Statistics Final Essay

5. The biggest question facing Schoenfeld now is “how to drive that next stage of growth” (case p. 2). The first option is to focus on the shoe category. The

Topics Distribution of the sample mean. Central Limit Theorem. Confidence intervals for a population mean. Confidence intervals for a population proportion. Sample size for a given confidence level and margin of error (proportions). Poll articles. Hypotheses tests for a mean, and differences in means (independent and paired samples). Sample size and power of a test. Type I and Type II errors. You will be given a table of normal probabilities. You may wish to be familiar with the follow formulae and their application.

When you perform a test of hypothesis, you must always use the 4-step approach: i. S1:the “Null” and “Alternate” hypotheses, ii. S2: calculate value of the test statistic, iii. S3: the level of significance and the critical value of the statistic, iv. S4: your decision rule and the conclusion reached in not rejecting or rejecting the null hypothesis. When asked to calculate p–value, S5, relate the p-value to the level of significance in reaching your conclusion.

To test the null hypothesis, if the P-Value of the test is less than 0.05 I will reject the null hypothesis.

We conduct an independent sample t-test using Excel, and obtain the following output (see sheet T-TEST)

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?

| Based on explicit knowledge and this can be easy and fast to capture and analyse.Results can be generalised to larger populationsCan be repeated – therefore good test re-test reliability and validityStatistical analyses and interpretation are

With a P-value of 0.00, we have a strong level of significance. No additional information is needed to ensure that the data given is accurate.

The goal of this experiment was to determine the center of gravity of some of the students in our class (subjects) and express that COG as a percentage of height. Without a lot of prior knowledge about center of gravity it was expected that the COG would be consistent throughout the class. However, the experimental results showed that each subjects center of gravity varied based on characteristics such as height, weight, and gender. This makes sense because center of gravity is unique to each person and that is why it is so important in Biomedical Engineering. The experimental results showed that men had a greater percentage for center of gravity as a percentage of height than women. Specifically, the percentage was 2.4% greater for men meaning on average male’s center of gravity is positioned higher on their body.

Determining whether or not the shoe manufacturer can actually manufacture one size of shoe regardless of height and gender and still remain successful is not as simply addressed as one might think, yet is also not especially complex and can be addressed with just a basic knowledge of statistics. Entering the provided data into a spreadsheet allows for easy manipulation and calculation, and this was done here. The data was then sorted by shoe size and the quartiles marked as a preliminary means of analysis. Simply from this data sorting, it can be seen that gender has a very clear relationship with size there is only one male in the lower two quartiles of the data (the half of the data set with the smaller shoes), and similarly there is only one female in the upper two quartiles. This immediately and compellingly suggests that women generally wear smaller shoes and men generally wear larger shoes, and though this is not enough of a reason to dismiss the shoe manufacturer's plan it certainly does not bode well. Calculating the mean and the standard deviation of the shoe size sample also indicates that the plan is not especially advisable the mean shoe size is just over 9, which is the right size for only one person in the sample, and a standard deviation of 2.49 means there are substantial differences or a wide variance in shoe sizes there is not a strong cluster around the mean, but instead shoe sizes are very spread out. A correlation analysis between shoe

I am assigned to determine whether the weight of the male weight lifters is greater than the average weight of males in the population—180 pounds, which is from scenario 2. The null hypotheses for this scenario is: “There is no difference between the average weight of male weight lifters in the population and the 10 male weight lifters at Gold’s Gym” while the alternative hypotheses is: “There is a significant difference between the average weight of male weight lifters in the population and the 10 male weight lifters at Gold’s Gym.”

This paper will examine a data analysis and application for an independent t test comparing the mean GPAs of a sample of male and female students. It will pose a research question that the data will set out to answer. It will provide a null hypothesis and an alternative hypothesis, and will provide an analysis showing why the null hypothesis should be accepted or rejected in favor of the alternative hypothesis.

b) The same article reported the mean size was more than 2100 square feet? Can we conclude that the mean size of homes sold in the Gulshan area is more than 2100 square feet? Use the 0.05 significance level. What is the

In Chapter 9, we learned how to conduct a t test of a hypothesis when we were testing the mean of a single sample group against some pre-determined value (i.e., the 21.6 gallons of milk consumption as the national average). This week, in Chapter 10, we will see how to test hypotheses that involve more than one sample group—such as testing to see if males are significantly taller than females. If we have two groups, then the technique that we will use will still be a t test. If we have more than two groups, then we will have to use a different test called Analysis of Variance (ANOVA, for short).

There are seven steps that will be used within this paper to test a hypothesis. These steps include the following; Step 1: which is stating the null hypothesis, stating the research hypothesis, identifying the target population and sample population, identifying the independent variable and dependent variable. Step 2: establishing the level of significance. Step 3: selecting a one-tailed or two-tailed test. Step 4: conducting the test and excel and providing the results as an excel output. Step 5: calculating the degree of freedom. Step 6: stating the critical value to exceed by the calculated test statistic. Lastly, Step 7: comparing the calculated test statistic and to the level of significance and making a decision about the results. The following steps will be explained in further detail below.