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Business & Marketing
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Topic:

The Confidence Interval And Estimation Of A Population Average (Essay Sample)

Instructions:

Your response to each question must be at least 200 words in length. You are required to use your textbook plus one outside source as sources material for your response.
1. L Describe how a confidence interval can be used by a researcher to estimate a population average.
2. With regards to complexity and to value of findings, how do the four types of data analysis compare?
3. Explain how you can you use hypothesis in marketing research. How does a directional hypothesis differ from a nondirectional one, and provide an example of each.
4. Explain generalization. Distinguish between sample findings and population facts. How are they similar and how do they differ? Provide an example
5. What two levels of confidence are used most often, and which one is most commonly used? Explain how to obtain the most commonly used confidence interval for a percentage and an average.

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Content:

Week 6 Assignment
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Week 6 Assignment
1 Describe how a confidence interval can be used by a researcher to estimate a population average.
Confidence interval serves to provide the researcher with a degree of accuracy for a given population parameter. The mean or average of a population, for instance, maybe prone to sampling error during the collection of the data and thus necessitating the estimation of the population parameter using a range. Confidence interval serves to provide the researcher with such estimates based on a population parameter such as the average as presented for the case herein. Estimating the population average using confidence interval is a five-step process CITATION Bur17 \l 1033 (Burns, 2017). First and foremost the researcher has to identify the sample statistic, which is the average; x. The second step is identifying the sample size, n. The third step involves the determination of the amount of variation or variability in the sample population as the standard error of the average.
Standard error of average = Standard Deviation divided by the square root of the total sample (n).
The fourth step is to decide on the desired level of confidence; 95% and whose value is 1.96. The final step involves the computation of 95% confidence interval;
Average + or -1.96 multiplied by the standard error of the average
The population estimate average should be within the range of the lower and upper boundaries resulting from the above calcultions.
2 With regards to complexity and to value of findings, how do the four types of data analysis compare? 
Several data analysis techniques by researchers to describe different or specific variables for a given sample dataset. Each of the techniques varies in complexity and how valuable they are to the entire market research. The data analysis techniques include inference, difference, association, and relationship analysis. Inference analysis functions to make generalized conclusions about features of the population from the information deduced from the sample dataset CITATION Bur17 \l 1033 (Burns, 2017). Difference analysis, on the other hand, serves to provide the researcher with valuable information for market segmentation or even the viability of marketing strategies employed by a given firm CITATION Bur17 \l 1033 (Burns, 2017). It is more valuable and complex than inference analysis as it establishes the extent to which real and generalized differences exist in a given population. Association analyses function to establish the relationship between two variables if such exists CITATION Bur17 \l 1033 (Burns, 2017). It provides the researcher with real information concerning its performance based on the two variables and thus avoiding the common mistakes arising from misleading generalizations or conclusions CITATION Bur17 \l 1033 (Burns, 2017). It is more valuable than inference analysis but matches to difference analysis in complexity and value to the researcher. Relationship analysis is perhaps the most complex and valuable technique employed by market researchers in the analysis of sample datasets. It is more comprehensive than the other techniques as it establishes connections among various variables and thus creating valuable information on the existing multiple relationships and their benefits to the firm CITATION Bur17 \l 1033 (Burns, 2017).
3 Explain how you can use hypothesis

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