Excel work that shows the trend of Euro-Dollar Exchange Rate Statistics from January of 2006 to December of 2008 (three year span).

1. Excel Project 2 for all Classes Due Feb 25 for Wednesday Classes and Feb 26 for Thursday Class
You have already received 10 points for identifying your data. You should now use that data or
something similar for Excel Project 2.
The other tabs are samples of how to represent your data. Histograms can represent numerical data
where it is meaningful to interpret the mean, variance and standard deviation
Pie Charts and Pareto Charts can be used to represent percentage data
Contingency Tables can be used to represent two variable data.
Once you have chosen your method to describe your data follow the information needed to achieve the
following 40 points. These are only samples. You may choose to represent your data differently.
Your goal is to present your raw data, describe the data, use some form of chart or picture to represent
your data and use the summary statistics in your conclusions.

2. Column1
Mean 1.37
Standard Error 0.02
Median 1.34
Mode #N/A
Standard Deviation 0.11
Sample Variance 0.01
Kurtosis -0.83
Skewness 0.48
Range 0.39
Minimum 1.19
Maximum 1.58
Sum 49.16
Count 36

3. MS 1023 Name: Gideon Roberts ID Number: @01135781 Sorted Data
1.1900
Step 1: Raw Data
1.2000
The information presented here is the exchange rate between the Euro to the Dollar between the
timespan of January of 2006 and December of 2008 (3 years).
2006 1.2103
Months 1-6 1.2103 1.1938 1.2020 1.2271 1.2770 1.2650 1.2271
Months 7-12 1.2684 1.2811 1.2727 1.2611 1.2881 1.3213 1.2611
2007 1.2650
Months 1-6 1.2999 1.3074 1.3242 1.3516 1.3511 1.3419 1.2684
Months 7-12 1.3716 1.3622 1.3896 1.4227 1.4684 1.4570 1.2727
2008 1.2732
Months 1-6 1.4718 1.4748 1.5527 1.5751 1.5557 1.5553 1.2770
Months 7-12 1.5770 1.4975 1.4370 1.3322 1.2732 1.3449 1.2811
1.2881
1.2999
1.3074
Step 2: Frequency
Relative Cumulative
Class Relative Percent Cumulative Percent 1.3213
Class Interval Frequency Midpoint Frequency Frequency Frequency Frequency
1.15 ≤ 1.22 3 0.08 8% 3 8%
1.1850 1.3242
1.22 ≤ 1.30 10 1.26 0.28 28% 13 36% 1.3322
1.30 ≤ 1.37 9 1.34 0.25 25% 22 61% 1.3419
1.37 ≤ 1.45 4 1.41 0.11 11% 26 72% 1.3449
1.45 ≤ 1.52 5 1.49 0.14 14% 31 86% 1.3511
1.52 ≤ 1.60 5 1.56 0.14 14% 36 100% 1.3516
1.3622
totals 1.3716
36 1 100%
1.3896
Step 3: Create the histogram using the Class Interval and Frequency columns. 1.4227
1.4370
Exchange Rate Euro-Dollar 1.4570
1.4684
12
1.4718
1.4748
10
1.4975
8 1.5527
Frequency
1.5553
6
1.5557
Frequency
1.5751
4
1.5770
2
0
1.22 ≤ 1.30 1.37 ≤ 1.45 1.52 ≤ 1.60
1.15 ≤ 1.22 1.30 ≤ 1.37 1.45 ≤ 1.52
Exchange Rate Ranges

4. Step 4: Summary Statistics and Conclusion
Sorted Data Column1
Mean 1.37
Standard Error 0.02
Median 1.34
Mode #N/A
Standard Deviation 0.11
Sample Variance 0.01
Kurtosis -0.83
Skewness 0.48
Range 0.39
Minimum 1.19
Maximum 1.58
Sum 49.16
Count 36
The information collected here is from http://www.statistics.dnb.nl/index.cgi?
lang=uk&todo=Koersen. This information is useful for identifying the trend of
exchange rates between the European Euro and United States Dollar over a
course of three years (three most recent years). The above sample is taken
from 36 exchange rates between the Euro and Dollar. The average exchange
rate (mean) throughout these three years has been calculated at 1.37
Dollars/Euro. All the sample numbers fall within two standard deviations below
the mean (approximately 1.14) and almost within two standard deviations above
the mean (approximately 1.57). It is interesting that after a steady rise in the
value of the Euro in comparison to the Dollar, the Euro's value fell continually for
five straight months (the last five months of 2008). Thus, it stands to reason that
by utilizing this information, an individual or organization can develop strategic
business tactics on how he, she, or they can plan operations appropriate for the
difference of exchange rates between the two nations.

5. MS 1023 Name: ID Number:
Step 1: (10 points already awarded) Identify your data and provide a short
description of the data.
Where are Soft Drinks Sold?
Step 2: 10 points. Create the Pie Chart for your data
Places of Sales Percentages
Supermarket 44
Fountain 24
Convenience stores 16
Vending 11
Mass Merchandisers 3
Drugstores 2
Where are Soft Drinks Sold
2% 3%
11%
44%
16%
24%
Row 6 Row 7 Row 8 Row 9 Row 10 Row 11
Step 3: 10 points. Create a Pareto Chart for your data
Where are Soft Drinks Sold
50
44
45
40
35
30
Percentages
24 Column B
25
Column C
20 16
15 Column D
11
10
3 2
5
0
Fountain Vending Drugstores
Supermarket Convenience stores Mass Merchandisers
Place of Sales
Step 4: 20 points. Provide a summary analysis of your data results.

6. The above data was taken from your text book. The collection of this data is useful to all soft drink
companies because it readily shows the distribution of sales across six different types of merchants.
Supermarkets are the largest distributor of sales at 44%, where fountain drinks are second at 24%.
Convenience store and vending machine sales contribute 16 and 11 percent respectively. Mass
merchandisers like Costco and Sams have only 3% of sales and drugstores only 2%. Since sixty-
eight percent of the product is sold in supermarkets and related fountain drinks companies realize
quickly that this product requires continuous monitoring to ensure quality. The remaining thirty-two
percent of the product can be monitored for quality at less regular time intervals. This data should be
collected quarterly by each manufacturer to ensure quality products are on the shelves and older
merchandise
1 reference
2 describe the question of interest
3 use summary statistics
4 conclusion

7. MS 1023 Name: ID Number:
Step 1: (10 points already awarded) Identify your data and provide a short
description of the data.
We found data about mortality rates for persons wearing and not wearing a seat
belt.
Step 2: 10 points. Create the Raw Data Value Matrix for your data
Raw Data Value Matrix
Survived Died
S D total
Yes 412368 510 412878
Wearing a
Seat Belt No 162527 1601 164128
total 574895 2111 577006
Step 3: 10 points. Create the Probability Data Value Matrix for your data
Probability Data Value Matrix
Survived Died
S D total
Yes 0.7147 0.0009 0.7156
Wearing a
Seat Belt No 0.2817 0.0028 0.2844
total 0.9963 0.0037 1.0000
Step 4: 20 points. Provide a summary analysis of your data results.
The above information was taken from your text book. This information is researched because many
times we find ourselves wondering what the probabilities are of surviving an accident given that the
person was wearing a seat belt. The above data was collected on 577,006 persons. The raw data
value matrix shows that 412,878 persons , 72 %, were wearing a seat belt and 164,128, or 28% of the
people were not wearing a seat belt at the time of an accident. The raw data matrix also shows that
574, 895 , 99.6 %, of the people survived and 2,111 persons , less than 1 %, did not survive an
accident.
You should continue with conclusions based on P (S), P (D), P(SUD), P(S|D) and so forth.
1 reference
2 describe the question of interest
3 use summary statistics
4 conclusion