Data Analysis and Forecasting: Humidity Data and Linear Model

This report presents a detailed analysis of humidity data collected over ten consecutive days. It begins with a table of the raw data and then introduces two types of charts: bar charts and pie charts, for visual representation. The report proceeds to calculate and interpret key statistical measures, including the mean, median, mode, range, and standard deviation. The methodology for calculating each of these values is clearly outlined, with step-by-step instructions. The core of the report involves the application of a linear forecasting model to predict humidity levels on the 15th and 21st days. The report provides the formulas and calculations for determining the 'm' and 'c' values within the linear model, and then uses these values to forecast the expected humidity percentages for the specified future dates. The conclusion summarizes the key findings and highlights the importance of data analysis and forecasting in understanding and predicting environmental conditions. The report concludes with a list of relevant references.

Data Analysis and
Techniques

Table of Contents
INTRODUCTION...........................................................................................................................1
1. Table of humidity data of 10 days...........................................................................................1
2. Two types of Chart..................................................................................................................1
3. Calculation of values of mean, mode, median and standard deviation...................................3
4 Using of Linear forecasting model...........................................................................................5
a) Steps for calculating “m” value..............................................................................................5
b) Steps for calculating “c” value................................................................................................6
c) Forecasting humidity by calculating “m” and “c” values for 15th and 20th day....................6
CONCLUSION................................................................................................................................6
REFERENCES................................................................................................................................8

INTRODUCTION
Numeracy as well as data analysis plays an important role as it shows knowledge and
ability of analysing numerical data in an efficient manner (Chambers, 2017). This present report
is going to analysis of humidity data of 10 consecutive days which is collected via online
sources. It will also show the importance of chart and mean, mode and median by which whole
data is analysed. It helps in analysing and forecasting humidity value on the 15th and 21nd days.
1. Table of humidity data of 10 days
Humidity data of …...10 consecutive days is being shown below:
Days (Jan 7th 2020 to 16th Jan 2020) Humidity (%)
7 69%
8 77%
9 81%
10 76%
11 90%
12 88%
13 69%
14 65%
15 69%
16 67%
2. Two types of Chart
Graphs 1: Bar chart
This is a bar chart of type which represents categorical data with rectangular bars having
lengths or heights proportional to values which they represent.
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Graph 2: Pie Chart
It is pie chart of type which is a circle form that divided into slices in order to show or
illustrate numerical values (Bertini, Elmqvist and Wischgoll, 2016).
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3. Calculation of values of mean, mode, median and standard deviation
Mean: Means refers average of some data for calculating this average, all data have to be
added and then divide by the total number of numbers (Klatt, Schröder‐Turk and Mecke, 2017).
Here are detailed steps by which mean or average is being calculated of humidity data:
Step 1: Writing up all values or data in excel and just below writing the formula of mean which
is: Average (values)
Step 2: After putting up formula, all values are selected for which average or means is to be
calculated. For example: humidity data.
Step 3: After selecting values, need to press enter key only. Mean of this data or humidity % is
75.1
So, it can be said that calculating mean or average is so simple as it can be completed by
following 3 easy steps only.
Median: Median is the middle value in the list of data or numbers. For calculating
median there is a requirement to have all data listed in numerical order from smallest to largest
or rewrite data so that, median can be found out in an easy manner (Burke and et.al., 2018). Here
are some steps by which middle number or median of humidity data is being calculated:
Step 1: Writing formula of median below the values such as: Median (values)
Step 2: Selecting whole value for which we have to calculate median.
Step 3: Press enter key after the 2nd step or selecting values.
So, it can again be said that calculating median is also easy as it has 3 easy steps. For this data
value of median is 72.5.
Mode: Mode is the value or number which occurs most often or repeats. In the case,
where no number is repeated then there is no mode for the list. It can easily be find out by seeing
repetitive number but here is detailed step such as:
Step 1: Writing all values or data in excel.
Step 2: Writing formula of mode such as: mode (values)
Step 3: Selecting columns for which mode is needed to be calculated.
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Step 4: After all steps, press enter key for getting mode value. Here is the repetitive number is 69
and it is the value of mode.
Range: Range of a list of data or numbers is a difference between the largest and smallest
value.
Step 1: Writing down all data in excel
Step 2: Putting up formula of range such as: Max (value)- Min (value)
Step 3: Selecting column as like mode for which range has to be calculated
Step 4: Press enter key. Here is the value of range is 25.
Standard deviation: It refers a quantity expressing by the number of data which differ
from the mean value for the total data (Vazac and et.al., 2016). Here are some steps of
calculating standard deviation for humidity data or % of 10 consecutive days.
Step 1: Inserting or writing down all values in excel.
Step 2: Putting up formula of standard deviation below the values such as: Stdev (value)
Step 3: Press enter key and get result. Here the standard deviation of humidity % is 8.408.
So, from above discussed steps of calculation of mean, mode and median it can be said
that formula plays an important role and all have easy steps.
Days (Jan 7th 2020 to 16th Jan 2020) Humidity (%)
7 69
8 77
9 81
10 76
11 90
12 88
13 69
14 65
15 69
16 67
mean 75.1
Median 72.5
Mode 69
Range 25
4

Standard deviation 8.408
4 Using of Linear forecasting model
Days (Jan 7th 2020 to 16th Jan
2020) Days (X)
Humidity
(%) XY X^2
7 1 69 69 1
8 2 77 154 4
9 3 81 243 9
10 4 76 304 16
11 5 90 450 25
12 6 88 528 36
13 7 69 483 49
14 8 65 520 64
15 9 69 621 81
16 10 67 670 100
Total 55 751 4042 385
a) Steps for calculating “m” value
Particulars Details
m NΣxy– Σx Σy / NΣ x^2– (Σx)^2
(10 * 4042)– (55 * 751) / (10 * 385)– (55)^2
(40420-41305) / (3850-3025)
1.072727
Here N is number of days such as Σx days.
Σxy is value which is being calculated by multiplication of X and Y values. Here is the value of
Σxy is 4042
Σx is total number of days such as Σx which is the total of (1,2,3,4,5,6,7,8,9,10) which is 55
Σy is total number of humidity data which is 751
x^2 is total number of square of x in which X is days which is 385
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So, by putting up formula, value of m is being identified which is 1.02727
b) Steps for calculating “c” value
Particulars Details
c Σy– m Σx / N
[751– (1.027 * 55)]/10
69.4
c) Forecasting humidity by calculating “m” and “c” values for 15th and 20th day
Forecast of 15th day
Y = mX + C
Y 1.02 (X) + 69.4
X 15
Y 1.02 (15) + 69.4
84.7
Here is the forecasting value of 15 day f humidity is 84.7
Forecast of 21nd day
Y = mX + C
Y 1.02 (X) + 69.4
X 21
Y 1.02 (21) + 69.4
90.82
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Here is the forecasting humidity value of on 21nd day is 90.82
CONCLUSION
From the above study it can be summarized that data analysis played a vital role as it
shows the knowledge of numeracy related elements. This report has shown a detailed statistics of
humidity data of 10 consecutive days. In that data all factors like mean, mode, median identified.
Linear forecasting model helped out in identifying humidity data on 15th and 21nd day.
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REFERENCES
Books and journals
Bertini, E., Elmqvist, N. and Wischgoll, T., 2016. Judgment error in pie chart variations. In
Proceedings of the Eurographics/IEEE VGTC conference on visualization: Short papers
(pp. 91-95).
Burke, C.E. and et.al., 2018. Statistics time chart interface cell mode drill down. U.S. Patent
10,139,997.
Chambers, J.M., 2017. Graphical methods for data analysis: 0. Chapman and Hall/CRC.
Klatt, M.A., Schröder‐Turk, G.E. and Mecke, K., 2017. Mean‐intercept anisotropy analysis of
porous media. II. Conceptual shortcomings of the MIL tensor definition and Minkowski
tensors as an alternative. Medical physics. 44(7). pp.3663-3675.
Vazac, C.A. and et.al., 2016. Load test charts with standard deviation and percentile statistics.
U.S. Patent 9,251,035.
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