INFS 343 Business Analytics
Lab Assignment 02
Kennedy Kobayashi
O2/27/2024
About
Qualitative Descriptive Analytics aims to gather an in-depth understanding of the underlying reasons and motivations for an event or observation. It is typically represented with visuals or charts.
Quantitative Descriptive Analytics focuses on investigating a phenomenon via statistical, mathematical, and computational techniques. It aims to quantify an event with metrics and numbers.
In this project, we will explore quantitative analytics using the babies data set provided. This data is collected to understand different factors effecting baby weights at birth. Below are the explanations of the variables:
Bwt: Baby weights in ounces Gestation: Length of pregnancy in days Height: Mother’s height in inches Smoke: =1 if mother is smoker, = 0 Nonsmoker Age: Mother’s age in years Weight: Mother’s pregnancy weight Parity: = 0 if the baby is first born, =1 otherwise babies: number of babies before this birth
Read this worksheet carefully and follow the instructions to complete the tasks and answer any questions. Submit your work as an HTML or PDF or Word document to Sakai. If you are submitting as a team project only one submission from one of the team members will be sufficient but in this case you need to input the names of the team members in the above Title section.
Task 1: Testing for missing data (1 point)
babies_old data set will be used to check if it contains any missing data. First read the babies_old file and make sure you correctly read the file with the above mentioned variables.
#Read babies_old file and check the variables
setwd("C:/Users/kkobayashi/Downloads/LAB 02") # Change working directory
mydata_old <- read.csv("babies_old.csv")
dim(mydata_old)## [1] 1174 8Find NA’s in babies_old file
#Check your file for missing data
missing_data <- is.na(mydata_old)
missing_count <- which(is.na(mydata_old$Count))
missing_bwt <- which(is.na(mydata_old$bwt))
missing_gest <- which(is.na(mydata_old$gestation))
missing_parity <- which(is.na(mydata_old$parity))
missing_height <- which(is.na(mydata_old$height))
missing_weight <- which(is.na(mydata_old$weight))
missing_smoke <- which(is.na(mydata_old$smoke))
missing_age <- which(is.na(mydata_old$age))Implement omission strategy to remove observations with missing values. (0.5 point)
#implement omission strategy and check if all the observations with NA's were omitted.
CompletedData <-na.omit(mydata_old)
dim(CompletedData)## [1] 1163 8Implement mean imputation strategy (0.5 point)
#Implement imputation strategy
mean_weight <- mean(mydata_old$weight, na.rm = TRUE)
mydata_old$weight[is.na(mydata_old$weight)] <- mean_weight
mydata_old## Count bwt gestation parity height weight smoke age
## 1 1 120 284 0 62 100.0000 0 27
## 2 2 113 282 0 64 128.4996 0 33
## 3 3 128 279 0 64 115.0000 1 28
## 4 4 108 282 0 67 125.0000 1 23
## 5 5 136 286 0 62 93.0000 0 25
## 6 6 138 244 0 62 178.0000 0 33
## 7 7 132 245 0 65 140.0000 0 23
## 8 8 120 289 0 62 125.0000 0 25
## 9 9 143 299 0 66 136.0000 1 30
## 10 10 140 351 0 68 120.0000 0 27
## 11 11 144 282 0 64 124.0000 1 32
## 12 12 141 279 0 63 128.0000 1 23
## 13 13 110 281 0 61 128.4996 1 36
## 14 14 114 273 0 63 154.0000 0 30
## 15 15 115 285 0 63 130.0000 0 38
## 16 16 92 255 0 65 125.0000 1 25
## 17 17 115 261 0 60 125.0000 1 33
## 18 18 144 261 0 68 170.0000 0 33
## 19 19 119 288 0 66 142.0000 1 43
## 20 20 105 270 0 56 93.0000 0 22
## 21 21 115 274 0 67 175.0000 1 27
## 22 22 137 287 0 66 145.0000 0 25
## 23 23 122 276 0 68 182.0000 0 30
## 24 24 131 294 0 65 122.0000 0 23
## 25 25 103 261 0 65 112.0000 1 27
## 26 26 146 280 0 58 106.0000 0 26
## 27 27 114 266 0 65 175.0000 1 20
## 28 28 125 292 0 65 125.0000 0 32
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## 30 30 122 270 0 61 105.0000 0 26
## 31 31 93 278 0 61 146.0000 0 34
## 32 32 130 268 0 66 123.0000 0 30
## 33 33 119 275 0 60 105.0000 0 23
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## 35 35 134 283 0 67 130.0000 0 22
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## 1056 1056 118 273 0 63 120.0000 0 21
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## 1065 1065 158 289 0 66 140.0000 0 30
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## 1071 1071 134 287 0 63 118.0000 0 40
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## 1073 1073 102 285 0 63 128.4996 1 29
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## 1076 1076 98 279 1 65 115.0000 1 18
## 1077 1077 130 246 0 62 118.0000 0 19
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## 1079 1079 122 285 0 62 102.0000 1 31
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## 1085 1085 89 238 1 64 136.0000 0 26
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## 1087 1087 143 281 0 67 132.0000 0 29
## 1088 1088 106 279 0 63 125.0000 1 29
## 1089 1089 99 246 0 62 106.0000 0 35
## 1090 1090 156 300 0 65 120.0000 1 27
## 1091 1091 72 266 1 66 200.0000 1 25
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## 1094 1094 106 264 0 64 114.0000 0 41
## 1095 1095 91 225 0 68 117.0000 1 18
## 1096 1096 117 269 1 61 99.0000 0 28
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## 1108 1108 112 268 1 59 103.0000 0 25
## 1109 1109 139 311 0 66 135.0000 0 37
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## 1111 1111 130 294 0 63 110.0000 1 32
## 1112 1112 71 254 0 61 145.0000 1 19
## 1113 1113 82 270 0 65 150.0000 1 21
## 1114 1114 119 280 1 64 128.0000 0 21
## 1115 1115 123 353 0 63 115.0000 0 26
## 1116 1116 115 278 0 59 95.0000 0 27
## 1117 1117 124 289 1 67 145.0000 1 21
## 1118 1118 138 292 0 65 130.0000 1 25
## 1119 1119 88 276 0 63 103.0000 1 25
## 1120 1120 128 241 1 64 126.0000 0 17
## 1121 1121 82 274 0 64 101.0000 1 31
## 1122 1122 100 274 0 63 113.0000 0 24
## 1123 1123 114 271 0 61 130.0000 0 32
## 1124 1124 97 269 0 65 137.0000 1 20
## 1125 1125 126 298 0 61 112.0000 0 24
## 1126 1126 122 275 1 65 127.0000 0 20
## 1127 1127 152 295 0 62 140.0000 0 39
## 1128 1128 116 274 0 62 110.0000 1 21
## 1129 1129 132 302 0 63 145.0000 1 36
## 1130 1130 84 260 1 66 140.0000 0 37
## 1131 1131 119 277 1 61 89.0000 1 18
## 1132 1132 106 312 0 62 135.0000 1 24
## 1133 1133 139 291 0 65 160.0000 0 24
## 1134 1134 103 273 0 65 158.0000 1 36
## 1135 1135 112 299 0 67 145.0000 1 24
## 1136 1136 96 276 0 64 127.0000 1 33
## 1137 1137 102 281 1 67 135.0000 1 19
## 1138 1138 120 300 0 63 150.0000 1 34
## 1139 1139 102 338 0 64 170.0000 0 19
## 1140 1140 97 255 1 63 107.0000 1 22
## 1141 1141 113 285 0 70 145.0000 0 22
## 1142 1142 130 297 0 58 130.0000 0 32
## 1143 1143 97 260 1 63 115.0000 1 25
## 1144 1144 116 273 0 61 120.0000 0 31
## 1145 1145 114 266 0 64 113.0000 0 29
## 1146 1146 127 242 0 61 135.0000 1 17
## 1147 1147 87 247 1 66 125.0000 1 18
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## 1151 1151 75 265 0 65 103.0000 1 21
## 1152 1152 138 286 1 68 120.0000 0 28
## 1153 1153 99 271 0 69 151.0000 0 39
## 1154 1154 118 293 0 63 103.0000 0 21
## 1155 1155 97 266 0 62 128.4996 0 24
## 1156 1156 146 319 0 66 145.0000 0 28
## 1157 1157 81 285 0 63 150.0000 1 19
## 1158 1158 110 321 0 66 180.0000 0 28
## 1159 1159 135 284 1 60 95.0000 0 19
## 1160 1160 114 290 1 65 120.0000 1 21
## 1161 1161 124 288 1 64 116.0000 1 21
## 1162 1162 115 262 1 64 136.0000 1 23
## 1163 1163 143 281 0 65 135.0000 1 28
## 1164 1164 113 287 1 70 145.0000 1 29
## 1165 1165 109 244 1 63 102.0000 1 21
## 1166 1166 103 278 0 60 87.0000 1 30
## 1167 1167 118 276 0 64 116.0000 0 34
## 1168 1168 127 290 0 65 121.0000 0 27
## 1169 1169 132 270 0 65 126.0000 0 27
## 1170 1170 113 275 1 60 100.0000 0 27
## 1171 1171 128 265 0 67 120.0000 0 24
## 1172 1172 130 291 0 65 150.0000 1 30
## 1173 1173 125 281 1 65 110.0000 0 21
## 1174 1174 117 297 0 65 129.0000 0 38Task 2: Testing for Outliers(1 point)
Read the babies.csv file and calculate the statistics required to find out possible outliers for baby weights.
First, read the babies.csv file and calculate the mean, standard deviation, maximum, and minimum for the bwt column using R.
#Read babies file
setwd("C:/Users/kkobayashi/Downloads/LAB 02")
babies=read.csv("babies.csv")
dim(babies)## [1] 1174 8#Calculate and display the average, standard deviation, maximum and minimum babies weight.
average_weight<-mean(babies$weight, na.rm = TRUE)
stnddeviation_weight <-sd(babies$weight, na.rm = TRUE)
max_weight <- max(babies$weight, na.rm = TRUE)
min_weight <- min(babies$weight, na.rm = TRUE)
cat("average baby weight:", average_weight, "\n")## average baby weight: 128.4787cat("stn baby weight:", stnddeviation_weight, "\n")## stn baby weight: 20.73428cat("max baby weight:", max_weight, "\n")## max baby weight: 250cat("min baby weight:", min_weight, "\n")## min baby weight: 87Use the standard approach to find out possible outliers (0.5 points)
Use the formula from class to detect any outliers. An outlier is value that “lies outside” most of the other values in a set of data.
#calculate the upper and lower thresholds
Q1 <- quantile(babies$weight, 0.25, na.rm = TRUE)
Q3 <- quantile(babies$weight, 0.75, na.rm = TRUE)
interquartile_range <- Q3 -Q1
upper_threshold <- Q3 + 1.5 * interquartile_range
lower_threshold <- Q1 - 1.5 * interquartile_rangeDo you think there are possible outliers? Why?
If you decided that you have possible outliers can you identify the observations with possible outliers?
#Observations with possible outliers at the upper end
possible_outliers_up<-babies$weight[babies$weight>upper_threshold]#observations with possible outliers at the lower end
possible_outliers_low<-babies$weight[babies$weight>lower_threshold]What is the number of outliers at the lower end and upper end of the data?
Use the IQR approach to identify outliers (0.5 point)
Another similar method to find the upper and lower thresholds discussed in class involves finding the interquartile range. Find the interquantile range using the following chunk.
#Find the inter quantile range
Q1 <- quantile(babies$weight, 0.25, na.rm = TRUE)
Q3 <- quantile(babies$weight, 0.75, na.rm = TRUE)
interquartile_range <- Q3 - Q1The threshold is the boundaries that determine if a value is an outlier. If the value falls above the upper threshold or below the lower threshold, it is a possible outlier.
#calculate the upper threshold
upper_threshold <- Q3+1.5*interquartile_range#calculate the lower threshold
lower_threshold <- Q1-1.5*interquartile_rangeDo you think there are possible outliers and why?
If you think there are possible outliers can you identify them? If so how many?
possible_outliers_upper <- babies$weight[babies$weight > upper_threshold]
possible_outliers_low <- babies$weight[babies$weight > lower_threshold]
num_possible_outliers <- length(possible_outliers_upper)
length(possible_outliers_low)## [1] 1174It can also be useful to visualize the data using a box plot.
boxplot(babies$weight,main="Baby Weight Bowplot")
Can you identify the outliers from the boxplot? If so how many?
Task 3: Calculating Z-Value (0.25 points)
Given a birth weight of 182, calculate the corresponding z-value or z-score.
# Calculate the z-value and display it
birth_weight <- 182
mean_weight <- mean(babies$weight, na.rm = TRUE)
sd_weight <- sd(babies$weight, na.rm = TRUE)
z_value <- (birth_weight - mean_weight) / sd_weight
cat("Z-value for birth weight:", z_value, "\n")## Z-value for birth weight: 2.581295Based on the z-value, how would you evaluate the weight of this baby. Explain your logic.
Task 4:Relationships with the variables (0.75 points)
How can you describe the relationship between baby weights and other variables?
#correlation between baby weight and other variables
correlation <- cor(babies[, -1], babies$weight, use = "complete.obs")Your comments on these relationships: A correlation value close to 1 means a strong positive connection, while a value near -1 suggest a strong negative link. If thew value is close to 0, then it means there is no clear relationship between baby weight and the other factors in your dataset.