reflection Damned Lies and Statistics by Joel Best gives the reader a whole new perspective on the idea of quantitative data. His central argument is that just because someone gives you a statistic doesn’t mean that statistic is accurate. He informs people to pay attention to the statistics that they see and hear about. People naturally assume that because they are being given a number, that number has to be true. Joel Best teaches us to be more observant of numbers and to ask questions such as who is presenting these numbers and why they are presenting them.

Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. It is applicable to a wide variety of academic disciplines, from the physical and social sciences to the humanities. Statistics are also used for making informed decisions – and misused for other reasons – in all areas of business and government. Statistical methods can be used to summarize or describe a collection of data; this is called descriptive statistics. In addition, patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations, and then used to draw inferences about the process or population being studied; this is called inferential statistics. Both

B) i: The purpose of using descriptive statistics is to try to estimate the student population’s stress level as they are preparing for exams. ii: The purpose of inferential statistics is to try to make sure that the population of students are not too stressed for their exams.

Statistics show if the treatment has had a good number of responses to the treatment or a bad number of responses. Back in 1775 they

Major League Baseball is known as America’s favorite pastime, and MLB teams spend an extensive amount of money in the excess of a billion dollars with the ultimate goal to win the World Series. This learning team’s focus throughout this descriptive statistics paper is the MLB players’ performances, salaries, salary caps, and winning percentages. Though salaries will by no means be a trade for wins, the goal is to use the less experienced players and pay them a lower salary. Research has been done on whether or not player’s salaries and wins are connected.

In simple terms, statistics is termed as a branch of mathematics which basically deals with collection of data and its subsequent analysis and interpretation both qualitatively and quantitatively. In most of the cases the data is numerical, but there are also cases when the data is non-numerical such as relationship between objects.