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

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?

No

Yes

Flag question: Question 6
Question 6 1 pts
Based on the Excel output, what is the regression equation?

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?

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?

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?

4

12

7

2

Flag question: Question 11
Question 11 1 pts
Which of the following is NOT an example of seasonality?

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?

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?

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?

6,383

8,554

6,429

10,042

12,116

14,190
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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
S&P100 stocks. The explanatory, or \$X\$, variables are:

1. log volatility – The log of the daily return standard deviation
2. 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
3. log trades – This is the log of the average number of trades per day
4. 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
5. 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
logvolatility= lab2{ : ,2};
logsize = lab2{ : ,3};
logturn = lab2{ : ,5};
numberanalysts = lab2{ : ,6};

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

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

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

Q4. (2 points) Would you exclude any variable from the regression? Explain.
% INSERT CODE HERE, if necessary
2

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

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