MINITAB EXAM

2. The exam/term project is open book and open notes.

3. Submission of answers only in Minitab 17.2 files, without defining your work in obtaining the solution in full, is not acceptable. Detailed discussions must include the (a) business problem (b) data analysis techniques, (c) data, (d) analytical techniques, (e) computerized procedures, and (f) results and interpretation (per the course syllabus).

4. Merely providing screen shots from the Minitab 17.2 software is not acceptable. Rather they should be referenced in the results and interpretation. All computer output from Minitab 17.2 needs to be fully annotated.

Regression Forecast Modeling Analysis of Their Insurance Companies

Regression Model Fit for Three Sets of Explanatory Variables

Using the first 24 months of data for 2013 and 2014, forecast the price of your fund for months 25, 26, and 27 in year 2015. In all cases, the response variable is the price of your fund. You need to indicate the name of your fund in your submission.

In order to insure for validity of your Minitab regression computations, you must show a link between your Minitab data set, your Minitab regression drop downs, and your Minitab regression outputs. That is, for each regression run, the data file, the drop down, and the output all need to be given.

Regression Models - Explanatory Variable

A. Set (1): Variables of explanation: time, relating it to the price of your fund.

1.time (t) (1, 2, ..., 24) time (t-1, 2, ..., 24)

2.time (t2) (1, 2, ..., 24) time (t-1, 2, ..., 24)

3.time [(1/t)] (1, 2, ..., 24) time (t-1, 2, ..., 24)

For Set 1, there will be three one-variable regression models. These models will be generated by using Minitab 17.2. regression.

For each regression model set, you need to display the R2, the model coefficients, the model standard error. You need for each model Set (1) to identify which model gives the best fit and why.

For each set of models, you will need to display the forecast of the price of your fund for months 25, 26, and 27. You need for each model Set (1) to identify which model gives the best forecast and why.

B. Set (2): Variables of explanation: finance, relating it to the price of your fund.

1. Dow Jones Average (1, 2, ..., 24)

2. NASDQ Average (1, 2,.., 24)

3. S&P Average (1, 2,..., 24)

For Set (2), there will be three one-variable regression models. These models will be computed by using Minitab 17.2 regression.

For each regression model set, you need to display the R2, the model coefficients, the model standard error. You need for each model Set (2) to identify which model gives the best fit and why.

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TAKE HOME EXAM #1, PART 2

I. DIRECTIONS FOR THE TAKE HOME EXAM #3

1. You must bring the completed exam to class on February 15, 2018.

2. The exam is open book and open notes.

3. Submission of answers only in Minitab 17.2 files, without defining your work in obtaining the solution in full, is not acceptable. Detailed discussions must include the (a) business problem (b) data analysis techniques, (c) data, (d) analytical techniques, (e) computerized procedures, and (f) results and interpretation (per the course syllabus).

4. Merely providing screen shots from the Minitab 17.2 software is not acceptable. Rather they should be referenced in the results and interpretation. All computer output from Minitab 17.2 needs to be fully annotated.

Regression Model

The following set of data relates 34 store data values for sales (Y), price (X1) and Promotion (X2) for the dependent variable, Y, and the independent variables, X1 and X2, have been collected using simple random sampling:

Develop a multiple linear regression equation for these data.

a. Calculate the sum of squared residuals, the total sum of squares, and the coefficient of determination.

b. Calculate the standard error the estimate

c. Calculate the standard error for the regression slope.

MGMT 216 Spring 2018 - Exam 1