Principles of business forecasting keith ord pdf download

Principles of business forecasting keith ord pdf download

principles of business forecasting keith ord pdf download

Ord/Fildes Principles of Business Forecasting 1e Chapter 2 Solutions of business forecasting keith ord pdf download principles of business. Downloaded from diseinuak4web.net on. November 12 this principles of business forecasting by keith ord robert fildes, but end stirring in harmful downloads. Rather than esp webserver pdf, architecture for beginners. This second edition of Principles of Business Forecasting by Keith Ord, Robert Fildes, and Get your Kindle here, or download a FREE Kindle Reading App. principles of business forecasting keith ord pdf download

Principles of business forecasting 1st edition ord solutions manual  

Principles of Business Forecasting 1st Edition Ord Solutions Manual Download: diseinuak4web.net Chapter 2 â&#x;? Basic Tools for Forecasting Outline Solutions

Time series plot for temperature Time Series Plot of Temperature 80 70

Temperature

60 50 40 30 20 Month Jan Year

Jan

Jan

Jan

Jan

Jan

Jan

Jan

Jan Year

Seasonal plot: clear and stable seasonal pattern Scatterplot of Temperature vs N_month 80

Temperature

70 60

50 40 30 20 0

2

4

6 N_month

8

10

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Ord/Fildes Principles of Business Forecasting 1e Chapter 2 Solutions

(a) Scatter plot for injuries versus train miles

Š Cengage Learning

(b) Time plots for Injuries, Train Miles and Injuries per million train miles

.

2


Ord/Fildes Principles of Business Forecasting 1e Chapter 2 Solutions

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Š Cengage Learning

(c) The level of injuries fluctuates but shows an upward trend over time.

The standard Excel output shows that the median is unchanged but the mean is much smaller and the standard deviation much larger. A few outliers can greatly distort the analysis. Returns Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count

Returns with outlier #N/A

Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count

#N/A

The MAD is without the outlier and with the outlier.


Ord/Fildes Principles of Business Forecasting 1e Chapter 2 Solutions

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Means and medians are close suggesting a symmetric pattern. November and the winter months are somewhat more variable. Month JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

N

Mean 18 18 18 18 18 18 18 18 18 17 17 17

Std Dev Median MAD

The standard summary statistics are given in the table. To compute the MAD in each case, calculate the absolute values of the deviations about the mean and compute their average. Measure Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count

Variable MAD

Train-miles

Train-miles

Injuries #N/A

Injuries per T-M #N/A

Injuries

Injuries per T-M

Since the variables display a rising trend, the calculation of these averages is not particularly informative.


Ord/Fildes Principles of Business Forecasting 1e Chapter 2 Solutions

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The correlation between the and figures is The associated P-value is to 3 decimal places.

The three sets of results are given below. The variation among the first 50 values is much greater than the second 50, so that the results for the first 50 and for all are very similar. The high negative correlation between rank and return on capital (ROC) simply reflects the fact that the rank ordering placed heavy weight on ROC.

All Rank Return on Capital P-E Ratio

First 50 Rank Return on Capital P-E Ratio

Second 50 Column 1 Column 2 Column 3

Rank

Rank

Rank

Return on Capital Return on Capital Return on Capital

P-E Ratio

P-E Ratio

P-E Ratio

a. The mean and median are not useful, given the strong trend in the data. b. There is a clear slowing of percentage growth over time


Ord/Fildes Principles of Business Forecasting 1e Chapter 2 Solutions

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Š Cengage Learning

The use of the means produces apparently better results, but it should be recognized that the means include the observations actually being forecast. Only prior information should be used. As expected, one-month-ahead forecasts are not useful in this case. 12 month

ME MAE RMSE MASE

1-step

errors

Means errors

errors

The mean is around 36 degrees and the standard deviation is 11 degrees. With 18 observations, the 95% t-value from tables is so the prediction interval is * [,] . In other words, be prepared for almost any kind of weather. Clearly much better forecasts are possible closer to the event!!


Ord/Fildes Principles of Business Forecasting 1e Chapter 2 Solutions

.

Temperature Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count

The prediction limits are Âą* or Âą Out of monthly values 4 are below the lower limit and 2 are above the upper limit, making just under 3 percent overall.

There are two values below the lower limit and none above, possibly suggesting either milder weather or a cost-saving effort by the home-owner.

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Ord/Fildes Principles of Business Forecasting 1e Chapter 2 Solutions

Period

Actual

Forecast

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

8

Error Lower PI Upper PI

20

Counts

Below Lower Limit

Above Upper Limit

0 0 0 0 0 0 1 0 0 1 0 0 2

0 0 0 0 0 0 0 0 0 0 0 0 0

Let S = sales and I = inventory. Then the cost will be: C * S I , if S Cost = (I S), if I

I

S

=0, if I=S

Given the probabilities, we can compute the expected cost for given values of C and I. The solution may be determined numerically by various means. As expected, when C=1, the optimal level of inventory to hold is I= and the total expected cost is When C=3, the optimal level of inventory to hold is I= and the total expected cost is


Ord/Fildes Principles of Business Forecasting 1e Chapter 2 Solutions

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Minicases The aim is to be creative â€ in all the minicases. These summary results are designed to provide basic information and not a complete solution.

Minicase Baseball Salaries Histogram (with Normal Curve) of Salary ($s) 40

Mean StDev N

Frequency

30

20

10

0

0

Salary ($s) .

Descriptive Statistics: Salary ($s) Variable Salary ($s)

N

Mean

StDev

Minimum

Q1

Median

Q3

Maximum

Use results to discuss shape of distribution, etc. The matrix plot shows that there is a lot of variation even after allowing for the number of years played. Note the decline in salaries in later years. When the long serving pitchers are removed the positive relationship between years and salary becomes clearer (higher correlation). Correlations that change substantially are highlighted.


Ord/Fildes Principles of Business Forecasting 1e Chapter 2 Solutions

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Matrix Plot of Salary ($, Years in Maj, Career ERA, 0

10

20

0

0

Salary ($s)

0

20

10

Years in Majors

0 5 4

Career ERA

3

Innings Pitched

0

Career Wins

0 Career Losses

0 0

3

4

5

0

.

Matrix Plot of Salary_sorte, years_sorted, ERA_sorted, IP_sorted, 0

5

10

0

0

50

Salary_s orted

0

10 5

years _sorted

0 5 4

ERA_s orted

3

IP _s orted

0

80

CW_s orted

0 50

CL_s orted

0 0

.

3

4

5

0

80


Ord/Fildes Principles of Business Forecasting 1e Chapter 2 Solutions

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Correlations: Salary ($, Years in Maj, Career ERA, Innings Pitc, Salary ($s)

Years in Majors

Innings Pitched

Career Wins

Career Losses

Innings Pitched

Career Wins

Years in Majors Career ERA

Career Wins Career Losses

Career ERA

Cell Contents: Pearson correlation P-Value Correlations: Salary_sorte, years_sorted, ERA_sorted, IP_sorted, CW_sorted, Salary_sorted

years_sorted

IP_sorted

CW_sorted

CL_sorted

years_sorted ERA_sorted

CL_sorted

CW_sorted

Cell Contents: Pearson correlation P-Value

ERA_sorted

IP_sorted


Ord/Fildes Principles of Business Forecasting 1e Chapter 2 Solutions

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Minicase Whither Walmart? The plot shows clearly the movement towards Super Stores and away from the regular stores; some were doubtless conversions. The Sam’s Club count is stable over the first half of the period but shows a steady growth in later years. These trends are likely to persist in later years. Time Series Plot of Walmart Stores, Super Stores, Sam_s Club Variable W almart Stores Super Stores Sam_s C lub

Data

0

Year .

As for many other companies, Walmart experienced a slowdown in growth as the result of the “Great Recession”. Scatterplot of Growth by Quarter

Growth

.

Quarter


Ord/Fildes Principles of Business Forecasting 1e Chapter 2 Solutions

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Minicase Economic Recessions Descriptive Statistics: Duration, Gap Variable Duration Gap

N 14 13

Mean

SE Mean

StDev

Minimum

Median

Maximum

One-Sample T: Duration, Gap Variable Duration Gap

N 14 13

Mean

StDev

SE Mean

95% CI ( , ) (, )

The length of the Great Depression from on greatly affects the analysis. Discuss the impact of omitting this observation. It is clear from the data that the â€&#x;business cycleâ€? can vary considerably in length, a point worth making in anticipation of the discussion of seasonality later. There is essentially no evidence that the length of a growth period is related to the length of a recession either preceding or following it. Correlations: Gap, Duration _after, Duration_before Duration _after Duration_before

Gap

Duration _after

Cell Contents: Pearson correlation P-Value


Ord/Fildes Principles of Business Forecasting 1e Chapter 2 Solutions

Principles of Business Forecasting 1st Edition Ord Solutions Manual Download: diseinuak4web.net principles of business forecasting pdf principles of business forecasting keith ord pdf principles of business forecasting keith ord pdf download principles of business forecasting 2nd edition principles of business forecasting ord pdf principles of business forecasting pdf download forecasting: principles and practice

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Principles of business forecasting keith ord pdf download

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