How to Calculate Point Estimates in R? Last Updated : 12 Aug, 2024 Comments Improve Suggest changes Like Article Like Report Point estimation is a technique used to find the estimate or approximate value of population parameters from a given data sample of the population. The point estimate is calculated for the following two measuring parameters:Measuring parameterPopulation ParameterPoint EstimateProportionπp Meanμx̄ This article focuses upon how we can calculate point estimates in R Programming Language.The point estimate of the population proportionPoint estimation of population proportion can be calculated by using the below mathematical formula,Syntax: p′ = x / n Here,x : Signifies the number of successesn : Signifies the sample size.p′ is the point estimate of population proportionExample:Let's say we want to estimate the proportion of students in a class who are present on a particular day. The sample data consist of 20 data elements. R # define data data <- c('Present', 'Absent', 'Absent', 'Absent', 'Absent', 'Absent', 'Present', 'Present', 'Absent', 'Present', 'Present', 'Present', 'Present', 'Present', 'Present', 'Present', 'Absent', 'Present', 'Present', 'Present') # find total sample size n <- length(data) # find number who are present k <- sum(data == 'Present') # find sample proportion p <- k/n # print print(paste("Sample proportion of students who are present", p)) Output:Example:Note that we can calculate the 95% confidence interval for the population proportion by using the following source code, R # define data data <- c('Present', 'Absent', 'Absent', 'Absent', 'Absent', 'Absent', 'Present', 'Present', 'Absent', 'Present', 'Present', 'Present', 'Present', 'Present', 'Present', 'Present', 'Absent', 'Present', 'Present', 'Present') # find total sample size total <- length(data) # find number who responded 'Yes' favourable <- sum(data == 'Present') # find sample proportion ans <- favourable/total # calculate margin of error margin <- qnorm(0.975)*sqrt(ans*(1-ans)/total) # calculate lower and upper bounds of # confidence interval low <- ans - margin print(low) high <- ans + margin print(high) Output:Hence, The 95% confidence interval for the population proportion is [0.440, 0.859].The point estimate of a population meanPoint estimation of population mean can be calculated by using mean() function in R. The syntax is given below,Syntax: mean(x, trim = 0, na.rm = FALSE, ...)Here,x: It is the input vectortrim: It is used to drop some observations from both end of the sorted vectorna.rm: It is used to remove the missing values from the input vectorExample:Let's say we want to estimate the population mean of heights of the students in a class. The sample data consist of 20 data elements. R #define data data <- c(170, 180, 165, 170, 165, 175, 160, 162, 156, 159, 160, 167, 168, 174, 180, 167, 169, 180, 190, 195) #calculate sample mean ans <- mean(data, na.rm = TRUE) #print the mean height print(paste("The sample mean is", ans)) Output:Hence, The sample means the height is 170.6 cm.Example:Note that we can calculate the 95% confidence interval for the population mean by using the following source code, R # define data data <- c(170, 180, 165, 170, 165, 175, 160, 162, 156, 159, 160, 167, 168, 174, 180, 167, 169, 180, 190, 195) # Total number of students total <- length(data) # Point estimate of mean favourable <- mean(data, na.rm = TRUE) s <- sd(data) # calculate margin of error margin <- qt(0.975,df=total-1)*s/sqrt(total) # calculate lower and upper bounds of # confidence interval low <- favourable - margin print(low) high <- favourable + margin print(high) Output:Hence, The 95% confidence interval for the population mean is [165.782, 175.417]. 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