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MGMT 3710 – Business Management Exam

MGMT 3710 – Business Management Exam on Excel

MGMT 3710 - Business Management Assignment Helpassignment help
MGMT 3710 – Business Management Exam

Hello. I hope you are doing great! This exam is the first take home exam for MGMT 3710 class.

Regression analysis & forecasting is one of the most complicated topics of Industrial Engineering and Business & Management assignment help. If you need help with your MGMT3710 Take Home Exam; Click here to register & post a job for free in less than 5 minutes!

Assignment Type: Exam (Take Home)

Assignment Category: Business and Management Assignment Help

Platform: Take home exam

University: Unknown

Template: No template

Assignment Size: 120 minutes of work

Attached File(s);

MGMT 3710
Exam I (Take Home Part)

The department manager has decided that the appropriate forecasting model for the sales of toasters a
local department store is the Winter’s Model with additive trend and multiplicative seasonality. Given
the time series data set in the Excel spreadsheet (Sheet I – Toasters), make the forecasts for the four (4)
quartets (QI, Q2, Q3 and 04) for the year 2020. Make sure to include a line chart with markers
indicating the actual demand, the trend line, and your forecasts. Also. calculate the MAD, MSE, MAPE,
bias and the tracking signal for the managers forecast. the supplied Excel spreadsheet to input your
with the correct formulas. Would you accept the manager’s based on my Of the
measurements Explain briefly.
(2S points)

Graduate Management Assessment Tests ( GMAT) scores are often used as an indicator of graduate
school performance in terms of predicting a student’s grade point average (GPA). Given the following
data (Excel File Sheet 2 – GMAT), use a linear regression model to predict the GPA for a student
scoring 720 on his/her GMAT exam. Make sure to identify the independent dependent variables
correctly. You must show the output from the Regression model. Also, provide the rend or regression
line equation and show your calculations for the forecast.

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