Linear Regression Analysis Paper

Note: 1993 is the base year for your analysis in Questions 1a, 1b, and 1c

Stage 2 Mathematical Applications Interest minimisation on a Loan Occupation Josh Emma Occupation Social Worker Secondary Teacher Wages $1297 per week 1297 X 52 = $67444 per year $1424 per week 1424 X 52 = $74048 per year Tax amount $633,001 – $95,000 per year = $14,760 + $0.42 for each $1.00 over $63,000 67444 – 63000 = 4444 (4444 X .42) + 14760 = $16626.48 tax $633,001 – $95,000 per year = $14,760 + $0.42 for each $1.00 over $63,000 74048 – 63000 = 11048 (11048 X .42) + 14760 = $19400.16 tax Salary after tax 67444 – 16626.48 = $50817.52 after tax 74048 – 19400.16 = $54647.84 after tax Weekly budget of expenses for both Josh and Emma Expenses Price

If I had to choose a career in the business related industry after graduating high school, I would choose to be a Market Research Analyst. As a Market Research Analyst, I will study market conditions to examine potential sales of a product or service. Not only that, but I will also help companies understand what products people want, who will buy them, and at what price.

d. Compute the following ratios (assume that the year-end amounts of total assets and total stockholders’ equity also represent the average amounts throughout the year):

From Table 8 and Table 3, we were quickly able to identify that the regression assumptions were not going to be satisfied and that our dependent variable needed transforming. Looking at the plot in figure 8, we could see that the residuals of the predictions were not following a straight line. This indicated that linear regression was not suitable for the data.

Find the present value of the following ordinary annuities (see the Notes to Problem 4-12).

12. Jeff and John shared equally in an inheritance. Using his inheritance, John immediately bought a 10-year annuity-due with annual payments of 2500 each. Jeff put his inheritance in an investment fund earning an annual effective interest rate of 9%. Two years later, Jeff bought a 15-year annuity-immediate with annual payments of Z. The present value of both annuities were determined using an annual effective

a. For Delta, what was its annual depreciation expense (per $100 of gross aircraft value) prior to July 1, 1986; from July 1, 1986 through March 31, 1993; and from April 1, 1993

Our regression analysis was done on OMNITRANS fuel consumption. This has been an ongoing issue for OMNITRANS where there seems to be an inconsistency with there CNG fuel consumption. There continues to be variance in what is consumed each day compared to the amount of miles driven. This issue is very important to OMNITRANS because it makes it very difficult to plan for future use with the CNG industry. OMNITRANS wants to have a consistency with CNG use so they can plan for budgeting purposes and new contracts that are connected with the CNG usage. We are going to establish what we believe will be a good analysis for OMNITRANS to look at and establish what is needed in order to improve there fuel consumption within the

There are many issues that could potentially harm the validity of the regression forecast or results. One of the issues is that the variables are not normally distributed. This can be detected by visual inspection, specifically by looking at the Histogram or the normal profitability plot. This issue could actually lead to a higher amount of error in the regression project. One of the fixes is to actually ignore or remove the observation. Ignoring or removing the error could lead to a major amount of error build up, or could lead to removing an important variable within the data. Another issue is possibly the presence of serial correlation. This problem can develop or be detected with visual inspection, through the residuals specifically in

a. Draw time lines for (1) a $100 lump sum cash flow at the end of Year 2; (2) an ordinary annuity of $100 per year for 3 years; and (3) an uneven cash flow stream of -$50, $100, $75 and $50 at the end of Years 0 through 3.

The traditional method for the determination of the time lag resorts to a limited portion of the downstream pressure rise curve to perform the required extrapolation to the time axis. When a numerical model is available, an alternative method to obtain the membrane properties is to fit the variation of the pressure change in the downstream reservoir as a function of time using a nonlinear least squares method [30]. By minimizing the sum of squares of the differences between the experimental data and the numerical model, the optimal combination of S and D can be obtained. The nonlinear regression method has three advantages in diminishing the noise effect: 1) it uses the whole range of pressure data instead of only the quasi-steady state

A sample size equaling 50 + 8m is required to do a multi-linear regression, where m is the number of independent variables chosen. At least 3 independent variables can be analyzed (assuming a moderate effect size) taking males and females separately if an equal number of males and females are chosen (Green, 1991). Thus the sample size is adequate for a multi-linear regression analysis.

In the first question there is the need to assess the present value of $4,200 in a year's time. The interest rate is 5%, which means that the value given is effectively 105% of the original total. A very simply calculation would be to take the total, divide it by 105 and then multiple it by 100 to give the answer. However, while this may be easy to use in a single figure for only a single year, it is better to look for a technique which will allow discounting for different period of time.

From this equation, we can solve for the amount to be put aside each year.