## Transcribed Text

2.2.2
The median incomes fernales each state of the United States, including the Distric of
Columbia and Puerto Rico, are givenir table #2.2 10(' "Mediar income of,' 2013). Create
frequency distribution, relative frequency distribution, and cumulative frequency distribution
using classes.
Table #2.2.10: Data Mediar Income for Females
$31,862 $40,550 $36,048 $30,752 $41,817 $40,236 $47,476 $40,500
$60,332 $33,823 $35,438 $37,242 $31,238 $39,150 $34,023 $33,745
$33,269
$29,548
$33,865
$31,067
$33,424
$35,484
$41,021
$47,155
$32,316
$42,113
$33,4
$32,462
$35,746
$31,274
$36,027
$37,089
$22,117
$41,412
$31,330 531,32
$33,184
$35,301
$32,843
$38,177
$40,969
$40,993 $29,688 $35,890 $34,381
2.2.6
Create histogram and relative frequency histogram for the data in table $2.2.10 Describe the shape
and any findings you can from the graph.
2.2.10
Create ogive for the data table #2. .10. Describe any findings you car from the graph.
2.3.4
Table 7 contains the value of the house and amountof rental income inayearthat the house
brings "Capital and rental," 2013). Create scatter plot and state fthereis relationship between
the value the house and the annual rental income.
Table#2. 3.7: Data House Value versus Rental
Value
Rental
Value
Rental
Value
Rental
Value
Rental
81000
77000
4576
75000
7280
67500
6864
95000
7904
94000
8736
90000
6240
85000
7072
121000
12064
115000
7904
110000
7072
104000
7904
130000
9776
126000
125000
140000
9568
140000
135000
7488
13312
165000
8528
155000
148000
8320
178000
11856
174000
10400
170000
9568
170000
2688
200000
12272
200000
10608
194000
11232
190000
8320
214000
8528
208000
10400
200000
10400
200000
8320
240000
10192
240000
12064
240000
11648
225000
12480
289000
11648
270000
12896
262000
244500
325000
12480
12272
300000
2.3.8
The economic of 2008 affected though some more than others. Some people in
Australia wasn't badly fromthe crisis. The bank assets billions
of Australia dollars (AUD))of the Reserve Bank Australia (RBA) the time periodo March 2007
through March 2013 containedin l'B1 assets of," 2013). Create time-series plot and
interpret findings
Table#2.3.11:
Data
Date
versus
Assets in
billions ot
Date
AUD
Mar-2006
-2006
-2007
134.0
123.0
2008
105.6
101.5
2008
158.8
2009
118.7
87.0
86.
83.4
85.7
74.8
83.9
95.8
Mar-2013
90.5
3.1.2
The lengths (in kilometers) of rivers on the South Island of New Zealand that flow to the Pacific
Ocean are listed table #3.1. (Lee, 1994). Find the mean median, and mode.
Table #3 .1.8 Lengths of Rivers (km) Flowing to Pacific Ocean
River
Length
River
Length
(km)
(km)
Clarence
209
Clutha
Conway
Taieri
288
Waiau
169
Shag
Hurunui
Kakanui
64
Waipara
Rangitata
121
Ashley
97
Ophi
80
Waimakariri
161
Pareora
56
Selwyn
95
Waihao
64
Rakaia
145
Waitaki
209
Ashburton
90
3.1.8
State which type of measurement scale each represents and then which center measures can
be use for the variable?
a.)
You collect data on the height of plants using new fertilizer.
b.) You collect data on the cars that people drivei Campbelltown Australia
c.) You collect data on the temperature at differen locations Antarctica
d.) You collect data on the first. second, and third winner beer competition
3.1.12
An employee Coconino Community College (CCC) evaluated based ongoal setting and
accomolishments toward goals joh effectiveness competencies. CCC core values. Suppose for specific
employee, goal has weight 20% goal 2has weight goal has weight 10%job
effectiveness weight 25% competency 1 has goal 4%, competency 2has agoal has weight
of 3%, competency has weight competency 4 has : weight of 5% values has weight
of 10% Suppose employee scores F2.0for goal 1 2.0 for goal 2, 4.0 for goal 3, 3.0 for job
effectiveness, 2.0 for competency 3.0 for competency 2,20for competency 3, 3.0 for competeno 4,
and 4.0 for core values. Find the weighted average score for this employee. If an employee that has a
score less than 1.5. they must have Performance Enhancement Plan written. Does this employee need
aplan?
3.2.2
The lengths (in kilometers) of ivers on the South Island of New Zealand that flow the Pacific
Ocean are listedintable #3.2.9 (Lee. 1994).
Table #3.2.9: Lengths Rivers (km) Flowing Pacific
Ocean
River
Length
River
Length
(km)
(km)
Clarence
209
Clutha
Conway
Taieri
288
Waiau
169
Shag
Hurunui
138
Kakanu
64
Waipara
64
Waitaki
209
Ashley
Waihao
64
Wairnakariri
161
Pareora
Selwyn
95
Rangitata
Rakaia
145
Ophi
80
Ashburton
90
a.) Find mean and median
b.) Find range.
c.) Find the variance and standard deviation
3.2.6
Print- Matic printing company spends specific amounts on fixed costs every month. The costs
of those fixed costs are intable #3.2.13.
Table #3.: .13: Fixed Costs for Print Matic Printing Company
Monthly charges
Monthly
cost($)
Bank charges
Cleaning
2208
Computer expensive
Lease payments
Postage
Uniforms
a.) Find mean and median.
Find the range
c.)
Find
variance
and
deviation

These solutions may offer step-by-step problem-solving explanations or good writing examples that include modern styles of formatting and construction
of bibliographies out of text citations and references. Students may use these solutions for personal skill-building and practice.
Unethical use is strictly forbidden.

## 2.2.2

```{r}

class1 <- c(31862, 40550, 36048, 30752, 41817, 40236, 47476, 40500)

class2 <- c(60332, 33823, 35438, 37242, 31238, 39150, 34023, 33745)

class3 <- c(33269, 32684, 31844, 34599, 48748, 46185, 36931, 40416)

class4 <- c(29548, 33865, 31067, 33424, 35484, 41021, 47155, 32316)

class5 <- c(42113, 33459, 32462, 35746, 31274, 36027, 37089, 22117)

class6 <- c(41412, 31330, 31329, 33184, 35301, 32843, 38177, 40969)

class7 <- c(40993, 29688, 35890, 34381)

all_classes <- c(class1, class2, class3, class4, class5, class6, class7)

# Frequency Distribution

classes_freq_dist <- table(all_classes)

classes_freq_dist

# Cumulative Frequency Distribution

classes_cumm_dist <- cumsum(classes_freq_dist)

classes_cumm_dist

# Relative Frequency Distribution

classes_rel_dist <- classes_freq_dist/length(unique(all_classes))

classes_rel_dist

```

## 2.2.6

```{r}

hist(all_classes)

```

Histogram shows data is right skewed. It shows data is mostly distributed between in the range of 30000 and 40000. There is an outlier beyond 60000.

```{r}

hist(classes_rel_dist)

```

Since, each data is unique so relative distrubution is constant ie. 0.01923077....