a) RCB manufacturers black & white television sets for overseas markets. Annual exports in thousands of units are tabulated below for the past 6 years. Given the long term decline in exports, forecast the expected number of units to be exported next year. |Year |Exports |Year |Exports | |1 |33 |4 |26 | |2 |32 |5 |27 | |3 |29 |6 |24 |

b) A small hospital is planning for future needs in its maternity wing. The data below show the number of births in each of the past eight years. |Year |Births |Year |Births | |1 |565 |5 |615 | |2 |590 |6 |611 | |3 |583 |7 |610 | |4 |597 |8 |623 |

Use simple linear regression to forecast the annual number of births for each of the next three years. Determine the coefficient of determination for the data and interpret its meaning.

Moving Averages

IPC’s Plant estimates weekly demand for its many materials held in inventory. One such part, the CTR 5922, is being studied. The most recent 12 weeks of demand for the CTR 5922 are : |Week |Demand in units |Week |Demand in units | |1 |169 |7...

...
Unit 5 – RegressionAnalysis
Mikeja R. Cherry
American InterContinental University
Abstract
In this brief, I will demonstrate selected perceptions of the company Nordstrom, Inc., a retailer that specializes in fashion apparel with over 12 million dollars in sales last year. I will research, review, and analyze perceptions of the company, create graphs to show qualitative and quantitative analysis, and provide a summary of my findings.
Introduction
Nordstrom, Inc. is a retailer that specializes in fashion apparel for men, women and kids that was founded in 1901. The company is headquartered in Seattle, Washington with over 61,000 employees world-wide as of February 2, 2013. (Business Wire, 2014)
Nordstrom, Inc. offers on online store, e-commerce, retail stores, mobile commerce and catalogs to its consumers. It operates 117 full-line stores within the United States and 1 store in Canada, 167 Nordstrom Rack stores, 1 clearance store under the Last Chance Banner, 1 philanthropic treasure & bond store called Trunk Club and 2 Jeffrey boutiques. The option of shopping online is also available at www.nordstrom.com along with an online private sale subsidiary Hautelook. They have warehouses, also called fulfillment centers, which manages majority of their shipping needs that are located in Cedar Rapids, Iowa. (Business Source Premier, 2014)
Nordstrom, Inc. continues to make investments in their e-commerce...

...
Forecasting
HSM/260
January 17, 2014
Janice Gilstorff
Forecasting
Exercise 9.1
Forecasting is a guess of what the financial future holds (production output or sales). In the scenario in the book exercise 9.1 they want you to forecast what the 20X5 figures would be. It does give you some background information, such as the Human services expenses over the past four years.
20X1 [$5,250,000]
20X2 [$5,500,000]
20X3 [$6,000,000]
20X4 [$6,750,000]
Weighted moving averages and moving averages, just use the data for the past three fiscal years. This would look like this
Moving Averages-
20X2 [$5,500,000]
20X3 [$6,000,000]
20X4 [$6,750,000]
20X5 [$6,083,000]
With just the three we already knew the total of $18,250,000. If you divide the total by three you get, $6,083,000.
Weight averages-
20X2 $5,500,000 1=$5,500,000
20X3 $6,000,000 2=12,000,000
20X4 $6,750,000 3=$20,250,000
20X5 $6,300,000 6=$37,750,000
For 20X5 I divided by 6 (which represents the values 1+2+3=6), which equals $6,291,667 or $6,300,000 as a weighted average. From the information gathered a prediction for the forecast can be made.
Exponentialsmoothing:
The alpha method of 0.95 would work here. The formula would look like this: NF=LF + a (LD- LF)
Last Forecast (LF) = $6,300,000
Last Data (LD) = $6,750,000
a = 0.9
NF = LF + (LD LF)
NF = 6,300,000 +...

...associated with a β1 change in Y.
(iii) The interpretation of the slope coefficient in the model ln(Yi ) = β0 + β1 ln(Xi ) + ui is as
follows:
(a) a 1% change in X is associated with a β1 % change in Y.
(b) a change in X by one unit is associated with a β1 change in Y.
(c) a change in X by one unit is associated with a 100β1 % change in Y.
(d) a 1% change in X is associated with a change in Y of 0.01β1 .
(iv) To decide whether Yi = β0 + β1 X + ui or ln(Yi ) = β0 + β1 X + ui fits the data better, you
cannot consult the regression R2 because
(a) ln(Y) may be negative for 0 < Y < 1.
(b) the TSS are not measured in the same units between the two models.
(c) the slope no longer indicates the effect of a unit change of X on Y in the log-linear
model.
(d) the regression R2 can be greater than one in the second model.
1
(v) The exponential function
(a) is the inverse of the natural logarithm function.
(b) does not play an important role in modeling nonlinear regression functions in econometrics.
(c) can be written as exp(ex ).
(d) is ex , where e is 3.1415...
(vi) The following are properties of the logarithm function with the exception of
(a) ln(1/x) = −ln(x).
(b) ln(a + x) = ln(a) + ln(x).
(c) ln(ax) = ln(a) + ln(x).
(d) ln(xa) = aln(x).
(vii) In the log-log model, the slope coefficient indicates
(a) the effect that a unit change in X has on Y.
(b) the elasticity of Y with respect to X.
(c) ∆Y/∆X.
(d)
∆Y
∆X
×
Y
X...

...Choose one of the forecasting methods and explain the rationale behind using it in real life.
I would choose to use the exponentialsmoothing forecast method. Exponentialsmoothing method is an average method that reacts more strongly to recent changes in demand than to more distant past data. Using this data will show how the forecast will react more strongly to immediate changes in the data. This is good to examine when dealing with seasonal patterns and trends that may be taking place. I would find this information very useful when examining the increased production of a product that appears to be higher in demand in the present than in the past Taylor (2011). For example, annual sales of toys will probably peak in the months of March and April, and perhaps during the summer with a much smaller peak. This pattern is likely to repeat every year, however, the relative amount of increase in sales during March may slowly change from year to year. During the month of march the sales for a particular toy may increase by 1 million dollars every year. We could add to our forecasts for every March the amount of 1 million dollars to account for this seasonal fluctuation.
Describe how a domestic fast food chain with plans for expanding into China would be able to use a forecasting model.
By looking at the data of other companies the fast food chain would be able to put together a...

...with
weights of0.50 for the immediate preceding year and 0.3, 0.15, and 0.05 for the
three years before that?
F2013 = 0.50A2012 + 0.3A2011 + 0.15A2010 + 0.05A2009
=0.50(83000) + 0.30(67000) + 0.15(64000) + 0.05(48000)
= 41,500 + 20,100 + 9,600 + 2,400
= $73,600
$73,600 is the forecast for 2013
Q2. Using exponentialsmoothing with a weight of 0.6 on actual values:
a) If sales are $45,000 and $50,000 for 2010 and 2011, what would you forecast for 2012?
(The first forecast is equal to the actual value of the preceding year.)
Actual values are
2010: $45,000
2011: $50,000
α = 0.6
F2012 = 0.60A2011 + 0.40A2010
= 0.60(50000) + 0.40(45000)
=48000
Forecast for 2012 is $48,000
b) Given this forecast and actual 2012 sales of $53,000, what would you then forecast for2009?
Actual value of 2012 = $53,000
F2009 =
Q3. In question 4-1, taking actual 2009 sales of $48,000 as the forecast for 2010, what sales
would you forecast for 2011, 2012, and 2013, using exponentialsmoothing and a weight
a on actual values of (a) 0.4 and (b) 0.8?
a) α = 0.4
Actual values of 2009 = $48,000 and it is forecasted for 2010
We have an Actual value for 2010 = $64,000
F2011 = 0.4(64,000) + 0.6(48,000)
F2011= $54,400
Now we have both actual and forecasted values for 2011
Actual value for 2011= $67,000
F2012 = 0.4(67,000) +...

...increased from $320 to $360. He was concerned about the accounting procedure that increased his capital cost from $375,000 to $620,000, but earlier discussions with boss suggested that there was nothing that could be done about the allocation.
Bob wanted if his productivity had increased at all. He called Sharon and conveyed the above information to her and asked her to prepare this part of the report.
Discussion Question;
Prepare the productivity part of the report for Mr Richards. He probably expects some analysis of productivity inputs for all factors, as well as a multifactor analysis for both years with the change in productivity (up or down) and the amount noted.
Solution Q 1 [20 marks]
Question # 2 [20 Marks]
Consider the following two techniques for forecasting F1 and F2. The actual and the two sets of forecast are as follows
|Period |Demand |F1 |F2 |
|1 |68 |66 |66 |
|2 |75 |68 |68 |
|3 |70 |72 |70 |
|4 |74 |71 |72 |
|5 |69 |72 |74 |...

...Introduction
This presentation on RegressionAnalysis will relate to a simple regression model. Initially, the regression model and the regression equation will be explored. As well, there will be a brief look into estimated regression equation. This case study that will be used involves a large Chinese Food restaurant chain.
Business Case
In this instance, the restaurant chain's management wants to determine the best locations in which to expand their restaurant business. So far the most successful locations have been near college campuses. This opinion is based on the positive numbers that quarterly sales (y) reflect and the size of the student population (x). Management's mindset is that over all, the restaurants that are within close proximity to college campuses with large student bodies generate more sales than restaurants located near campuses with small student bodies.
In the sample box below, xi is the size of the student population (in thousands) and yi is the quarterly sale (in thousands of dollars). The value for xi and yi for all of the 10 Chinese Food restaurants given in the sample are reflected as follows:
Sample Data:
(measured in 1,000s) (measured in $1,000s)
Restaurant Student Population Quarterly Sales
(i) (xi) (yi)
1 2 58
2 6 105
3 8 88
4 8 118
5 12 117
6 16 137
7 20 157
8 20 169
9 22 149
10 26 202
Methodology
Given the...

...RegressionAnalysis Exercises
1- A farmer wanted to find the relationship between the amount of fertilizer used and the yield of corn. He selected seven acres of his land on which he used different amounts of fertilizer to grow corn. The following table gives the amount (in pounds) of fertilizer used and the yield (in bushels) of corn for each of the seven acres.
|Fertilizer Used |Yield of Corn |
|120 |138 |
|80 |112 |
|100 |129 |
|70 |96 |
|88 |119 |
|75 |104 |
|110 |134 |
a. With the amount of fertilizer used as an independent variable and yield of corn as a...