Chapter 11 Comparisons Involving Proportions Learning Objectives 1. Be able to develop interval estimates and conduct hypothesis tests about the difference between the proportions of two populations. 2. Know the properties of the sampling distribution of the difference between two proportions[pic]. 3.
Be sure to define all the terms of your hypothesis. What are your independent and dependent variables? How odes your hypothesis compare with the study to be replicated? 4. Method: a.
If you simply want to take a look into the behaviors of individuals then you will want to use qualitative research methodology. However, if you want to look at the statistics or percentage aspects of a situation then it is important that you use the quantitative research methodology. Mixed method research is actually where the use of quantitative and qualitative research method is both being used together to conduct a research and using both forms of data to come to a conclusion. It is appropriate to use both if a researcher wants to look at an issue in a 360 degree manor meaning they would like to see how many people the issue is actually affecting, as well as why it is affecting them. Scientifically sound research actually supports the function of a human service manager because the results or findings of this research is often backed up and proved successful by science laws, as well as being backed up by facts.
The slope b is the approximate change in y when x increases by 1. 2. How do you use the regression line for prediction? To make a prediction when you have population data, take average of group; group averages very often form line, also to make a prediction when you have a sample, take all group averages and draw a line of best fit; use line to predict. Regression models are often constructed based on certain conditions that must be verified for the model to fit the data well, and to be able to predict accurately.
It is possible to normalize the data when the population shape has a known skew. There are many ways for normalizing skewed distribution, for instance using the square root transformation and logarithmic transformation just to name a few. (Sekaran, 2003) Starting with a set of data which is not standard, any non-normal or probably the uniform distribution would work in explaining it better to other students. * Example 1; Here is an example of how a not so normal histogram analysis (“Histograms”, 2012) * * The choice of choosing samples from the set of data of size 6, calculates the mean, then repeat several times more, then change to a larger sample size. When your sample sizes increases you will see the histogram of the mean look like a normal distribution.
3 Understand techniques and criteria for monitoring the quality of assessment Internally - (Professional discussion to be based on this Essay) 3.1 Evaluate different techniques for sampling evidence of assessment, including use of technology: There are different techniques for sampling the evidence of learners work such as the vertical sampling (identical elements from different assessors and horizontal sampling which means that a specific piece of all units over the course of time is checked very carefully for validity, consistency and so forth,. This would include liaising with and interviewing assessors, reviewing their practical views on specific areas to tutoring to obtain evidence that match up with unit/element criteria, including witness statements; scrutinising their records whether they are paper based or electronically stored. I would also take the opportunity to observe good working practice and if need be consult any witnesses, for example; with regards to my work as an IV at Bradford College I would implement the above techniques for sampling evidence. With respect to this, there are a number of factors to be taken into consideration, such as the type of evidence, i.e., what format it is in, how many learners there are in the current cohort for example and which is then also dependent on the experience of the assessors and whether they are qualified or not to carry out the job effectively and efficiently and adhering to the organisation awarding body strategies, policies and procedures, all within realistic timeframes. I understand that other than the availability of a hard copy of evidence if required by the IQA staff/team, there is another more modern way of sampling using technology such as Moodle is basically a virtual delivery environment which means that candidate/learners can receive tasks and information so that they can
Cumulative gains charts for training sample and test sample - Comparison between the cumulative gains charts helps assessing the strength of the model. Key Observations – * The shapes of the curves are similar. * For both samples, the maximum distance between the model and no model lines is in the range of 36-37% for around 32-35% of customers. It can be inferred that predicted model using the sample data helps explaining the differences in the test sample as well. Hence, the nodes are pruned at the right points, minimizing the errors.
Discuss the difference between the two strategies. The questions asks us to indentify two strategies that could be used in a low control risk assessment. The two I will use are the User Controls and the Application Controls. 1. User Controls will use design manual procedures to test completeness Also, the auditor can test the controls directly, similar to testing other human controls.
Hypothesis Testing and Confidence Intervals Utilizing the data provided AJ Davis Department Store performed Hypothesis Testing and Confidence Intervals performed to determine s the assumptions made are correct or incorrect. As with any Hypothesis test we must know some basic factors, such as sample size, sample mean, sample standard deviation, and you must have an hypothesized mean. Either through mathematical formulas, or by employing programs such as MS Excel and or Minitab, we can test whether or not the to reject or accept the hypothesis. For all subsequent testing, both MS Excel and Minitab will be utilized to determine whether or not accept or reject the null hypothesis. Assumptions are as follows: 1.
This stage is necessary to explain the theory and recognize the populations which will be worked with all through the study. 2.) Find out the features of the comparison distribution. In instances where the null theory is correct the comparison distribution is compared to the score depending on the sample’s outcomes. 3.)