Coursework

Question from chapter 5 1

Chapter5: Question 4 under `Demand Estimation.`

4)Startwith four columns:&nbsp&quotx&quot,&quot(x-Mx)2&quot,&quoty&quot, &quot(y-My)2&quot

  1. Input the data for group X in column&nbspx, and similarly for group Y in column&nbspy

  2. Compute mean for the two groups

  3. Compute deviation scores for the groups by subtracting each score from its group mean, square it and put these in the columns &quot(x-Mx)2&quot and&nbsp&quot(y-My)2&quot

  4. Sum the squared deviation scores for individual groups

  5. Calculate&nbspS2&nbspfor each of the groups

  6. Initiate the formula

  7. Compute&nbspt

  8. Confirm to establish whether t is statistically significant on the probability table with&nbspdf&nbsp=&nbspN-2 and&nbspp&nbsp&lt .05 (N&nbsp= total number of scores)

Formula

M&nbsp= mean&nbspn&nbsp= number of scores per group

x&nbsp= individual scoresM&nbsp= meann= number of scores in group

Thebasis for using the rule is that absolute value of t is greater than2.The estimated coefficient is also significant at the 5% level(Davisand Pecar, 2013). Without the basis, it cannot be utilized. It isadvised to check the assumption before starting computation.

Questionsfour and eight under `Forecasting.`

CHAPTER5 Questions 4

4)a) When a company experiences a rise in the number of orders forgoods, It indicates that its production of customers’ items willincrease in the future. If new requests are fewer than in the past,then there will be a drop in the consumer goods to be produced in thefuture. The reverse is also true. When a business experiences adecline in the number of products ordered, then the production ofconsumer goods will fall in the future

Whena business experiences a rise in orders for nondefense capital goods,it indicates that the firms will produce more capital goods in thetimes to come. When there are fewer orders of products of this kind,the production of capital goods will be lower in future.

b)The index of industrial production is an appropriate coincidentindicator because changes in the level of this indicator usually showsimilar changes in overall economic activity, and therefore&nbspgrossdomestic product&nbsp(GDP). The index show changes onmonth-to-month and year-over-year levels. It thus indicatesshort-term ratesof change&nbspand business cycle growth, respectively.

c)The Federal Reserve places this interest rate to respond to economicgrowth rates. It is also to stimulate growth. It will be set low fora period after the economy is recovering. Many banks use the primerate to price loan products like student loans and credit card.

8)Exponential smoothing smoothens data. It helps in studies of airturbulence where you would expect significant spikes in data.

Movingaverage is frequently employed to&nbsptimeseries&nbspdata to smooth out short-term fluctuations andemphasize longer-term trends or cycles. The threshold between shortand long-term depends on the application, and the parameters of themoving average set accordingly. For example, in&nbsptechnicalanalysis&nbspof financial information like stock&nbspprices.

Bothtechniques apply when random fluctuations occur as opposed tocyclical and seasonal variations. It is also advisable to use the twowhen the direction of the series changes infrequently, and the serieslacks a strong trend.

Themoving average method is more useful than exponential smoothingbecause it emphasized long-term trends. It provides greaterflexibility by providing more control on the weighting.

Problem2 under `Demand Estimation.`

Month

Price

Quantity

Jan

12500

15

Feb

12200

17

March

11900

16

April

12000

18

May

11800

20

June

12500

18

July

11700

22

August

12100

15

Sept

11400

22

Oct

11400

25

Nov

11200

24

Dec

11000

30

Jan

10800

25

Feb

10000

28

a)Discounts will result in an increase in quantity sold and henceshould be adopted. As the price goes down, the quantity demanded ofthe commodity increases.

b)Other factors in the regression analysis are income and revenue.

Itmay be difficult to plot the other elements due to full utilizationof the axes by current factors, and also there will be a need tocollect additional data to establish how they impact price andquantity. Customers may also be unwilling to disclose personalinformation like their income for the study.

Problems8 and ten under `Forecasting`.

St=43.6+0.8t

Month

Index

t for 2013 Months

St=43.6+0.8t

Forecast 2013 =St*Index

Jan

60

61

92.4

5544

Feb

70

62

93.2

6524

March

85

63

94

7990

April

110

64

94.8

10428

May

110

65

95.6

10516

June

100

66

96.4

9640

July

90

67

97.2

8748

August

80

68

98

7840

September

95

69

98.8

9386

Oct

110

70

99.6

10956

Nov

140

71

100.4

14056

Dec

150

72

101.2

15180

t=sumof months from Jan 2008 to 2013 with Jan 2013 being month 61, Feb2013 being month 62. The fourth column, title forecast, shows theforecasted monthly sales for the year 2013.

Thet= is replaced in the formulae St= 43.6+0.8t to get the monthlysales. St is then multiplied by the index to arrive at the salesforecast for 2013

Problem10 under forecasting

Problem 10 forecasting

Growth rate

0.015

Intercept

1376

Forecasted Temp

-17.1

Prior day Temp

-3.7

Forecasted wind speeds

4.2

Q=(1+G)*(a+b1T+b2P+b3W)

Substituting in the equation

Q=(1+0.015)*(1376-17.1*40+-3.7*37+4.2*8)

597.5305

Thecompany demand forecast equation is Q= (1+G)*(a+b1T+b2P+b3W).With the coefficients provided, we substitute. This enables us to getthe future demand. We are thus able to predict gas demand for thatday.

ModelF

Thismodel has two cross price independent variables. Both arestatistically significant and act as a substitute and a compliment toZinfandel. Merlot’s variable becomes significant at a level of 10%.Model F is thus the best specification. Its adjusted R2 isthe highest, and its standard of the estimate is the lowest when wecompare to the rest of the regression on the table (Keat,Young and Erfle, 2013).Merlot compliments while Chardonnay substitutes white zinfandel inthe specification.

ModelG

Ithas two independent variables that are the cross price. Onesubstitute while the other compliments white zinfandel (Keat,Youngand Erfle, 2013). They are both statistically insignificant. It isevident in the attached workbook.

ModelH

Apartfrom having the price of white zinfandel, this model also includesthree cross prices. The additional file attached provides thisanalysis. It is possible to see how this H model compares to therest. We also see its shortfall when it comes to statisticalsignificance.

ExcelInterpretation

Kindlynote that the count that I was able to use was only 17 since thosewere the only visible observations from your jpeg pictures. Kindlyimpute the other rows (19 to 52) and include them in the formula forregression to obtain the exact duplication of your data. This linkwill show you how to manipulate the data to achieve this. http://www.excel-easy.com/examples/regression.html

RSquare 0.98. This fit is good. 96% of the variations in the quantitysold is due to variables InQWZ, InPWZ, InPCH, InPCS, InPM, time, andPeak.

Pand F-Values.

Tosee if our results are reliably significant when it comes tostatistics, we look at the F significance value=3.3900.

Weproceed to delete the variable whose p values that exceed 0.5 untilthe significance falls below 0.5.

Thecoefficients are used to derive an equation for forecasting.

Theresiduals tell us how far our data is from the predicted points ofdata. These residuals can be used to create a scatter plot

Reference

Keat,P.G., Young, P.K.Y. &amp Erfle, S.E. (2013). Managerial Economics(7th edition).UpperSaddle River, NJ: Pearson Prentice Hall.