A-Level Coursework on "Abraham Lincoln to a Proposed Survey Questionnaire"

A-Level Coursework 5 pages (1379 words) Sources: 6

[EXCERPT] . . . .

Abraham Lincoln to a proposed survey questionnaire has indicated he had not received a grade school diploma. When a researcher correlated the information to the expected variables, he found the data did not highly correlate to the expectations derived from the data. There are many possible problems and we will discuss some of them.

Lincoln could have lied on the survey to provide a false indication of his education. He could have received the grade school diploma, which would mean the data is in error. This speaks to the issue of poor quality data and the need to have high quality and very specific data on which to base a forecast, prediction, or expected variable. Lincoln may have pursue his educational goals outside the realm of the classroom. So although lacking what he perceives to be a formal education as ruled by the state at the time, he educated himself on his own interests and therefore the survey is invalid.

The survey questionnaire is an example of the use of nominal data or data that is non-numerical. This qualitative data is subjective and open to interpretation and does not measure for the environmental variable yet the environmental effect may show up in the error statistic. Given the problem of the small sample size, this may the case. The probability for a greater measure in error measured as the t-statistic is higher as the size of the sample decreases

The lack of identifying the environmental variables that were important to Lincoln's education is critical to the wrong bivariate analysis and prediction. Lincoln likely, was bored with the rigid academic environment and decided to pursue his own educational curiosity and learn about his environment according to his interpretation of what is important to know. This bivariate analysis did not have the correct data to measure and analyze for environmental variables.

2.

Management of a regional bus line wanted to measure the correlation between two variables. The dependant variable is the passenger/mile ratio and the independent variable is the cost of gas. Therefore, y is the dependant variable and x is the independent variable.

The correlation coefficients for the price to mile and passenger ratio is varied. The null hypothesis is H0 with N=5 and RHO=0 with the year price and mile information given as the following

Statistical Techniques in Business & Economics

The data is spread out but is almost perfectly correlated, suggesting there is a strong and positive, linear correlation between the cost of gas and the passenger/mile ratio. Therefore the null hypothesis or H=0 is accepted.

3. A correlation matrix for a company measures the sales force activity as a function of a number of variables including age, years of service, and current sales. The matrix is depicted below:

The results of the correlation are interesting. Age and Years of Service result with the greatest correlation as dependant and independent variables. The correlation is .64 and suggests that age is a function in the years of service. Therefore, the older an employee becomes, the more service in years will accrue. However, this is to an extent. If one were to assume a ten-year career, on average, the statistic suggests an average of 6.5 years on the job. Therefore, it is not a perfect measure of years on the job but gives an indication that independent of the age of the employee, they will have about a 7-year career.

The strength is not as great as in the previous data however the relative strength of the correlation between years of service and current sales is close to .5 and therefore suggests that beyond a certain number of years of experience, sales seems to increase as a greater percentage of each additional year of experience.

Hence, a greater marginal increase is expected after x number of years of service. This may likely be due to a reputation established in the neighborhood after working in the community for a number of years or having sold a number of cars and the word of mouth effect enabling greater sales as a function of years.

Age to Current Sales is very interesting… READ MORE

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