In the dataset "RestRating", the 30 randomly selected patrons give the overall dining experience ratings (Outstanding, Very good
, Good, Average, or Poor) at a restaurant on a Saturday evening
2 answers:
Answer:
The dataset image is attached.
The question has three parts:
a) Find the frequency distribution and relative frequency distribution for these data.
b) Construct a percentage bar chart for these data
c) Construct a percentage pie chart for these data
Explanation:
a)
Frequency distribution tell us how often something occurs.
Frequency distribution
Experience Rating Frequency
OutStanding 14
Very Good 10
Good 5
Average 1
Poor 0
This relative frequency distribution table shows how something are distributed.
Relative Frequency Distribution
Experience Rating Frequency Relative Frequency
OutStanding 14 14/30 = 7/15 = 0.467
Very Good 10 10/30 = 1/3 = 0.333
Good 5 5/30 = 1/6 = 0.167
Average 1 1/30 = 0.033
Poor 0 0
Total 30 1.000
b) Chart image is attached
c) Chart image is attached
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Answer:
See attachment for chart
Explanation:
The IPO chart implements he following algorithm
The expressions in bracket are typical examples
<u>Input</u>
Input Number (5, 4.2 or -1.2) --- This will be passed to the Processing module
<u>Processing</u>
Assign variable to the input number (x)
Calculate the square (x = 5 * 5)
Display the result (25) ----> This will be passed to the output module
<u>Output</u>
Display 25
Answer:
the only one that meets the requirements is option C .
Explanation:
The tolerance of a quantity is the maximum limit of variation allowed for that quantity.
To find it we must have the value of the magnitude, its closest value is the average value, this value can be given or if it is not known it is calculated with the formula
x_average = ∑ / n
The tolerance or error is the current value over the mean value per 100
Δx₁ = x₁ / x_average
tolerance = | 100 -Δx₁ 100 |
bars indicate absolute value
let's look for these values for each case
a)
x_average = (2.1700000+ 2.258571429) / 2
x_average = 2.2142857145
fluctuation for x₁
Δx₁ = 2.17000 / 2.2142857145
Tolerance = 100 - 97.999999991
Tolerance = 2.000000001%
fluctuation x₂
Δx₂ = 2.258571429 / 2.2142857145
Δx2 = 1.02
tolerance = 100 - 102.000000009
tolerance 2.000000001%
b)
x_average = (2.2 + 2.29) / 2
x_average = 2,245
fluctuation x₁
Δx₁ = 2.2 / 2.245
Δx₁ = 0.9799554
tolerance = 100 - 97,999
Tolerance = 2.00446%
fluctuation x₂
Δx₂ = 2.29 / 2.245
Δx₂ = 1.0200445
Tolerance = 2.00445%
c)
x_average = (2.211445 +2.3) / 2
x_average = 2.2557225
Δx₁ = 2.211445 / 2.2557225 = 0.9803710
tolerance = 100 - 98.0371
tolerance = 1.96%
Δx₂ = 2.3 / 2.2557225 = 1.024624
tolerance = 100 -101.962896
tolerance = 1.96%
d)
x_average = (2.20144927 + 2.29130435) / 2
x_average = 2.24637681
Δx₁ = 2.20144927 / 2.24637681 = 0.98000043
tolerance = 100 - 98.000043
tolerance = 2.000002%
Δx₂ = 2.29130435 / 2.24637681 = 1.0200000017
tolerance = 2.0000002%
e)
x_average = (2 +2,3) / 2
x_average = 2.15
Δx₁ = 2 / 2.15 = 0.93023
tolerance = 100 -93.023
tolerance = 6.98%
Δx₂ = 2.3 / 2.15 = 1.0698
tolerance = 6.97%
Let's analyze these results, the result E is clearly not in the requested tolerance range, the other values may be within the desired tolerance range depending on the required precision, for the high precision of this exercise the only one that meets the requirements is option C .