1. (5 points) Give and explain two characteristics of effective spreadsheets. 2.

Question

1. (5 points) Give and explain two characteristics of effective spreadsheets.

2. (5 points) A general rule in hypothesis testing is to reject H0 if p < ?. If p = 0.024, for what significance (?) values would H0 be rejected?

3. (5 points) Explain the difference between an unbounded problem and an unbounded feasible region. Does one imply the other (both directions)? Explain.

4. (5 points) Explain the purpose of the correlation matrix in multiple linear regression. What do the values represent? What sorts of values would you prefer to see (large, small, positive, negative...)? Explain.

5. (5 points) In regression modeling, what is a reference category? Explain.

6. (5 points) Consider the spreadsheet below. If the formula in cell D2 is copied “right” and “down” into cells D3, and E2:E3, what will be the value in cell E3?
a. 17.
b. 23.

c. 4.
d. 9.
e. None of the above/There is not enough information to tell.

7. (5 points) Consider the scatterplots of residuals versus fitted values below. Which meet(s) linear regression assumptions?
a. Chart A only.
b. Chart B only.

c. Both A and B.
d. None of the above/There is not enough information to tell

Explanation / Answer

1. Give and explain two characteristics of effective spreadsheets.

2. A general rule in hypothesis testing is to reject H0 if p < ?. If p = 0.024, for what significance (?) values would H0 be rejected?

  P-value = 0.0127 which is less than ? = 0.05 therefore we reject the null hypothesis

3.  Explain the difference between an unbounded problem and an unbounded feasible region. Does one imply the other (both directions)? Explain

A Feasible region is the solution to the system of linear inequalities. It is the set of all points that satisfy all the constraints while an unbounded region is the feasible region that can not be encircled or bounded in a circle..

Unbounded problem is one where the objective function is not restricted by constraints and the optimal one goes to infinity.

4. Explain the purpose of the correlation matrix in multiple linear regression. What do the values represent? What sorts of values would you prefer to see (large, small, positive, negative...)? Explain.

Simple Linear Regression analysis means a regression analysis where only one independent variable is involved. It is relevant only for the cases having one independent variable which the main constraint of this analysis.

Multiple regression analysis means where more than one independent variable is used. When all the independent variable are supposed to affect the dependent variable in a linear fashion and independently of one another, the procedure is called multiple regression analysis.

Correlation is a measure of how to variables are associated with each other. The variables are not termed as dependent or independent.

The direction and strength of the linear association between the two variables is computed in a range between -1 and +1. This correlation can be can be positive (i.e., higher levels of one variable are associated with higher levels of the other) or negative (i.e., higher levels of one variable are associated with lower levels of the other).

A correlation which is close to zero implies that there is no linear association between two continuous variables. So we we would prefer to see positive values.

The sign of the correlation coefficient shows the direction of the association. The magnitude of the correlation coefficient specifies the strength of the association.

The strength of prediction from a multiple regression equation is calculated by the square of the multiple correlation coefficient, R2

5. In regression modeling, what is a reference category? Explain.

In Regression modelling,  a dummy variable indicates the absence or presence of some categorical effect that may be expected to affect the result.  It takes the value of either 0 or 1. Dummy variables are used to sort data into mutually exclusive categories like vegetarian/non vegetarian etc.

Dummy coding is a method of coding a categorical variable. It is used to compare other groups of the predictor variable with one specific group of the predictor variable. Often, the specific group is called the reference group or category

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