b. Company policy calls for a given month’s ending raw materials inventory to equal 50% of the next month’s materials requirements. The March 31 raw materials inventory is 4,925 units, which complies with the policy. The expected June 30 ending raw materials inventory is 4,000 units. Raw materials cost $20 per unit. Each finished unit requires 0.50 units of raw materials.
c. Company policy calls for a given month’s ending finished goods inventory to equal 80% of the next month’s expected unit sales. The March 31 finished goods inventory is 16,400 units, which complies with the policy
d. Each finished unit requires 0.50 hours of direct labor at a rate of $15 per hour.
e. Overhead is allocated based on direct labor hours. The predetermined variable overhead rate is $2.70 per direct labor hour. Depreciation of $20,000 per month is treated as fixed factory overhead.
f. Sales representatives’ commissions are 8% of sales and are paid in the month of the sales. The sales manager’s monthly salary is $3,000 per month.
g. Monthly general and administrative expenses include $12,000 administrative salaries and 0.9% monthly interest on the long-term note payable.
h. The company expects 30% of sales to be for cash and the remaining 70% on credit. Receivables are collected in full in the month following the sale (none is collected in the month of the sale).
i. All raw materials purchases are on credit, and no payables arise from any other transactions. One month’s raw materials purchases are fully paid in the next month.
J. The minimum ending cash balance for all months is $40,000. If necessary, the company borrows enough cash using a short-term note to reach the minimum. Short-term notes require an interest payment of 1% at each month-end (before any repayment). If the ending cash balance exceeds the minimum, the excess will be applied to repaying the short-term notes payable balance.
K. Dividends of $10,000 are to be declared and paid in May.
l. No cash payments for income taxes are to be made during the second calendar quarter. Income tax will be assessed at 35% in the quarter and paid in the third calendar quarter.
m. Equipment purchases of $130,000 are budgeted for the last day of June.
Prepare the following budgets and other financial information as required. All budgets and other financial information should be prepared for the second calendar quarter, except as otherwise noted below. Round calculations up to the nearest whole dollar, except for the amount of cash sales, which should be rounded down to the nearest whole dollar:
How do i figure the taxes paid for april, may, and june
Mark the end of the list with a negative number.After I enter this numbers.34 55 22 17 99 1 0 22 22 -1It should sort in this order:0 1 11 17 22 22 34 55 99And have an output like thisAfter adding 200 more value the size of the VectorDouble is 210Press any key to continue . . .
Bain, do you want this 4 pager… due in 24 Hrs?
One of the problems your firm has is hiring new technologists
who have a fundamental understanding of relational databases. Therefore, you’ve
been asked to create a database orientation paper.
Write a four to five (4) page
paper in which you:
Describe what a relational
database is and why relational databases are needed.
Describe the process of
normalization and why it is needed.
Contrast and compare logical and
physical database design.
Critique SQL as a user-friendly
Use at least three (3) quality
resources in this assignment. Note: Wikipedia and similar
Websites do not qualify as quality resources.
Quick short answers: just a sentence or two apiece
Speculate on whether something such as sound can be
converted into binary, given that sound is continuously varying while
binary has only two states. Explain your rationale.
“Data Structures” Please respond to the following:
Consider and explain whether or not you can use a sort
routine to sort unstructured data.
Contrast and compare: an array, a stack, and a queue.
Identify the principal uses of each and give an example.
This completed assignment is worth up to 2.5 extra credit points and may serve as the multiple regression portion of the class project. Late submissions will not be graded.
This assignment is essentially the multiple regression analysis portion of your project. This means that I expect you to develop a good regression model with more than one independent variable (X). Ideally, if you made a good choice of variables in your proposal you should be able to include all three or more X variables in your regression equation. Be sure to complete each part and write your responses supported by Minitab/excel work. This assignment should be turned in to me as a Word document. You should include excel and Minitab tables and graphs in the Word document as required. Be sure to comment on each of the 10 points below.
1.Run scatter plots and a correlation matrix on your project variables and comment on their values and significance if you have done this earlier you may use that analysis here.
2. Note any seasonality in your Y data with ACF (autocorrelation analysis of Y) You may use ACFs that you previously developed.
3.Determine if any of your variables require transformation. If they do, calculate the transformed values and create a scatter plot with a regression line and run a correlation with Y for each transformed X. Create a table for the Y, X and X transformed values.
4.Determine if your model requires dummy variables (e.g. for Y variable seasonality or significant events) and include a table of the dummy variable values for regression analysis. You may use either Decomposition centered moving average of Y (CMA) for Y and seasonal indices (SI) to seasonally adjust your Y variable or use dummy X variables in regression.
5.Use regression to evaluate the variable combinations to determine the best regression model. Note that is any seasonal dummy variables are used all of the seasonal dummy variables must be used. Use R square and F as primary determinants of the best model.
Note the significance of each slope term in the model. Rule– if the coefficient is not significant then you may not use the model to forecast.7.Investigate your best model using appropriate statistics or graphs to comment on possible:
a.Autocorrelation (Serial correlation) with the DW statistic
b.Heteroscedasticity with a residuals versus order plot (look for a megaphone effect)
c.Multicollinearity with the VIF statistic Determine the best remedies for any of the problems identified in 5 above and make the appropriate changes to your regression model if required. Rerun the model and evaluate the fit again including error measures, R adjusted square, F value, slope coefficient significance, DW and VIF.
6.Evaluate the best multiple regression model accuracy with 2 error measures (RMSE and MAPE) each for the fit and again for the forecast period.
9.Evaluate the best model fit residuals and comment on their randomness using autocorrelation functions (ACFs) , histogram and a normality plot (You should use a four-in-one graphs as well). Comment on the cause of the error — trend, cycle, seasonality and if it is statistically significant.
10. Forecast for the holdout period using your hold out X values to forecast Y. You can use Minitab Regression – Options menu by placing the columns for the X variables hold out values and any dummy variable predictions in the “Minitab/Regression/Options/Prediction intervals for new observations” area.
11.Evaluate the forecast error measures and residuals to determine if the error is acceptable or has systematic variation. Write your conclusion relative to the acceptability of the sales forecast.
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