Numeracy and Data Analysis: Calculation, Presentation and Forecasting of Humidity in London
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This report outlines the data analysis of humidity in London, including computation of different statistical formulas, presentation of data in tabular and chart form, and calculation of linear regression for forecasting humidity of the next two days. The report concludes with a summary of the findings and references used.
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NUMERACY AND DATA ANALYSIS
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Table of Contents INTRODUCTION...........................................................................................................................3 MAIN BODY..................................................................................................................................3 1. Arranging the data in Table format-........................................................................................3 2. Presentation of the data in different types of chart-.................................................................3 3. Calculation and discussion of the following-..........................................................................4 4. Linear regression.....................................................................................................................6 CONCLUSION................................................................................................................................8 REFERENCES................................................................................................................................9
INTRODUCTION Data analysis is a method used for inspecting, transforming, cleaning and modelling of data which aims to convert raw information or data into useful information which can be used for making decisions. The report will outline the data analysis of humidity in London. It will highlight computation of different statistical formulas along with this there will be discussion regarding the result of each formula. The statistical formulas that are being computed are: mean, mode, median, range and standard deviation. Data collected regarding humidity in London will be also presented in tabular as well as in different charts. Further, report will also include calculation of linear regression which is used for forecasting. In this report linear regression formula will be used to forecast humidity of next two days i.e. 11thand 12thin London. MAIN BODY 1. Arranging the data in Table format- Last ten days humidity in London DateHumidity 9-Sep78 10-Sep72 11-Sep62 12-Sep56 13-Sep77 14-Sep60 15-Sep58 16-Sep45 17-Sep42 18-Sep78 The above table represents humidity of London of ten consecutive days from 9th September, 2022 to 18thSeptember, 2022. 2. Presentation of the data in different types of chart- Bar chart=
Line chart= 3. Calculation and discussion of the following- 1) Mean=Mean is the value which characterizes average value of certain data set. The steps are taken as below= Formula= Mean= Sum of the taken humidity/ Number of the days.(Saidi and Siew, 2019) = 628/10 = 62.8
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The value 62.8 exhibits that the average of this humidity series is 62.8 so average humidity has been 62.8% in London. 2) Median=This gives the value of centre which rifts a series in two parts, one is bigger and second one is smaller than the extended value (Kaliyadan and Kulkarni, 2019). Formula= Median= (n+1)/2 Arranging the data in increasing order= 42, 45, 56, 58, 60, 62, 72, 77, 78, 78 = 10+1/2 = 5.5thvalue = 60+62/2 =61 The value of median explains that the medium humidity in London has been 61. So in other days the humidity was recorded half tenure lower than 61 and half tenure bigger. 3) Mode=It is the value which shows the most frequent value out of the taken data series (George and Mallery, 2018). In this case Observation method has been applied as- 42, 45, 56, 58, 60, 62, 72, 77, 78, 78 As from observation it can be twigged that the value 78 is the most frequent one. So the mode in this case is 78. It articulates that the maximum humidity has been notched up at 78%. 4) Range=this value shows the difference between the biggest and smallest number or figure. So the formula is= Formula= Range= Biggest value- Smallest value Range= 78-42 = 36 The difference between humidity of the undertaken 10 days was 36. So the humidity was ranging with this margin. On the basis of the calculation it can be summarized that the fulgurations were quite significant or intensive in last ten days. That’s the reason of such a comprehensive figure of range(Bagus, and Hanaoka, 2022) 5) Standard Deviation=It shows the value which deciphers the spread out of certain data set (Weir and et.al., 2018). If the data is clustered around the mean, then it would be lower and in case of hyper data spread out then the value would be bigger. Formula=
So by applying the formula outcomes are as= Humidity figuresDeviation form MeanSquare of the deviations 7815.2231.4 729.284.64 62-0.80.64 56-6.846.24 7714.2201.64 60-2.87.84 58-4.823.04 45-17.8316.84 42-20.8432.64 7815.2231.04 628 (mean= 628/10=62.8)01575.6 So as per the formula (1575.6/10) = 157.56 =157.56^ (1/2) = 12.55 Standard deviation= 12.55 It shows large spread of the values form the mean. Keeping it as testimony it would be fair enough to articulate that the humidity was recorded haphazardly. On the basis of the evaluation it can be said that the spread was highly disseminated. 4. Linear regression Liner regression is a tool in statistics that is used to predict value of one variable depending on value of another variable. The variable that is being predicted is known as dependent (response)
variable and variable on which dependent variable is predicted is called independent (predictor) variable (Hope, 2020). It is one of the best of forecasting and is relatively simple mathematical formula. It provides a relationship between dependent and independent variables. Formula y= mx+ c y is dependent variable m is estimated slope x represents independent variable c represents estimated intercept DateXHumidity (Y)X*YX^2 09/09/2022178%0.781 10/09/2022272%1.444 11/09/2022362%1.869 12/09/2022456%2.2416 13/09/2022577%3.8525 14/09/2022660%3.636 15/09/2022758%4.0649 16/09/2022845%3.664 17/09/2022942%3.7881 18/09/20221078%7.8100 Sum55628.00%33.01385 Formula for computing value ofminy = mx + c m= m= {(10*33.01)- (55*6.28)}/ {(10*385)- (55^2)} m=-0.01855 Formula for computing value ofc c= c= {6.28- (-0.01855*55)}/10 c= 0.73
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Forecasting humidity for day 11thand 12th Putting the value of m and c calculated above in formula given below humidity of 11thand 12th day can be forecasted. 11thday humidity y= mx+ c y= (-0.01855*11) + 0.73 y= 0.526 or 52.60% Therefore, it can be said that humidity of 11thday in London may be52.60%. 12thday humidity y= mx+ c y= (-0.01855*12) + 0.73 y= 0.507 or 50.70% Hence, it can be forecasted that humidity of 12thmay be50.70%. CONCLUSION The report had been made with an aim of understanding different mathematical formulas used for analysing data and generating useful information which can be used for making effective decisions. Report consist of calculation different statistical formulas and generate information regarding humidity in London. The data collected for various day humidity were presented in tabular and charts form. At last, report also highlighted forecast of 11thand 12thday humidity using linear regression.
REFERENCES Bagus, M. R. D. and Hanaoka, S., 2022. The central tendency of the seaport-fulcrum supply chain risk in Indonesia using a rough set.The Asian Journal of Shipping and Logistics. George, D. and Mallery, P., 2018. Descriptive statistics. InIBM SPSS Statistics 25 Step by Step(pp. 126-134). Routledge. Hope, T. M., 2020. Linear regression. InMachine Learning(pp. 67-81). Academic Press. Kaliyadan, F. and Kulkarni, V., 2019. Types of variables, descriptive statistics, and sample size.Indian dermatology online journal.10(1). p.82. Saidi, S. S. and Siew, N. M., 2019. Assessing Students' Understanding of the Measures of CentralTendencyandAttitudetowardsStatisticsinRuralSecondary Schools.International Electronic Journal of Mathematics Education,14(1), pp.73-86. Weir, C. J and et.al., 2018. Dealing with missing standard deviation and mean values in meta- analysisofcontinuousoutcomes:asystematicreview.BMCmedicalresearch methodology.18(1). pp.1-14.