This assessment will cover following questions:
- Prepare the data with the help of column chart, bar chart, scatter plot etc.
- For your data, use the linear forecasting model which is y=mx+c to calculate , value and discuss the answer.
The report will be discussing the statistical analysis of weather data on humidity. This will be representing descriptive analysis methods used by the experts. For measuring the data descriptive analysis using mean, median, mode, standard deviation and range method. The report is based on the data of humidity of Bradford. Predictions have been in the report using regression methods. The report will provide understanding of the above concepts using calculations.
1) Arrangement of Data into Table
2) Presentation of Data
3) Calculation of mean, median, mode and standard deviation
In analysing the data descriptive statistics is an essential tool used by the analysts and experts at the initial level. The tools is used widely as it provides the basic level of information to identify the the direction towards which variable is heading. The methods involved in calculation are mean, median, mode and standard deviations.
Mean refers to the tool which is used by the experts and analysts in their research for analysing the provided data properly. This is among one of the essential tool that is used in the descriptive statistics. It calculates the average performance of variable (Holcomb, 2016). The given mean table represent that the mean value is 86.10% of humidity reflecting the average humidity of in Bradford during ten days is around 86.10%. If the humidity level of the Bradford increases beyond the given level in table will increase the average humidity.
|Number of observations||10|
Median is used for classifying the data sets into two parts and this makes the analysis simple and easy. Big data could be analysed using this method. The median value of given data set of humidity is 89%. The value of the median represents that it will be declining slowly sut again shows a fluctuating increase. So this could be said that median helps in analysing the data related to any variable.
|Mid Point (10/2)||5|
|M= (88% + 90%) / 2||89%|
Mode refers to the values that is repeating maximum number of times in the data field. In the present data set of humidity, figure representing maximum number of times is 90% Therefore, it can be said that the mode is 90% in humidity data. Mode is used by the analysts to identify the trends that is being repeated number of times in the given data set (George and Mallery, 2016).
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Standard deviation refers to tool indicating the variations occurring between the variable values. In the below table standard deviation is calculated to 0.054. The deviation between variables is not high (Young and Wessnitzer, 2016). On this analysis it could be interpreted that variables have not much deviations from mean value.
SQRT ( 7.44/10)-(8.61/10)^2)
SQRT (0.744 – 0.741)
Range refers to tool used for indicating the differences between the minimum and maximum values in a given data set. 97 is the maximum value in given data set and and 78 is the minimum value (McCarthy and et.al., 2019). Range refers to difference between two values that is 97 minus 78 is 19. The values of variable is not moving in higher data range Range statistics. 2019.
4) Forecast and Calculation of M & C
Calculation of M
|Calculation of M|
|m = NΣxy – Σx Σy / NΣ x^2 – (Σx)^2|
|Y = mX + c|
|M = 10(47.52)-(55*8.61) / ((10*385)-55^2))|
|M = (475.52- 473.55) / (3850 – 3025)|
|M = 1.65 / 825|
|M = 0.002 or 0.20%|
Calculation of C
|c = Σy – m Σx / N|
Forecasting the humidity on 15th and 20th day
Forecast for 15th day
|Y = Mx + c|
Forecast for the 20th day
|Y = Mx + c|
Humidity during the 15th day will be 91.9% where on the 20th day it would be 92.9%. Interpreting the above data it could be analysed that the humidity will be increasing in Bradford in the coming time. For making the predictions for future period equation of mX + c is used (Norman, Mello and Choi, 2016). Value of m on calculation is coming to 0.002 and the value of c is 0.889. Value of X is changing in both the events. The value obtained is 91.9 when the value taken is 15 and 92.9 is obtained on calculating the value taking 20.
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The above study shows that the descriptive statistics is of great importance for the analysts. Users of this tool can develop an basic understanding about the variables in appropriate manner. Descriptive analysis is also used by business firms for making systematic analysis of the data using the tools. Thus carrying out the about research it could be concluded that the humidity level of Bradford in coming years will be increasing.
Books and Journals
Holcomb, Z.C., 2016. Fundamentals of descriptive statistics. Routledge.
George, D. and Mallery, P., 2016. Descriptive statistics. In IBM SPSS Statistics 23 Step by Step (pp. 126-134). Routledge.
McCarthy, R.V. and et.al., 2019. What Do Descriptive Statistics Tell Us. In Applying Predictive Analytics (pp. 57-87). Springer, Cham.
Norman, C., Mello, M. and Choi, B., 2016. Identifying frequent users of an urban emergency medical service using descriptive statistics and regression analyses. Western Journal of Emergency Medicine, 17(1), p.39.
Young, J. and Wessnitzer, J., 2016. Descriptive statistics, graphs, and visualisation. In Modern statistical methods for HCI (pp. 37-56). Springer, Cham.