Numeracy and Data Analysis: Central Tendency and Linear Forecasting
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Added on 2023/06/08
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Learn about central tendency and linear forecasting in numeracy and data analysis with the help of temperature data from Biggin, UK. Compute mean, median, mode, range, and standard deviation. Calculate the value of 'x', 'y,' and 'm' for forecasting temperature values of days 11th and 14th.
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Table of Contents INTRODUCTION...........................................................................................................................3 MAIN BODY...................................................................................................................................3 1. Arrange the temperature data in an appropriate table format.............................................3 2. Represent the data in two suitable chart formats................................................................4 3. Explain and compute the central tendency of temperature data.........................................4 4. Define the Linear forecasting model using calculation......................................................6 CONCLUSION................................................................................................................................8 REFERENCES................................................................................................................................9
INTRODUCTION A central tendency is a statistical tool that helps to explain a data set by examining the central position from given information(Zhao and et.al., 2022). The following report is considered a central tendency tool for 10 days of data evaluation systematically and easily. It includes five major types of data numeracy that are mean, mode, range, median, and standard deviation in the context of Biggin, UK. Moreover, it explains linear forecasting and calculates the worth of 'y', 'x', and 'c'. Further, calculate the degree of the temperature 11thand 14thdays by using linear forecasting values. MAIN BODY 1. Arrange the temperature data in an appropriate table format DayTemperature 17 212 36 48 511 69 79 87 911 1015 Total95
2. Represent the data in two suitable chart formats Fig.1 Bar chart of biggin 10 days temperature Fig.1 Line chart of biggin 10 days temperature 3. Explain and compute the central tendency of temperature data Mean: This term helps to calculate the average data from a set data point(Yoshino and Oshio., 2022). It is generally used in mathematics and statistics for understanding numbers easily. Steps to compute mean value: Step1: Collect required data in Quantitative form. Step2: Systematically put the data. Step3: Sum up the data points’ value Step4: Divide the number of values from the sum of data sets
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The formula for analysing the mean value of Biggin Temperature: Mean = Sum of given worth / Number of data set = 95 / 10 Mean = 9.5 Median: The midterm or mid-value of set data is known as the median(Lin and Liu., 2020). Steps to calculate the value of the median Step1: Collect data from required sources. Step2: Order the data (ascending or descending). Step3: Count or analyse the data set are Odd or even. Step4: At last, use the formula according to Step3 Computation of Median value of Biggin temperature: 7, 12, 6, 8, 11, 9, 9, 7, 11, 15 Ascending order: 6, 7, 7, 8, 9, 9, 11, 11, 12, 15 Median = (N+1) / 2 = (10+1) / 2 Median = 5.5thterm Hence, the Median is between the 5thand 6thterms of the data set. Median = (9+9) / 2 Median = 9 Mode: This part of the central tendency observes the probability or repentance of the data set (García-Madariaga and et.al., 2019). It considers a higher repeated value as a model. Steps to compute mode value: Step1: Gather appropriate data points and then arrange them in systematic order. Step2: Analyse a higher occurrence value. Step3: Then consider maximal repeated value as mode. 7, 12, 6, 8, 11, 9, 9, 7, 11, 15 Ascending order: 6, 7, 7, 8, 9, 9, 11, 11, 12, 15 Mode = 7, 9, 11 Standard Deviation: It explains a proportion of how apportioned the data is equilibrium to the value of the mean(Zhou and et.al., 2018). Steps to calculate standard deviation:
Step1: Collect necessary data and then find out the mean value. Step2: Less every data point to mean Step3: Sum up all the values of Step2. Step4: Divide Step 3 to 'n' term Step5: Square root of Step 4 value. The formula for calculating Standard deviation Biggin temperature data set SD = √∑ (xi – μ) 2 / N = √(68.5 / 10) SD = 2.617 Range: Variation between the top most value and the lower value is known as range. Steps of Range Computation Step1: Arrange the value in upward and downward directions. Step2: Figure out the highest and lowest values. Step3: Minus lowest value from highest value. Range = Maximal value of data set – Minimal value of data set Calculation of Biggin temperature range Ascending order: 6, 7, 7, 8, 9, 9, 11, 11, 12, 15 Range = 15 – 6 Range = 9 4. Define the Linear forecasting model using calculation Linear forecasting plays a role in collecting future estimates by past experiences and data on the straight-line method. Temperature is also measured with the help of the linear forecasting method. Steps to compute the Linear forecasting model Step1: Collect data points from the necessary sources.
Step2: Observe collected data from the initial stage. Step3: Put linear forecasting set formula. Formula to compute Linear forecasting model y= mx + c Here, 'y' refers to the dependent factor, 'mx' refers to the independent factor and 'c' states for a constant factor Following are some steps to compute the 'm' value Step1: Analyze the value of 'n' Step2: Add both 'x' and 'y' variables separately. Step3: Calculate 'x' and 'y' value product respectively then sum the product value∑xy Step4: Find out the Square of 'x' and 'y' terms. Step5: Total the value of x and y squares individually. Step6: At last, put all the values in the respective 'm' formula. Calculation of ‘m’ value M = ((10*558) - (55*95)) / ((10*385) – (55)2) M = (5580 – 5225) / (3850 – 3025) M = 0.43 A few steps to calculate the value of 'c' are listed below: Step1: Calculate the aggregate of the 'y' factor Step2: Figure out the value of 'm' Step3: Sum of 'x' multiplied by 'm' Step4: Minus aggregate 'y' to aggregate 'x' Step5: Divide the remaining value from 'n'
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C = (95 – (0.43*55)) / 10 C = (95 – 23.65) / 10 C = 7.135 Temperature of 11thDay C= 7.135, m = 0.43 and x = 11 Y = mx + c = 0.43*11 + (7.135) Y = 11.865 Temperature of 14thDay C = 7.135, m = 0.43 and x = 14 Y = mx + c =0.43*14 + (7.135) Y = 13.155 CONCLUSION The above report is concluded that numeracy and data analysis is the vital tool to compute the data set central position. It helps to create a dynamic database of aggregate information by using effective cost. The above report contains ten days’s temperature data in numerical form for calculating mean, median, standard deviation, mode, and range. Further, figure out the value of 'x', 'y,' and 'm' for computing the temperature value of days 11thand 14th.
REFERENCES Books and Journals Von Briel, F., 2018. The future of omnichannel retail: A four-stage Delphi study.Technological Forecasting and Social Change.132. pp.217-229. Zhao, L. and et.al., 2022. Investigation of the spreading tendency of emulsified oil slicks on open systems.Marine Pollution Bulletin.180. p.113739. Yoshino, S. and Oshio, A., 2022. Personality and migration in Japan: Examining the tendency of extrovertedandopenpeopletomigratetoTokyo.JournalofResearchin Personality.96. p.104168. Lin, Y. and Liu, Q., 2020. Perceived subjective social status and smartphone addiction tendency amongChineseadolescents:Asequentialmediationmodel.ChildrenandYouth Services Review,116, p.105222. García-Madariaga, J. and et.al., 2019. Do isolated packaging variables influence consumers' attention and preferences?.Physiology & behavior.200. pp.96-103. Zhou, C. and et.al., 2018. Estimation of eco-efficiency and its influencing factors in Guangdong province based on Super-SBM and panel regression models.Ecological Indicators.86. pp.67-80.