Based on the output shown, what independent variables are significant?

Group of answer choices

Region

Foam

Residue

Region

Scent

Color

Foam

Residue

Scent

Color

Flag question: Question 5

Question 5 1 pts

Can I reject the null hypothesis? Can I use this regression to forecast future events?

Group of answer choices

No

Yes

Flag question: Question 6

Question 6 1 pts

Based on the Excel output, what is the regression equation?

Group of answer choices

Housing starts = 221,794 + 90*(Population) – 206*(Mort Rate) – 120*(Year)

Housing starts = 221,794 + 90*(Population) + 206*(Mort Rate) + 120*(Year)

Housing starts = 661,959 + 162*(Population) – 55*(Mort Rate) – 353*(Year)

Flag question: Question 7

Question 7 1 pts

The data below represents sales recorded of merchandise sold at different shelf heights. Copy the data into Excel. What is the R squared? Please round to 4 decimal places (0.0000)

Note: Copying the numbers only, excluding the title row, should make it easy to copy into Excel.

Observation Height in feet Sales

1 5 39

2 6 52

3 7 47

4 5 28

5 5 25

6 6 59

7 6 65

8 7 41

9 7 41

10 5 20

11 6 64

12 7 43

13 4 32

14 5 44

15 8 47

16 4 24

17 4 29

18 5 50

19 9 68

20 6 37

Flag question: Question 8

Question 8 1 pts

What is a good use of multiple regression analysis?

Group of answer choices

Measuring the impact of advertising by a team with no formal training in regression and no experience in using regression techniques.

Forecasting sales for next month

Predicting sales for a new product in a new industry with new customers

Flag question: Question 9

Question 9 1 pts

Can I include the month of the year in my regression analysis?

Group of answer choices

No, only quantitative variables can be included

Yes, use the numbers 1-12 to designate the months

Yes, create 11 variables with values 0 or 1 for the month

Yes, create 12 variables with values 0 or 1 for the month

Flag question: Question 10

Question 10 1 pts

How many points should be averaged to create a moving average for the data below?

Group of answer choices

4

12

7

2

Flag question: Question 11

Question 11 1 pts

Which of the following is NOT an example of seasonality?

Group of answer choices

Starbucks revenue grew 5% year over year last quarter

Jewelry stores see a significant spike in sales from Valentine’s Day every day year

The December holiday season is the busiest time of the year for most retailers

Text book sales spike in August and January based on the academic calendar year

Flag question: Question 12

Question 12 1 pts

Below is the output of a multiple regression measuring sales seasonality and trend, only. What is the trend?

Group of answer choices

Negative, by 34 units per month

Unknown, not enough data

Positive, by 34 units per month

Positive, by 5,684 units per month

Negative, by 3% per year

Flag question: Question 13

Question 13 1 pts

Below is the output of a multiple regression measuring sales seasonality and trend, only. How can the coefficient for August be interpreted?

Group of answer choices

August sales are 4,797 higher than December

August sales are 4,797 higher than the average month

August sales are 4,797 higher than July

August sales cannot be interpreted because the intercept is positive

Flag question: Question 14

Question 14 1 pts

Below is the output of a multiple regression measuring sales seasonality and trend, only. It is based on monthly dollar sales for Jun 2015 – May 2020. What is the forecast for Jun 2020?

Group of answer choices

6,383

8,554

6,429

10,042

12,116

14,190

No new data to save. Last checked at 8:55am

Explaining the bid-ask spread

This file uses the same data on bid-ask spreads (tradecost) and candidate determinants used in lab2 HW2.

We are interested in the relation between the average bid-ask spreads on stocks and the characteristics of the

corresponding companies.

The data file lab2.xls contains information for the 100 stocks in the S$&$P 100 index.

Our variable of interest (the $Y$ variable) is the bid-ask spread (constructed as an average over the day) – or

tradecost – of the

S&P100 stocks. The explanatory, or $X$, variables are:

- log volatility – The log of the daily return standard deviation
- log size – The log of the size of the stock. Size is total outstanding number of shares multiplied by share

price. Size is measured in thousands of dollars - log trades – This is the log of the average number of trades per day
- log turn – This is the log of the ratio of the average number of shares traded per day (in dollars) over the

number of shares outstanding (in dollars - NumberAnalysts – This is the number of analysts following the stock

.

. Import the data set and set up variable names

% import dataset lab2.xls (using Import Dataset)

% rename variables

tradecost = lab2{ : ,1};

logvolatility= lab2{ : ,2};

logsize = lab2{ : ,3};

logtrades = lab2{ : ,4};

logturn = lab2{ : ,5};

numberanalysts = lab2{ : ,6};

logtradecost = log(tradecost);

Run a regression of the log of the bid-ask spread on the 5 explanatory variables.

Q1. (2 points) Are all independent variables significant at a 3% significance level? Explain.

% INSERT CODE HERE

1

Answer:

Q2. (2 points)Test the assumption that the coefficient associated with logvolatility is equal to 1. If this is the

case, how would you interpret the relation between daily volatility and bid-ask srepads?

% INSERT CODE HERE

Answer:

Q3. (2 points) Test the assumption that the coefficients associated with logsize and logtrades are equal to each

other. Interpret the result.

% INSERT CODE HERE

Answer:

Q4. (2 points) Would you exclude any variable from the regression? Explain.

% INSERT CODE HERE, if necessary

2

Answer:

Q5. (2 points) Let us use this model to predict what the spread will look like tomorrow.

For prediction, use a regression which does not include the number of analysts to predict.

Consider a stock which has a log size of 10.5. Suppose that for this stock we expect that for the following day

the log turnover will be

-1.1, the log of the number of trades will be 7.6, and the log of the standard deviation will be -3.5.

Predict what the spread will be for this stock tomorrow. (Note that since the regression is run with log spreads

you