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ICT110 Introduction To Data Science For Statistical Software

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ICT110 Introduction To Data Science For Statistical Software

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Course Code: ICT110
University: University Of The Sunshine Coast

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Country: Australia

Question:
Provide an introduction to the problem. Include background material as appropiate:who cares about this problem,what impact it has,where does the data come from.
Answer:
Introduction
Authorization and Purpose
In this report, the main purpose is the exploration of the data on health and population statistics. The data has been retrieved from World Bank which is a secondary source of data. The dataset contains information about the world health and population over the years 2001 – 2015. Information of countries across the East Asia and the Pacific are contained in the dataset. The statistical software “R-Studio” will be used to perform the analysis.
Limitations
The dataset that will be used for this is the secondary data retrieved from World Bank. The dataset contains a lot of missing values for the variables that are assumed to be important.
Scope
The dataset that has been retrieved for this study has the chances of performing one variable and two variable analyses along with a graphical representation for each type of analysis. For further analysis, k-means cluster analysis and regression analysis can also be performed.
Methodology
With the help of the descriptive statistics measures, exploratory data analysis will be performed and the advanced analysis will be performed with the help of k-means clustering and regression analysis.
Data Setup
The dataset retrieved from World Bank on the Health and Population data is in .csv format. The dataset contains information on numerous attributes but the values of all the attributes on all the years are not present in the dataset. There are a lot of unrecorded data which has to be cleaned before the analysis. All these cleaning and extracting of the data is performed using the statistical software “R-Studio” with the help of various packages. The packages that are required for the analysis are listed as follows:

table: used to provide data frame and faster data manipulations.
reshape2: Used to reshape the data
psych: Used for multivariate analysis
ggploy2: Used for plotting data
lattice: Graphics package
dplyr: Used for data cleaning and manipulation

The R-codes that will be used for data cleaning are given in the following table:

###===============Importing Data File in R=================###
health <- read.csv(file.choose(), header = TRUE, sep = "," , na.strings = "..", blank.lines.skip = TRUE,)   ###===================Libraries Used===================### library(data.table) library(reshape2) library(psych) library(ggplot2) library(lattice) library(dplyr)     ###======================Data Cleaning======================### health1 <- data.table(health) health1 <- health1[Series.Code %in% c("SP.ADO.TFRT", "SP.DYN.CBRT.IN","SP.DYN.LE00.IN","SL.UEM.TOTL.ZS","SL.UEM.TOTL.FE.ZS",                                   "NY.GNP.PCAP.CD","SH.XPD.TOTL.ZS","SL.UEM.TOTL.FE.ZS")] health1 <- health1[,"X2015..YR2015." := NULL] health1 <- na.omit(health1) str(health1) View(health1) ###=============Converting Years in Rows and Attributes in Columns=============### health1 <- melt(health1, Series.Code = "Country.Code") View(health1) Exploratory Data Analysis One Variable Analysis Summary of Adolescent Fertility Rate The R codes in table 3.1 and the following boxplot in figure 3.1 shows the summary measures of the variable adolescent fertility rate. The boxplot shows the distribution of the fertility rates in women between 15 to 18 years of age. The black line in the plot shows the median of the distribution which is found to be less than the mean. Thus, it can be said that the fertility rate is less than the average rate in most females. There are no outliers to the data. Table 3.1 ###============Adolscent Fertility Rate================### exp1 <- health1[Series.Code %in% "SP.ADO.TFRT"] describe(exp1$value) fill <- "green" exp_plot1 <- ggplot(exp1, aes(x = factor(0), y = value)) + geom_boxplot(fill = fill) exp_plot1 <- exp_plot1 + xlab("Adolescent fertility rate (births per 1,000 women ages 15-19)") + scale_x_discrete(breaks = NULL) exp_plot1 <- exp_plot1 + ggtitle("Distribution of Adolescent fertility rate (births per 1,000 women ages 15-19)") +  theme_bw() plot(exp_plot1)   Summary of Crude Birth Rate    The R codes in table 3.3 and the following boxplot in figure 3.2 shows the summary measures of the variable Crude Birth Rate.  The histogram shows the distribution of the Crude Birth Rate. The bars show that the data is negatively skewed which indicates that in most of the cases, the crude birth rate is high. ###============Crude Birth Rate================### exp2 <- health1[Series.Code %in% "SP.DYN.CBRT.IN"] describe(exp2$value) View(exp2) n <- length(exp2$value) r <- diff(range(exp2$value)) barfill <- "blue" barlines <- "black" exp_plot2 <- ggplot(exp2, aes(x = value)) + geom_histogram(binwidth=r/(log2(n)+1), colour = barlines, fill = barfill) exp_plot2 <- exp_plot2 + scale_x_continuous("Birth rate, crude (per 1,000 people)", breaks = seq(0,24,3), limits = c(0,21)) + scale_y_continuous("Count")+theme_bw() exp_plot2 <- exp_plot2 + ggtitle("Distribution of Birth rate, crude per 1,000 people")+ theme(plot.title = element_text(hjust = 0.5)) plot(exp_plot2)    Summary of Total Life Expectancy at Birth    The R codes in table 3.5 and the following boxplot in figure 3.3 shows the summary measures of the variable Total Life Expectancy at Birth.     The boxplot shows the distribution of the Total Life Expectancy at Birth. The black line in the plot shows the median of the distribution which is found to be almost equal to the mean from the figure. Thus, it can be said that the Total Life Expectancy at Birth is symmetrically distributed. There are no outliers to the data.Table 3.5###============Total Life Expectancy at Birth================###exp3 <- health1[Series.Code %in% "SP.DYN.LE00.IN"]describe(exp3$value)fill <- "green"exp_plot3 <- ggplot(exp3, aes(x = factor(0), y = value)) + geom_boxplot(fill = fill) exp_plot3 <- exp_plot3 + xlab("Life expectancy at birth, total (years)") + scale_x_discrete(breaks = NULL)exp_plot3 <- exp_plot3 + ggtitle("Life expectancy at birth, total (years)") +  theme_bw()plot(exp_plot3)  Two Variable Analysis Country Wise Analysis of total Unemployment On the basis of different countries the rate of total unemployment is calculated. The summary shows the comparison with the help of boxplots. The boxplot compares the changes in the unemployment rates with respect to the countries. Here boxplot is used as one variable is numerical and the other is categorical. In 37 boxplots representing countries, it can be seen that there are outliers in countries coded as CHIN, LAO, MYS, SLB, THA and VNM. SLB has two outliers. PHL has the highest rate of unemployment. MMR, PRK and SLB has the lowest unemployment rates.  Country Wise Analysis of total health expenditure On the basis of different countries the total health expenditure is calculated. The summary shows the comparison with the help of boxplots. The boxplot compares the changes in the total health expenditure with respect to the countries. Here boxplot is used as one variable is numerical and the other is categorical. In 37 boxplots representing countries, it can be seen that there are outliers in countries coded as KHM, KIR, PLW, SGP, TLS,TUV, VUT and WSM. KHM and KIR has two outliers. NRU has the highest health expenditure. MMR has the lowest health expenditure. ###============Total Health Expenditure (Country Wise)================### exp6 <- health1[Series.Code %in% "SH.XPD.TOTL.ZS"] fill <- "pink" exp_plot5 <- ggplot(exp6, aes(x = exp6$Country.Code, y = exp6$value)) + geom_boxplot(fill = fill) exp_plot5 <- exp_plot5 + scale_x_discrete(name = "Country") + scale_y_continuous(name = "Total Health Expenditure")+ theme_bw() exp_plot5 <- exp_plot5 +theme(axis.text.x = element_text(angle = 90, hjust = 1)) + ggtitle("Total Health Expenditure") plot(exp_plot5) Advanced Analysis Clustering Brief Explanation of k-means and Clustering In this method segmentation of the data is done on the basis of the group means. The values of the data which are close to the group means are segmented into those groups (Chatfield, 2018). Clustering Analysis In this study, k-means clustering analysis is performed between Crude birth Rate and Crude Death rate. The grouping is done according to the countries. As there was no data for the year 2015, the whole column was eliminated from the data. In the figure, the red clusters are the clusters for the CBR and the green are the clusters for CDR. The green countries show higher birth and death rates while the red countries show higher death rate and lower birth rate. The codes are attached in the following table (Husson, Lê & Pagès, 2017): ###============K-Means Clustering (Country Wise)================### cluster <- filter(health, Series.Code %in% c("SP.DYN.CDRT.IN","SP.DYN.CBRT.IN","SH.IMM.IBCG")) cluster <- subset(cluster, select = -(X2015..YR2015.)) cluster <- melt(cluster, Series.Code = c("Series.Code","Country.Name","Country.Code")) cluster <- dcast(cluster, formula = Country.Code  ~ Series.Code, mean) cluster <- na.omit(cluster) cluster group <- kmeans(cluster[,c("SP.DYN.CDRT.IN","SP.DYN.CBRT.IN")],centers = 2, nstart = 10) group order = order(group$cluster) data.frame(cluster$Country.Code[order], group$cluster[order]) cluster_plot <- plot(cluster$SP.DYN.CDRT.IN, cluster$SP.DYN.CBRT.IN, type="n", xlim=c(0,10), xlab="Crude Death Rate", ylab="Crude Birth Rate")+ text(x=cluster$SP.DYN.CDRT.IN, y=cluster$SP.DYN.CBRT.IN, labels=cluster$Country.Code,col=group$cluster+1) Linear Regression Brief Definition of Linear Regression The relationship between two numerical variables are established with the help of regression analysis (Fox. 2015). The general equation of linear regression is given by: Y = a + bX Here, x and y are respectively the independent and the dependent variables with a being the value of the dependent variable in the absence of the independent variable and b representing the slope of the regression line (Draper & Smith, 2014). Relation between tertiary school enrolment of females and female unemployment rate From the analysis, it can be seen that the regression line shows a negative relationship between the independent and the dependent variables. With the increase in the female school enrolment, the female unemployment rate decreases. The codes are attaches in the following table (Berk, 2016): Relation between immunization rate and CDR From the analysis, it can be seen that the regression line shows a very weak relationship between the independent and the dependent variables. Thus, it can be said that there is no effect of immunization on Crude Death Rate. The codes are attaches in the following table: ###============Regression-Immunization Rate and CDR================### reg_plot2 <- lm(formula = SP.DYN.CDRT.IN ~ SH.IMM.IBCG, data = reg) summary(reg_plot2) reg_plot2 <- ggplot(reg_plot2, aes(x=SH.IMM.IBCG, y=SP.DYN.CDRT.IN)) + geom_point(shape=2) + scale_x_continuous(name = "Immunization, BCG (% of one-year-old children)") + scale_y_continuous(name = "Crude Death rate per 1,000 people")+ geom_smooth(method=lm) +theme_bw()+ ggtitle("Relation of Crude Death Rate to Immunization BCG rate of one-year-old children") plot(reg_plot2)   Conclusion It can thus be concluded from all the analysis conducted so far that the variables have been analyzed by considering single variable, by considering two variables at a time. The k-means clustering analysis shows the relationship between the birth and the death rates across countries and have been grouped accordingly. Negative relationship has been observed within female education and female unemployment and no relationship has been observed on immunization and death rate. Reflection The problem that has been faced the most is the selection of the variables as most of the variables have innumerable missing values. However, the results have been obtained with some selected variables which could have been better if there were lesser missing values. Reference List Chatfield, C. (2018). Introduction to multivariate analysis. Routledge. Husson, F., Lê, S., & Pagès, J. (2017). Exploratory multivariate analysis by example using R. Chapman and Hall/CRC. Fox, J. (2015). Applied regression analysis and generalized linear models. Sage Publications. Draper, N. R., & Smith, H. (2014). Applied regression analysis(Vol. 326). John Wiley & Sons. Berk, R. A. (2016). Statistical learning from a regression perspective. New York: Springer.  Free Membership to World's Largest Sample Bank To View this & another 50000+ free samples. Please put your valid email id. E-mail Yes, alert me for offers and important updates Submit  Download Sample Now Earn back the money you have spent on the downloaded sample by uploading a unique assignment/study material/research material you have. After we assess the authenticity of the uploaded content, you will get 100% money back in your wallet within 7 days. UploadUnique Document DocumentUnder Evaluation Get Moneyinto Your Wallet Total 9 pages PAY 5 USD TO DOWNLOAD *The content must not be available online or in our existing Database to qualify as unique. Cite This Work To export a reference to this article please select a referencing stye below: APA MLA Harvard OSCOLA Vancouver My Assignment Help. (2020). Introduction To Data Science For Statistical Software. Retrieved from https://myassignmenthelp.com/free-samples/ict110-introduction-to-data-science/secondary-data.html. "Introduction To Data Science For Statistical Software." My Assignment Help, 2020, https://myassignmenthelp.com/free-samples/ict110-introduction-to-data-science/secondary-data.html. My Assignment Help (2020) Introduction To Data Science For Statistical Software [Online]. Available from: https://myassignmenthelp.com/free-samples/ict110-introduction-to-data-science/secondary-data.html[Accessed 18 December 2021]. My Assignment Help. 'Introduction To Data Science For Statistical Software' (My Assignment Help, 2020) accessed 18 December 2021.

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