Question

The cost of electricity (in $) was recorded for a random sample of 50 cities in a country. These data are as follows:

9.0 |
9.0 |
9.5 |
9.6 |
10.2 |
10.8 |
10.9 |
11.1 |
11.4 |
11.6 |

11.9 |
12.3 |
12.7 |
12.8 |
12.9 |
13.0 |
13.0 |
13.5 |
13.7 |
13.9 |

14.1 |
14.3 |
14.4 |
14.7 |
14.8 |
14.8 |
14.9 |
14.9 |
15.0 |
15.1 |

15.3 |
15.4 |
15.7 |
16.3 |
16.5 |
16.6 |
16.7 |
16.8 |
17.1 |
17.2 |

17.5 |
17.8 |
18.3 |
18.5 |
18.7 |
19.1 |
19.7 |
20.2 |
20.6 |
21.3 |

A “less than” frequency distribution is constructed by using equal-sized classes. The first class is “9.0 but less than 10.8”. What is the last class in this “less than” frequency distribution?

Select one:

a. 21.0 but less than 22.0

b. 20.4 but less than 22.2

c. 19.8 but less than 21.6

d. 19.5 but less than 21.3

Answer #1

I understand that these will be
considered seperate questions, but could I get a full answer
please.
Thank you.
1 A
civil engineering agency monitors water quality by measuring the
amount of suspended solids in a sample of river water. The
following results, measured in parts per million, were observed
over 60 days:
21.2
19.9
20.2
15.6
17.6
14.0
19.7
18.3
26.2
23.6
20.2
19.8
22.2
27.3
23.4
19.8
22.3
16.9
16.4
22.0
19.8
21.1
20.4
18.6
21.8
22.5
20.0...

The built-in R dataset swiss gives Standardized fertility
measure and socio-economic indicators for each of 47
French-speaking provinces of Switzerland at about 1888. The dataset
is a data frame containing 6 columns (variables). The column
Infant.Mortality represents the average number of live births who
live less than 1 year over a 3-year period. We are interested in
the Infant.Mortality column. We can convert the data in this colun
to an ordinary vector x by making the assignment x <-
swiss$Infant.Mortality....

Census data was collected on the 50 states and Washington, D.C.
We are interested in determining whether average lifespan (LIFE) is
related to the ratio of males to females in percent (MALE), birth
rate per 1,000 people (BIRTH), divorce rate per 1,000 people
(DIVO), number of hospital beds per 100,000 people (BEDS),
percentage of population 25 years or older having completed 16
years of school (EDUC) and per capita income (INCO).
"STATE" "MALE" "BIRTH" "DIVO" "BEDS" "EDUC" "INCO" "LIFE"
AK...

Census data was collected on the 50 states and Washington, D.C.
We are interested in determining whether average lifespan (LIFE) is
related to the ratio of males to females in percent (MALE), birth
rate per 1,000 people (BIRTH), divorce rate per 1,000 people
(DIVO), number of hospital beds per 100,000 people (BEDS),
percentage of population 25 years or older having completed 16
years of school (EDUC) and per capita income (INCO), with LIFE (y)
as the response variable, and MALE...

In R, if I have a plot of LIFE against BIRTH and I want to
specifically mark the data point corresponding to STATE "AK" in the
graph as red colour(the rest of the points as black color), what R
command should we use?(Please don't look through the data to find
AK's birth value(24.8)or life value (69.31)and mark this point
manually referring to this value as other state can have the same
birth and life value)
"STATE" "MALE" "BIRTH" "DIVO" "BEDS"...

The Student Government Association is interested in an estimate
of the mean number of hours students work per week. They take a
simple random sample of 89 students. The data they collected can be
found in the column labeled “Number of hours worked per week”. Use
the sample data to construct a 95% confidence interval for the mean
number of hours worked. Write one or two sentences to communicate
to the Student Government Association the estimate and the margin
of...

The built-in R dataset swiss gives Standardized fertility
measure and socio-economic indicators for each of 47
French-speaking provinces of Switzerland at about 1888. The dataset
is a data frame containing 6 columns (variables). The column
Infant.Mortality represents the average number of live births who
live less than 1 year over a 3-year period. We are interested in
the Infant.Mortality column. We can convert the data in this colun
to an ordinary vector x by making the assignment x <-
swiss$Infant.Mortality....

BODYFAT
HEIGHT
12.9
67.75
6
72.25
25.5
66.25
10.6
72.25
28.1
71.25
20
74.75
19.2
69.75
11.9
72.5
4.3
74
12.9
73.5
7.6
74.5
8.3
76
20.4
69.5
19.8
71.25
22.2
69.5
20.9
66
28
71
23
71
15.6
67.75
16.9
73.5
18.5
68
15.5
69.75
14.7
68.25
18
70
13.6
67.75
4.3
71.5
8
67.5
22.4
67.5
4.4
64.75
8.9
69
13.2
73.75
6.3
71.25
12.6
71.25
21
71
31.9
73.5
39.1
65
23.2
70
26.6
68.25
34.3
72.25...

Part C: Regression and Correlation Analysis
Use the dependent variable (labeled Y) and the independent
variables (labeled X1, X2, and X3) in the data file. Use Excel to
perform the regression and correlation analysis to answer the
following.
Generate a scatterplot for the specified dependent variable (Y)
and the X1 independent variable, including the graph of the "best
fit" line. Interpret.
Determine the equation of the "best fit" line, which describes
the relationship between the dependent variable and the selected...

Use the dependent variable (labeled Y) and the independent
variables (labeled X1, X2, and X3) in the data file. Use Excel to
perform the regression and correlation analysis to answer the
following.
Generate a scatterplot for the specified dependent variable (Y)
and the X1 independent variable, including the graph of the "best
fit" line. Interpret.
Determine the equation of the "best fit" line, which describes
the relationship between the dependent variable and the selected
independent variable.
Determine the coefficient of...

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