Employee ID Location
2344152 Camarillo
1135227 Camarillo
6885359 Camarillo
3194126 Camarillo
6763243 Camarillo
3522856 Camarillo
9115198 Camarillo
8225575 Camarillo
7140359 Camarillo
4131055 Camarillo
5026806 Camarillo
2770892 Camarillo
3550951 Camarillo
4394341 Camarillo
1273332 Camarillo
4658642 Camarillo
6949103 Camarillo
8643358 Camarillo
2943010 Camarillo
8128385 Camarillo
2684677 Camarillo
2766353 Camarillo
7155324 Camarillo
2292744 Camarillo
9976781 Camarillo
2294794 Camarillo
5386391 Camarillo
5291092 Camarillo
6516341 Camarillo
6788139 Camarillo
6199094 Camarillo
4373074 Camarillo
6275171 Camarillo
4625744 Camarillo
9971063 Camarillo
5126823 Camarillo
3337726 Camarillo
5036199 Camarillo
5882232 Camarillo
1674213 Camarillo
6660831 Camarillo
8314391 Camarillo
6074817 Camarillo
2182537 Camarillo
5531623 Camarillo
6557304 Camarillo
Female (=1 if yes) Age (years) Relevant Experience (years) Sales Goal (units)
0
35
13
12760
0
52
33
32750
0
33
14
13740
0
51
30
29760
0
47
26
25710
0
33
15
14700
0
54
32
32090
0
23
1
10000
0
52
34
33970
0
54
33
32970
0
43
22
21730
0
54
36
36080
0
28
9
10000
0
55
35
34860
0
35
17
16920
0
56
36
35990
0
27
8
10000
0
30
9
10000
0
37
19
18950
0
27
5
10000
0
48
30
30050
0
52
33
33010
0
32
12
11910
0
56
36
36000
0
47
28
27850
0
38
19
18710
0
56
38
37970
0
33
14
13820
0
49
29
29010
0
42
20
19710
0
31
11
11080
1
39
21
20840
1
40
18
17860
1
51
32
31800
1
45
26
25700
1
28
6
10000
1
51
29
28800
1
48
27
26880
1
43
23
22880
1
30
8
10000
1
43
24
23900
1
50
31
30810
1
52
30
29950
1
47
29
28910
1
26
8
10000
1
43
21
21010
8779991 Camarillo
4838450 Camarillo
5305586 Camarillo
7574051 Camarillo
7854223 Camarillo
5000581 Camarillo
1015851 Camarillo
6655831 Camarillo
6349232 Camarillo
9528021 Santa Barbara
2080445 Santa Barbara
3125708 Santa Barbara
2185773 Santa Barbara
1176645 Santa Barbara
9648958 Santa Barbara
6405128 Santa Barbara
5872559 Santa Barbara
2208145 Santa Barbara
4667934 Santa Barbara
1269458 Santa Barbara
1066795 Santa Barbara
8665929 Santa Barbara
4774077 Santa Barbara
2344142 Santa Barbara
8787303 Santa Barbara
5396747 Santa Barbara
6005417 Santa Barbara
8824391 Santa Barbara
1516065 Santa Barbara
2720285 Santa Barbara
9158381 Santa Barbara
9558048 Santa Barbara
1553933 Ventura
4785494 Ventura
4800970 Ventura
4062565 Ventura
9108605 Ventura
5956566 Ventura
7518596 Ventura
2959406 Ventura
1887370 Ventura
3591224 Ventura
9560112 Ventura
9366270 Ventura
3609639 Ventura
6941803 Ventura
8113738 Ventura
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
29
24
46
48
53
37
38
53
56
32
31
38
47
54
56
41
31
27
53
22
51
51
23
51
36
30
29
23
48
53
56
55
49
30
31
36
36
26
33
32
25
36
30
36
38
27
33
10
6
24
28
35
18
18
33
38
13
13
16
27
35
34
19
11
6
35
1
32
32
2
33
16
10
7
5
28
31
37
36
28
11
9
15
18
7
15
12
5
15
8
15
17
7
11
10090
10000
24070
28030
35090
17990
17720
32880
38050
12850
12890
16090
26970
35100
34040
18890
10880
10000
34700
10000
31950
31920
10000
33060
15840
10000
10000
10000
27850
31090
36750
35960
27880
10700
10000
14970
18090
10000
15100
12010
10000
14880
10000
14740
16920
10000
10840
9644300 Ventura
8557778 Ventura
9385443 Ventura
8311325 Ventura
9098285 Ventura
7759709 Ventura
2975684 Ventura
3355797 Ventura
7265633 Ventura
2719479 Ventura
9926005 Ventura
5059251 Ventura
5849889 Ventura
9349164 Ventura
8033482 Ventura
4728868 Ventura
6740882 Ventura
5692729 Ventura
9683217 Ventura
5209152 Ventura
2571228 Ventura
3791970 Ventura
8853761 Ventura
2836660 Ventura
6880424 Ventura
2405401 Ventura
4604958 Ventura
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
38
40
44
40
34
27
22
53
37
47
41
25
31
55
33
56
39
55
25
38
38
50
47
47
45
37
47
18
19
22
19
12
5
1
35
15
25
20
7
9
35
15
34
19
36
3
18
17
31
25
28
26
19
29
17780
19000
22000
18740
11720
10000
10000
34820
14760
24900
20100
10000
10000
34850
14850
33870
18730
35950
10000
17820
16700
30960
24820
28010
25770
19080
28870
Sales (Units) Base Pay
Bonus
Total Pay
3334 91915.64873 2612.465635 94528.11437
18960 164309.275 5789.312309 170098.5873
12888 98142.49331 9379.909686 107522.403
22993 153341.9182 7726.003042 161067.9212
14353 137869.868 5582.751198 143452.6192
6091 94972.95846 4143.676033 99116.63449
17011 160326.0432 5301.024963 165627.0681
6474 69581.3983 6473.553346 76054.95164
27313 172608.725 8040.370081 180649.0951
14968 159384.8244 4539.833577 163924.658
21513 124794.3996 9900.053982 134694.4536
30241 183465.8116 8381.749758 191847.5614
12042 78390.71727 17041.68347 95432.40073
31141 171787.3166 8933.083462
180720.4
5532 102235.8688 3269.735764 105505.6046
37583 174056.1886 15442.54535 189498.7339
11823 78614.17492 16822.69049 95436.86541
8915 87705.41699 8914.73498 96620.15197
23000 113405.0662 17136.98965 130542.0559
9620 76526.43847 9620.286943 86146.72541
24244 154327.6069 8067.922546 162395.5294
10926 168218.9996 3309.926935 171528.9265
14242 91624.85216 16957.88447 108582.7366
34416 179377.0765 9559.952169 188937.0287
12889 143033.5262 4627.980446 147661.5067
13885 113286.8249 7421.244231 120708.0691
45427 185329.1453 16963.91764 202293.0629
9653 97663.45549 6985.163726 104648.6192
17958 141917.0304 6190.263495 148107.2939
20355 108681.0215 15327.29186 124008.3133
13533 84222.85245 17213.8001 101436.6525
17077 113143.0674 8194.430338 121337.4978
11118 106429.6511 6224.933667 112654.5848
13959 155058.4075 4389.689262 159448.0967
15941 134831.4225 6202.866197 141034.2887
12488 72903.17295 17488.23235 90391.4053
8283 148762.8332 2876.108363 151638.9416
22578 135340.5131
8399.4236 143739.9367
13683 118694.2064 5980.473009 124674.6794
9281 82978.71708 9281.293054 92260.01014
14568 128640.1654 6095.558174 134735.7235
16283 155442.787 5285.004996 160727.792
11201 154302.6815 3739.776099 158042.4576
16320 147395.6062 5645.177605 153040.7838
7592 80741.48205 7592.19434 88333.67639
16928 113793.0346 8057.118579 121850.1532
10778 82747.78114
3571 72407.47492
28831 131281.8971
28721 141477.3557
9501 170188.282
16578 110618.5691
18324 106649.0187
19021 164553.0334
12879
190053.67
9657 93911.84833
3283 95018.03638
4691 101600.1213
19425 135325.2458
31828 169356.6038
20272 165576.243
5176 108753.2114
5048 84483.63749
3121 73770.33419
34641 168842.0322
6361 64344.75658
30071 158614.098
35767 158517.9956
2663 68538.77514
16674 162126.2725
8122 101162.0077
6271 88152.29226
5296 76794.67211
7386 72072.28872
30598 146233.1615
20473 157081.5458
25589 181543.453
40847 175111.0167
21352 141240.3264
7618 86818.29695
10604 81242.91776
12578 101148.659
5129 106012.2302
7770 82389.90319
14389 102250.9998
9285 92462.15281
12463 79399.70844
6708 102609.6069
10031 76059.88065
18013 99842.8173
19975 107188.4283
2790 83200.05685
13036 88206.72723
15681.88295
3570.771019
16977.90234
15246.6198
2707.588959
9215.045447
15341.06765
5784.970498
3384.752189
7515.305373
2547.049513
2915.27027
7202.424868
9067.666488
5955.233787
2740.123983
4640.005106
3121.360675
9982.885927
6361.096327
9411.896451
16205.07929
2662.614914
5043.680241
5127.803522
6270.801817
5296.313742
7385.863529
15986.8914
6585.141862
6963.104609
16359.09476
7658.404543
7119.345018
15604.05835
8402.184265
2835.351142
7770.194983
9529.102784
7730.956919
17463.46261
4507.793747
15031.1632
17220.46414
16805.36824
2789.931873
17026.05347
98429.66408
75978.24594
148259.7994
156723.9755
172895.8709
119833.6145
121990.0863
170338.0039
193438.4222
101427.1537
97565.08589
104515.3916
142527.6707
178424.2703
171531.4768
111493.3353
89123.6426
76891.69487
178824.9181
70705.8529
168025.9945
174723.0749
71201.39006
167169.9528
106289.8112
94423.09408
82090.98586
79458.15225
162220.0529
163666.6877
188506.5576
191470.1114
148898.731
93937.64197
96846.97611
109550.8432
108847.5814
90160.09817
111780.1026
100193.1097
96863.17106
107117.4006
91091.04385
117063.2814
123993.7965
85989.98873
105232.7807
11133 102565.6555
7143 113454.6583
18142 124421.604
19536 113789.9926
7682 93687.59566
6609 71283.88653
4913 65992.28448
15042 175294.7828
15836 99976.68222
16843 132158.2092
15252 109901.3357
2506 81600.57886
7467 87086.27746
15321 178049.443
13129 96146.39082
30216 169531.8706
7148 112016.0607
24896 182448.1466
6440 70037.29459
15705 109178.6126
7598 100872.923
36227 155818.234
16373 129236.4565
30600 146743.0569
7974 133318.7353
13546 113298.196
33108 149499.7492
6261.600587
3759.6588
8246.27972
15424.56857
6554.451048
6608.718387
4912.953857
4319.862248
15729.01557
6764.430471
7588.220921
2505.871115
7467.107413
4396.225084
8841.000233
8921.028084
3816.344021
6925.276975
6439.972546
8813.127574
4549.792889
16701.17188
6596.876662
15924.55858
3094.309951
7099.829129
16468.07859
108827.256
117214.3171
132667.8838
129214.5611
100242.0467
77892.60491
70905.23834
179614.6451
115705.6978
138922.6396
117489.5566
84106.44998
94553.38487
182445.6681
104987.3911
178452.8987
115832.4047
189373.4236
76477.26714
117991.7402
105422.7159
172519.4059
135833.3332
162667.6154
136413.0452
120398.0251
165967.8278
B/E 309 – Practice Quiz 2
Use the following information to answer questions 1 – 2: The running time of Rated G movies is
known to be distributed normally with mean 85 minutes and variance 25 squared minutes.
1. What is the probability that a randomly selected G rated movie will be between 85 and 90
minutes?
2. Suppose you randomly sample 81 G rated movies. What is the probability that the average
running time of these 81 movies will be less than 84 minutes?
Use the following information to answer questions 3– 5: Ikea furniture takes a while to assemble and
can sometimes be frustrating. Because assembling furniture can strain marriages, professional furniture
assemblers have introduced themselves to this market. Suppose that a Blork cabinet was assembled by 60
randomly selected pros and 50 randomly selected amateurs and the following stats were collectd.
Statistic
Number in group sampled
Mean time spent assembling
the Blork cabinet
Sample standard deviation of
time spent assembling
Pros (P)
ܰ = 60
തതത
ܻ = 22 ݉݅݊ݏ݁ݐݑ
Amateurs (A)
ܰ = 50
ഥ
ܻ = 62 ݉݅݊ݏ݁ݐݑ
ݏ, = 4 ݉݅݊ݏ݁ݐݑ
ݏ, = 20 ݉݅݊ݏ݁ݐݑ
3. Perform a test of the null-hypothesis that the actual average time for that amateurs spend on
assembly is one hour (60 minutes). Report t-stat, p-value, decision to reject/fail-to-reject, etc.
4. Construct a 95% confidence interval for the average amount of time spent by each group
assembling the cabinet. How would things change if we instead wanted a 99% confidence
interval?
5. Test the null-hypothesis that amateurs and pros spend the same amount of time assembling this
cabinet. What is the corresponding t-stat? What’s up with your p-value resulting from this test?
What does that mean?
Use the “Payroll Data (cross-section).xlsx” file to answer questions 6 – 8. Assume that this data
represents a random sampling of employees from each of the three locations (Camarillo, Santa
Barbara, and Ventura).
6. Conduct a t-test for the null-hypothesis that the average bonus received by Camarillo employees
is the same as the average bonus received by non-Camarillo employees.
7. What is the numeric value of the standard error of the difference in means for the test you are
conducting in the above question? (Hint: you’ll have to calculate this on your own – Excel’s ttest analysis doesn’t report this)
8. Perform a test of the null-hypothesis that the average employee at this firm (regardless of
location) is 40 years old.
Bus/Econ 309 – Graphing Portfolio Assignment – 240 points
Summary
For this assignment you will create four (4) high quality graphs/charts, one (1) high quality table, and
one (1) high quality regression table to demonstrate…
1. Your ability to work with data
2. Your ability to empathize with your audience by clearly conveying information about a topic they
might know nothing about
3. Your ability to organize data succinctly and clearly
4. Your ability to make smart graphical choices in terms of structure, organization, and planning
Your data
Your data can come from any source you like (typically online). If you cannot find any online data that
you would like to use, I have provided some data you can use on the course Canvas page under “Graphing
Assignment Files.” Each of those data files is in .xlsx format and also has an accompanying “About” file
to help you better understand the data.
Assignment details
Table: Your table must clearly convey whatever data it is presenting via clear labeling and styling.
Regression: Your regression table must be properly formatted as shown in the course Canvas videos.
Graphs: Your graphs must each must be a different type. Example – for your 4 graphs you might choose:
1.
2.
3.
4.
A pair of histograms
A collection of box and whisker plots
A scatterplot
A combo chart containing two bar-graphs and a line graph on a separate axis
But there are many others from which to choose, and you are encouraged to not just follow the above list.
RULE 1: I do not consider horizontal bar charts to be different from column charts, as one is just the other
turned on its side. Also, 3-dimensional charts are not different from 2-dimensional versions of the same
chart. A scatterplot with markers is not different from a scatterplot without markers, etc..
RULE 2: All your graphs must contain different data, with the exception being dates/time. For example,
the “two” graphs in Figure 1 would violate this rule as they’re just two versions of the same data.
Figure 1 – Breaking Rule 2 – These are NOT two different graphs
Also, these graphs would receive very low grades as they’re poorly labeled
RULE 3: When making your graphs, imagine that you just graduated and have been asked by your boss to
prepare them as one of your first work assignments. Your reputation with your boss, as well as the
reputation of your school and degree, is going to be formed based upon the quality of the work you do.1
RULE 4: You must submit your graphs in the template provided on the last pages of this handout. Save
your completed assignment as a .pdf with file name “Last.First.pdf” where “Last” is your last name and
“First” is your first name as they appear on Canvas.
Below are some examples of bad graphs, and explanations as to why they’re bad.
Figure 2 – A terrible graph
The graph in Figure 2 is terrible because…
•
•
•
•
There are no axis labels.
It’s unclear what units anything is being measured in. Are sales measured in dollars? In Euros? In
number of units sold?
I couldn’t even guess what the horizontal axis could possibly be measuring…time? Locations?
It’s visually a mess. The markers are all crammed together and make the line harder to see, and
the line itself feels really cramped. Also, the horizontal axis extends needlessly far to the right
after the data as stopped. The vertical axis units would benefit from some commas. Etc.
Figure 3 – Another terrible graph
1
Part of the reason I’m going to grade these so harshly is because it is much better to make mistakes in a safe
learning environment like a classroom as opposed to in the workplace where mistakes may be more costly.
The graph in Figure 3 is terrible because…
•
•
•
It’s not clear what the edges of the box and whiskers represent. Remember, different people apply
different rules to box-and-whisker graphs, so if you use one, you’ll need to include some notes
under your graph to let your reader know what everything represents. For example, what does the
top of the box represent? The top of the whisker? The dots outside the whiskers?
This is a huge waste of space: box-and-whisker graphs are nice choices for visualizing
distributions succinctly. Take advantage of this by putting at least two on the chart next to eachother so you can tell a story about how the data relates to each other.
Nothing is clearly labeled, including profit: what currency is it measured in? What firm’s profit is
this? Is it just one segment of the firm? Over what time period?
Below is an example of a good graph.
Figure 4 – A good graph
* US Dollar figures are nominal (not adjusted for inflation).
This is a good graph because…
•
•
•
Every axis is clearly labeled, including the units (revenues and costs are clearly measured in
dollars, sales are clearly measured in units.
It’s obvious what each bar measures, and in what time period. Same for the height of the line.
The title makes it clear what is being reported here, so if you found this graph on the ground
outside a 7-Eleven, you’d know exactly what you were looking at (though you’d be faced with
the mystery of how it got there). The note below the table adds even further clarity.
Grading rubric
Each graph/table is worth 40 points. The more closely your graphs resemble those in Figures 2 and 3, the
fewer points you’ll earn (the graphs in Figures 2 and 3 would earn 0 points each). The more closely your
graphs resemble the guidelines demonstrated in Figure 4 and/or those in the “Cool Graphs from the New
York Times” handout, the more points you’ll earn (the graph in Figure 4 would earn 40 points).
Please enter all of your graphs and tables into the template provided on the following pages. After you
have done so, save just those pages as a .pdf titled “Last.First.pdf” (per Rule 4, above). If any of your
graphs/tables require notes, please enter them below the graph in the space provided (and just make your
graph a little smaller so it all fits inside the designated pages).
Name: [TYPE YOUR NAME HERE]
B/E 309 – GRAPHING PORTFOLIO
Graph 1 –
Graph 2 –
Name: [TYPE YOUR NAME HERE]
B/E 309 – GRAPHING PORTFOLIO
Graph 3 –
Graph 4 –
Name: [TYPE YOUR NAME HERE]
B/E 309 – GRAPHING PORTFOLIO
Table –
Regression Table –
